From d2e79bb4df239f3e6833b87c4321e3452b3590ca Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Fri, 9 Dec 2022 15:24:23 -0800
Subject: [PATCH 01/14] initial commit after removing template files

---
 gsf/fitting.py        | 40 ++++++++++----------
 gsf/function.py       | 19 ++++++----
 gsf/function_class.py | 34 ++++++++---------
 gsf/function_igm.py   |  2 +-
 gsf/gsf.py            | 46 ++++++++++++++--------
 gsf/maketmp_filt.py   | 88 +++++++++++++++++++++++++++++++++++--------
 gsf/plot_sed.py       | 45 ++++++++++++----------
 gsf/writing.py        | 15 ++++----
 8 files changed, 187 insertions(+), 102 deletions(-)

diff --git a/gsf/fitting.py b/gsf/fitting.py
index bc71cca..b688dfe 100644
--- a/gsf/fitting.py
+++ b/gsf/fitting.py
@@ -281,7 +281,6 @@ def update_input(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:
             self.band['%s_fwhm'%(self.filts[ii])] = np.max(fd[:,1][con]) - np.min(fd[:,1][con])       
 
         # Filter response curve directory, for RF colors.
-        #self.filts_rf = ['u','b','v','j','sz']
         self.filts_rf = '93,141,95,220,160'.split(',')
         self.band_rf = {}
         for ii in range(len(self.filts_rf)):
@@ -675,12 +674,12 @@ def read_data(self, Cz0:float, Cz1:float, Cz2:float, zgal:float, add_fir:bool=Fa
         ##############
         # Spectrum
         ##############
-        dat = ascii.read(self.DIR_TMP + 'spec_obs_' + self.ID + '.cat', format='no_header')
-        NR = dat['col1']
-        x = dat['col2']
-        fy00 = dat['col3']
-        ey00 = dat['col4']
-
+        # dat = ascii.read(self.DIR_TMP + 'spec_obs_' + self.ID + '.cat', format='no_header')
+        # NR = dat['col1']
+        # x = dat['col2']
+        # fy00 = dat['col3']
+        # ey00 = dat['col4']
+        NR, x, fy00, ey00 = self.data['spec_obs']['NR'], self.data['spec_obs']['x'], self.data['spec_obs']['fy'], self.data['spec_obs']['ey']
         con0 = (NR<1000)
         xx0 = x[con0]
         fy0 = fy00[con0] * Cz0
@@ -698,12 +697,13 @@ def read_data(self, Cz0:float, Cz1:float, Cz2:float, zgal:float, add_fir:bool=Fa
         # Broadband
         ##############
         try:
-            dat = ascii.read(self.DIR_TMP + 'bb_obs_' + self.ID + '.cat', format='no_header')
-            NRbb = dat['col1']
-            xbb  = dat['col2']
-            fybb = dat['col3']
-            eybb = dat['col4']
-            exbb = dat['col5']
+            # dat = ascii.read(self.DIR_TMP + 'bb_obs_' + self.ID + '.cat', format='no_header')
+            # NRbb = dat['col1']
+            # xbb  = dat['col2']
+            # fybb = dat['col3']
+            # eybb = dat['col4']
+            # exbb = dat['col5']
+            NRbb, xbb, fybb, eybb, exbb = self.data['bb_obs']['NR'], self.data['bb_obs']['x'], self.data['bb_obs']['fy'], self.data['bb_obs']['ey'], self.data['bb_obs']['ex']
         except: # if no BB;
             print('No BB data.')
             NRbb = np.asarray([])
@@ -740,11 +740,12 @@ def read_data(self, Cz0:float, Cz1:float, Cz2:float, zgal:float, add_fir:bool=Fa
 
         # Append data;
         if add_fir:
-            dat_d = ascii.read(self.DIR_TMP + 'spec_dust_obs_' + self.ID + '.cat')
-            nr_d = dat_d['col1']
-            x_d = dat_d['col2']
-            fy_d = dat_d['col3']
-            ey_d = dat_d['col4']
+            # dat_d = ascii.read(self.DIR_TMP + 'spec_dust_obs_' + self.ID + '.cat')
+            # nr_d = dat_d['col1']
+            # x_d = dat_d['col2']
+            # fy_d = dat_d['col3']
+            # ey_d = dat_d['col4']
+            nr_d, x_d, fy_d, ey_d = self.data['spec_dust_obs']['NR'], self.data['spec_dust_obs']['x'], self.data['spec_dust_obs']['fy'], self.data['spec_dust_obs']['ey']
 
             NR = np.append(NR,nr_d)
             fy = np.append(fy,fy_d)
@@ -1504,7 +1505,7 @@ def prepare_class(self, add_fir=None):
         print('#################')
         print('Preparing library')
         print('#################\n')
-       # Load Spectral library;
+        # Load Spectral library;
         self.lib = self.fnc.open_spec_fits(fall=0)
         self.lib_all = self.fnc.open_spec_fits(fall=1, orig=True)
 
@@ -1525,7 +1526,6 @@ def prepare_class(self, add_fir=None):
         self.nwalk_cz = int(self.inputs['NWALKZ'])
         self.ZEVOL = int(self.inputs['ZEVOL'])
         self.fzvis = int(self.inputs['ZVIS'])
-        #self.fneld = int(self.inputs['FNELD'])
         if self.f_nested:
             print('Nested sample is on. Nelder is used for time saving analysis.')
             self.fneld = 1 
diff --git a/gsf/function.py b/gsf/function.py
index 83212cd..fef73ea 100644
--- a/gsf/function.py
+++ b/gsf/function.py
@@ -481,14 +481,18 @@ def get_Fint(lmtmp, ftmp, lmin=1400, lmax=1500):
     if len(lmtmp[con])>0:
         lamS,spec = lmtmp[con], ftmp[con] # Two columns with wavelength and flux density
         # Equivalent to the following;
-        I1  = simps(spec*lamS*1.,lamS)   #Denominator for Fnu
-        I2  = simps(lamS*1.,lamS)        #Numerator
-        fnu = I1 #/I2                     #Average flux density
+        I1 = simps(spec*lamS*1.,lamS)
+        I2 = simps(lamS*1.,lamS)
+        fnu = I1
     return fnu
 
 
 def get_Fuv(lmtmp, ftmp, lmin=1400, lmax=1500):
     '''
+    Purpose
+    -------
+    Get RF UV (or any wavelength) flux density.
+
     Parameters
     ----------
     lmtmp : 
@@ -498,15 +502,14 @@ def get_Fuv(lmtmp, ftmp, lmin=1400, lmax=1500):
     
     Returns
     -------
-    Convolved flux density. Not integrated sum.
+    Flux density estimated over lmin:lmax. Not integrated sum.
     '''
     con = (lmtmp>lmin) & (lmtmp<lmax) & (ftmp>0)
     if len(lmtmp[con])>0:
         lamS,spec = lmtmp[con], ftmp[con] # Two columns with wavelength and flux density
-        # Equivalent to the following;
-        I1  = simps(spec*lamS*1.,lamS)   #Denominator for Fnu
-        I2  = simps(lamS*1.,lamS)                  #Numerator
-        fnu = I1/I2                               #Average flux density
+        I1 = simps(spec*lamS*1.,lamS)
+        I2 = simps(lamS*1.,lamS)
+        fnu = I1/I2
     else:
         fnu = None
     return fnu
diff --git a/gsf/function_class.py b/gsf/function_class.py
index d963642..3e72079 100644
--- a/gsf/function_class.py
+++ b/gsf/function_class.py
@@ -51,12 +51,12 @@ def open_spec_fits(self, fall:int = 0, orig:bool = False):
         Load template in obs range.
         '''
         ID0 = self.MB.ID
-        tau0= self.MB.tau0 #[0.01,0.02,0.03]
+        tau0= self.MB.tau0
 
         from astropy.io import fits
         ZZ = self.ZZ
         AA = self.AA
-        bfnc = self.MB.bfnc #Basic(ZZ)
+        bfnc = self.MB.bfnc
 
         # ASDF;
         if fall == 0:
@@ -101,8 +101,8 @@ def open_spec_dust_fits(self, fall:int = 0):
         AA = self.AA
         bfnc = self.MB.bfnc
 
-        self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
-        self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
+        # self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
+        # self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
 
         if fall == 0:
             app = ''
@@ -185,8 +185,8 @@ def open_spec_fits_dir(self, nage:int, nz:int, kk, Av00:float, zgal:float, A00:f
         AA = self.AA
         bfnc = self.MB.bfnc
 
-        self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
-        self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
+        # self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
+        # self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
 
         app = 'all'
         hdu0 = self.MB.af['spec_full']
@@ -772,6 +772,7 @@ def open_spec_fits(self, fall:int=0, orig:bool=False):
         ZZ = self.ZZ
         AA = self.AA
         bfnc = self.MB.bfnc
+        DIR_TMP = self.DIR_TMP
 
         # ASDF;
         if fall == 0:
@@ -781,7 +782,6 @@ def open_spec_fits(self, fall:int=0, orig:bool=False):
             app = 'all_'
             hdu0 = self.MB.af['spec_full']
 
-        DIR_TMP = self.DIR_TMP
         NZ = len(ZZ)
         NT = self.MB.ntau
         NA = self.MB.nage
@@ -818,8 +818,8 @@ def open_spec_dust_fits(self, fall:int = 0):
         AA = self.AA
         bfnc = self.MB.bfnc #Basic(ZZ)
 
-        self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
-        self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
+        # self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
+        # self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
 
         if fall == 0:
             app = ''
@@ -832,7 +832,7 @@ def open_spec_dust_fits(self, fall:int = 0):
         nr = hdu0['colnum']
         xx = hdu0['wavelength']
         
-        lib  = np.zeros((len(nr), 2+len(self.Temp)), dtype='float')
+        lib = np.zeros((len(nr), 2+len(self.Temp)), dtype='float')
         lib[:,0] = nr[:]
         lib[:,1] = xx[:]
 
@@ -898,17 +898,17 @@ def open_spec_fits_dir(self, nage, nz, kk, Av00, zgal, A00):
         But for weird template.
         '''
         from astropy.io import fits
-        tau0= self.tau0 #[0.01,0.02,0.03]
+        tau0= self.tau0
         ZZ = self.ZZ
         AA = self.AA
-        bfnc = self.MB.bfnc #Basic(ZZ)
+        bfnc = self.MB.bfnc
 
-        self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
-        self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
+        # self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
+        # self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
 
         app = 'all'
         hdu0 = self.MB.af['spec_full']
-        DIR_TMP = self.DIR_TMP #'./templates/'
+        DIR_TMP = self.DIR_TMP
 
         pp = 0
         zz = nz
@@ -954,8 +954,8 @@ def open_spec_fits_dir(self, nage, nz, kk, Av00, zgal, A00):
 
         b = nrd_yyd
         nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-        yyd_sort     = nrd_yyd_sort[:,1]
-        xxd_sort     = nrd_yyd_sort[:,2]
+        yyd_sort = nrd_yyd_sort[:,1]
+        xxd_sort = nrd_yyd_sort[:,2]
 
         return A00 * yyd_sort, xxd_sort
 
diff --git a/gsf/function_igm.py b/gsf/function_igm.py
index dd492ef..5e34b3c 100644
--- a/gsf/function_igm.py
+++ b/gsf/function_igm.py
@@ -148,7 +148,7 @@ def dijkstra_igm_abs(xtmp, ytmp, zin, cosmo=None, xLL=1216., ckms=3e5,
 
 	ytmp_abs = ytmp * np.exp(-tau)
 
-	return ytmp_abs
+	return ytmp_abs, x_HI
 
 
 def madau_igm_abs(xtmp, ytmp, zin, cosmo=None, xLL=1216.):
diff --git a/gsf/gsf.py b/gsf/gsf.py
index cf145c7..507a42d 100644
--- a/gsf/gsf.py
+++ b/gsf/gsf.py
@@ -78,7 +78,7 @@ def run_gsf_template(inputs, fplt=0, tau_lim=0.001, idman=None, nthin=1, delwave
 
     # Read temp from asdf;
     # This has to happend after fplt==1 and before fplt>=2.
-    MB.af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
+    # MB.af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
 
     '''
     #
@@ -167,32 +167,34 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
                 from .maketmp_z0_tau import make_tmp_z0
                 make_tmp_z0(MB, lammax=lammax)            
 
-        #
-        # 1. Start making redshifted templates.
-        #
-        if MB.SFH_FORM == -99:
-            flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
-        else:
-            flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
-
     if not flag_suc:
         sys.exit()
 
     # Read temp from asdf;
     # Template must be registered before fplt>=2.
-    try:
-        aftmp = MB.af
-    except:
-        MB.af = asdf.open(os.path.join(MB.DIR_TMP, 'spec_all_' + MB.ID + '.asdf'))
+    # try:
+    #     aftmp = MB.af
+    # except:
+    #     MB.af = asdf.open(os.path.join(MB.DIR_TMP, 'spec_all_' + MB.ID + '.asdf'))
 
     if fplt <= 2:
+        MB.zprev = MB.zgal 
+        MB.ndim_keep = MB.ndim
+
+        #
+        # 1. Start making redshifted templates, at z=MB.zgal.
+        #
+        if MB.SFH_FORM == -99:
+            flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+        else:
+            flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+
         #
         # 2. Main fitting part.
         #
-        MB.zprev = MB.zgal 
-        MB.ndim_keep = MB.ndim
         flag_suc = MB.main(cornerplot=cornerplot, f_shuffle=f_shuffle, amp_shuffle=amp_shuffle, Zini=Zini, 
             f_prior_sfh=f_prior_sfh, norder_sfh_prior=norder_sfh_prior)
+
         while (flag_suc and flag_suc!=2):
 
             MB.ndim = MB.ndim_keep
@@ -221,6 +223,20 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
         sys.exit()
 
     if fplt <= 3 and flag_suc:
+
+        if True: #fplt >= 3: # If main fitting has not been done;
+            # make redshifted templates and register data;
+            
+            # Use the final redshift;
+            from astropy.io import fits
+            hd_sum = fits.open(os.path.join(MB.DIR_OUT, 'summary_%s.fits'%MB.ID))[0].header
+            MB.zgal = hd_sum['ZMC']
+            
+            if MB.SFH_FORM == -99:
+                flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+            else:
+                flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+
         if MB.SFH_FORM == -99:
             from .plot_sfh import plot_sfh
             from .plot_sed import plot_sed            
diff --git a/gsf/maketmp_filt.py b/gsf/maketmp_filt.py
index cca74fb..538c80d 100644
--- a/gsf/maketmp_filt.py
+++ b/gsf/maketmp_filt.py
@@ -272,7 +272,10 @@ def get_LSF(inputs, DIR_EXTR, ID, lm, c=3e18):
 def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, 
     tau_lim=0.001, tmp_norm=1e10, nthin=1, delwave=0, lammax=300000, f_IGM=True):
     '''
+    Purpose
+    -------
     Make SPECTRA at given z and filter set.
+    Also, after v1.8, through this function library and data are register to the main class object, MB.
     
     Parameters
     ----------
@@ -298,7 +301,6 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
     DIR_TMP = MB.DIR_TMP
     zbest = MB.zgal
     tau0 = MB.tau0
-    Lsun = 3.839 * 1e33 #erg s-1
 
     try:
         af = MB.af0
@@ -606,7 +608,7 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
 
                 # Flam to Fnu
                 spec_mul_nu[ss,:] = flamtonu(wave, spec_mul[ss,:], m0set=MB.m0set)
-                spec_mul_nu[ss,:] *= Lsun/(4.*np.pi*DL**2/(1.+zbest))
+                spec_mul_nu[ss,:] *= MB.Lsun / (4.*np.pi*DL**2/(1.+zbest))
                 # (1.+zbest) takes acount of the change in delta lam by redshifting.
                 # Note that this is valid only when F_nu.
                 # When Flambda, /(1.+zbest) will be *(1.+zbest).
@@ -694,7 +696,7 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
 
                         spec_mul_neb_nu[zz,uu,:] = flamtonu(wave, spec_mul_neb[zz,uu,:], m0set=MB.m0set)
                         
-                        spec_mul_neb_nu[zz,uu,:] *= Lsun/(4.*np.pi*DL**2/(1.+zbest))
+                        spec_mul_neb_nu[zz,uu,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
                         
                         spec_mul_neb_nu[zz,uu,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
                         ltmpbb_neb[zz,uu,:], ftmpbb_neb[zz,uu,:] = filconv(SFILT, wavetmp, spec_mul_neb_nu[zz,uu,:], DIR_FILT, MB=MB, f_regist=False)
@@ -855,9 +857,10 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
     # For observation.
     # Write out for the Multi-component fitting.
     ##########################################
-    fw = open(DIR_TMP + 'spec_obs_' + ID + '.cat', 'w')
+    MB.data = {}
+    file_tmp = 'tmp_library.txt'
+    fw = open(file_tmp,'w')#DIR_TMP + 'spec_obs_' + ID + '.cat', 'w')
     fw.write('# BB data (>%d) in this file are not used in fitting.\n'%(ncolbb))
-
     for ii in range(len(lm)):
         g_offset = 1000 * fgrs[ii]
         if lm[ii]/(1.+zbest) > lamliml and lm[ii]/(1.+zbest) < lamlimu:
@@ -872,9 +875,19 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
             fw.write('%d %.5f 0 1000\n'%(ii+ncolbb, ltmpbb[0,ii]))
         else:
             fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii]))
-
     fw.close()
-    fw = open(DIR_TMP + 'spec_dust_obs_' + ID + '.cat', 'w')
+
+    # register;
+    dat = ascii.read(file_tmp, format='no_header')
+    NR = dat['col1']
+    x = dat['col2']
+    fy00 = dat['col3']
+    ey00 = dat['col4']
+    dict_spec_obs = {'NR':NR, 'x':x, 'fy':fy00, 'ey':ey00}
+    MB.data['spec_obs'] = dict_spec_obs
+
+    # fw = open(DIR_TMP + 'spec_dust_obs_' + ID + '.cat', 'w')
+    fw = open(file_tmp,'w')#DIR_TMP + 'spec_obs_' + ID + '.cat', 'w')
     if MB.f_dust:
         nbblast = len(ltmpbb[0,:])+len(lm)
         for ii in range(len(ebb_d[:])):
@@ -883,10 +896,21 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
             else:
                 fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii]))
     fw.close()
+    if MB.f_dust:
+        dat = ascii.read(file_tmp, format='no_header')
+        nr_d = dat['col1']
+        x_d = dat['col2']
+        fy_d = dat['col3']
+        ey_d = dat['col4']
+        dict_spec_fir_obs = {'NR':nr_d, 'x':x_d, 'fy':fy_d, 'ey':ey_d}
+        MB.data['spec_fir_obs'] = dict_spec_fir_obs
 
     # BB phot
-    fw = open(DIR_TMP + 'bb_obs_' + ID + '.cat', 'w')
-    fw_rem = open(DIR_TMP + 'bb_obs_' + ID + '_removed.cat', 'w')
+    fw = open(file_tmp,'w')#DIR_TMP + 'spec_obs_' + ID + '.cat', 'w')
+    # fw = open(DIR_TMP + 'bb_obs_' + ID + '.cat', 'w')
+    file_tmp2 = 'tmp_library2.txt'
+    fw_rem = open(file_tmp2, 'w')
+    # fw_rem = open(DIR_TMP + 'bb_obs_' + ID + '_removed.cat', 'w')
     for ii in range(len(ltmpbb[0,:])):
         if SFILT[ii] in SKIPFILT:# data point to be skiped;
             fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii], FWFILT[ii]/2., SFILT[ii]))
@@ -899,9 +923,29 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
             fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
     fw.close()
     fw_rem.close()
+    # register;
+    dat = ascii.read(file_tmp, format='no_header')
+    NRbb = dat['col1']
+    xbb  = dat['col2']
+    fybb = dat['col3']
+    eybb = dat['col4']
+    exbb = dat['col5']
+    dict_bb_obs = {'NR':NRbb, 'x':xbb, 'fy':fybb, 'ey':eybb, 'ex':exbb}
+    MB.data['bb_obs'] = dict_bb_obs
+    try:
+        dat = ascii.read(file_tmp2, format='no_header')
+        x_ex = dat['col2']
+        fy_ex = dat['col3']
+        ey_ex = dat['col4']
+        ex_ex = dat['col5']
+        dict_bb_obs_removed = {'NR':NR_ex, 'x':x_ex, 'fy':fy_ex, 'ey':ey_ex, 'ex':ex_ex}
+        MB.data['bb_obs_removed'] = dict_bb_obs_removed
+    except:
+        pass
 
-    # Dust
-    fw = open(DIR_TMP + 'bb_dust_obs_' + ID + '.cat', 'w')
+    # Dust; Not sure where this is being used...
+    fw = open(file_tmp,'w')
+    # fw = open(DIR_TMP + 'bb_dust_obs_' + ID + '.cat', 'w')
     if MB.f_dust:
         for ii in range(len(ebb_d[:])):
             if ebb_d[ii]>ebblim:
@@ -909,8 +953,21 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
             else:
                 fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii], DFWFILT[ii]/2., DFILT[ii]))
     fw.close()
+    if MB.f_dust:
+        dat = ascii.read(file_tmp, format='no_header')
+        nr_bb_d = dat['col1']
+        x_bb_d = dat['col2']
+        fy_bb_d = dat['col3']
+        ey_bb_d = dat['col4']
+        dict_bb_fir_obs = {'NR':nr_bb_d, 'x':x_bb_d, 'fy':fy_bb_d, 'ey':ey_bb_d}
+        MB.data['bb_fir_obs'] = dict_bb_fir_obs
 
     print('Done making templates at z=%.2f.\n'%zbest)
+    os.system('rm %s %s'%(file_tmp, file_tmp2))
+
+    # Loading template;
+    MB.af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
+    MB.af0 = asdf.open(MB.DIR_TMP + 'spec_all.asdf')
 
     return True
 
@@ -1198,7 +1255,6 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
                 if ss == 0 and tt == 0 and zz == 0:
                     DL = MB.cosmo.luminosity_distance(zbest).value * MB.Mpc_cm # Luminositydistance in cm
                     wavetmp = wave*(1.+zbest)
-                    Lsun = 3.839 * 1e33 #erg s-1
 
                 if delwave>0:
                     fint = interpolate.interp1d(lm0_orig, spechdu['fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss)][::nthin], kind='nearest', fill_value="extrapolate")
@@ -1217,7 +1273,7 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
 
                 spec_mul_nu[ss,:] = flamtonu(wave, spec_av_tmp, m0set=MB.m0set)
 
-                spec_mul_nu[ss,:] *= Lsun/(4.*np.pi*DL**2/(1.+zbest))
+                spec_mul_nu[ss,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
                 spec_mul_nu[ss,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
                 ms[ss] *= (1./Ls[ss])*tmp_norm # M/L; 1 unit template has this mass in [Msolar].
 
@@ -1292,7 +1348,7 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
                             spec_mul_neb[zz,uu,:] = spec_neb_av_tmp
 
                         spec_mul_neb_nu[zz,uu,:] = flamtonu(wave, spec_mul_neb[zz,uu,:], m0set=MB.m0set)
-                        spec_mul_neb_nu[zz,uu,:] *= Lsun/(4.*np.pi*DL**2/(1.+zbest))
+                        spec_mul_neb_nu[zz,uu,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
                         spec_mul_neb_nu[zz,uu,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
                         ltmpbb_neb[zz,uu,:], ftmpbb_neb[zz,uu,:] = filconv(SFILT, wavetmp, spec_mul_neb_nu[zz,uu,:], DIR_FILT, MB=MB, f_regist=False)
 
@@ -1428,7 +1484,9 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
         else:
             fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii]))
 
-    fw.close()
+    fw.close()    
+
+
     fw = open(DIR_TMP + 'spec_dust_obs_' + ID + '.cat', 'w')
     if MB.f_dust:
         nbblast = len(ltmpbb[0,:])+len(lm)
diff --git a/gsf/plot_sed.py b/gsf/plot_sed.py
index f80d068..156d98a 100644
--- a/gsf/plot_sed.py
+++ b/gsf/plot_sed.py
@@ -334,8 +334,8 @@ def gaus(x,a,x0,sigma):
         marker='.', color=col_dat, linestyle='', linewidth=0, zorder=4, ms=8)#, label='Obs.(BB)')
         try:
             # For any data removed fron fit (i.e. IRAC excess):
-            data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
-            NR_ex = data_ex['col1']
+            #data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
+            NR_ex = MB.data['bb_obs_removed']['NR']# data_ex['col1']
         except:
             NR_ex = []
 
@@ -357,15 +357,20 @@ def gaus(x,a,x0,sigma):
     try:
         col_ex = 'lawngreen'
         # Currently, this file is made after FILTER_SKIP;
-        data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
-        x_ex = data_ex['col2']
-        fy_ex = data_ex['col3']
-        ey_ex = data_ex['col4']
-        ex_ex = data_ex['col5']
-
-        ax1.errorbar(x_ex, fy_ex * c / np.square(x_ex) / d, \
-        xerr=ex_ex, yerr=ey_ex*c/np.square(x_ex)/d, color='k', linestyle='', linewidth=0.5, zorder=5)
-        ax1.scatter(x_ex, fy_ex * c / np.square(x_ex) / d, marker='s', color=col_ex, edgecolor='k', zorder=5, s=30)
+        # data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
+        # x_ex = data_ex['col2']
+        # fy_ex = data_ex['col3']
+        # ey_ex = data_ex['col4']
+        # ex_ex = data_ex['col5']
+        x_ex, fy_ex, ey_ex, ex_ex = MB.data['bb_obs_removed']['x'], MB.data['bb_obs_removed']['fy'], MB.data['bb_obs_removed']['ey'], MB.data['bb_obs_removed']['ex']
+
+        ax1.errorbar(
+            x_ex, fy_ex * c / np.square(x_ex) / d,
+            xerr=ex_ex, yerr=ey_ex*c/np.square(x_ex)/d, color='k', linestyle='', linewidth=0.5, zorder=5
+            )
+        ax1.scatter(
+            x_ex, fy_ex * c / np.square(x_ex) / d, marker='s', color=col_ex, edgecolor='k', zorder=5, s=30
+            )
         f_exclude = True
     except:
         pass
@@ -751,7 +756,7 @@ def gaus(x,a,x0,sigma):
         #if f_grsm:
             #ax2t.plot(x1_tot, ytmp[kk,:], '-', lw=0.5, color='gray', zorder=3., alpha=0.02)
 
-        # Get FUV flux;
+        # Get FUV flux density;
         Fuv[kk] = get_Fuv(x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)) * (DL**2/(1.+zbes)) / (DL10**2), lmin=1250, lmax=1650)
         Fuv28[kk] = get_Fuv(x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)) * (4*np.pi*DL**2/(1.+zbes))*Cmznu, lmin=1500, lmax=2800)
         Lir[kk] = 0
@@ -864,9 +869,6 @@ def func_tmp(xint,eobs,fmodel):
 
         con_up = (ey>0) & (fy/ey<=SNlim) & (f_ex == 0)
         from scipy import special
-        #x_erf = (ey[con_up] - ysump[con_up]) / (np.sqrt(2) * ey[con_up])
-        #f_erf = special.erf(x_erf)
-        #chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey[con_up] * (1 + f_erf)) )
         x_erf = (ey_revised[con_up] - ysump[con_up]) / (np.sqrt(2) * ey_revised[con_up])
         f_erf = special.erf(x_erf)
         chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey_revised[con_up] * (1 + f_erf)) )
@@ -1070,15 +1072,20 @@ def func_tmp(xint,eobs,fmodel):
             hdr['MUV50'] = -2.5 * np.log10(np.percentile(Fuv[:],50)) + MB.m0set
             hdr['MUV84'] = -2.5 * np.log10(np.percentile(Fuv[:],84)) + MB.m0set
 
+            # Flam to Fnu
+            hdr['LUV16'] = 10**(-0.4*hdr['MUV16'])
+            hdr['LUV50'] = 10**(-0.4*hdr['MUV50'])
+            hdr['LUV84'] = 10**(-0.4*hdr['MUV84'])
+
             # Fuv (!= flux of Muv)
             hdr['FUV16'] = np.percentile(Fuv28[:],16)
             hdr['FUV50'] = np.percentile(Fuv28[:],50)
             hdr['FUV84'] = np.percentile(Fuv28[:],84)
 
-            # LIR
-            hdr['LIR16'] = np.percentile(Lir[:],16)
-            hdr['LIR50'] = np.percentile(Lir[:],50)
-            hdr['LIR84'] = np.percentile(Lir[:],84)
+            # # LIR
+            # hdr['LIR16'] = np.percentile(Lir[:],16)
+            # hdr['LIR50'] = np.percentile(Lir[:],50)
+            # hdr['LIR84'] = np.percentile(Lir[:],84)
         except:
             pass
 
diff --git a/gsf/writing.py b/gsf/writing.py
index 26be777..2acfa6c 100644
--- a/gsf/writing.py
+++ b/gsf/writing.py
@@ -2,9 +2,9 @@
 from astropy.io import fits
 import asdf
 import gsf
-
 from .function import filconv, calc_Dn4
 
+
 def get_param(self, res, fitc, tcalc=1., burnin=-1):
     '''
     Purpose
@@ -154,12 +154,13 @@ def get_param(self, res, fitc, tcalc=1., burnin=-1):
     ############
     # Get SN.
     ############
-    file = DIR_TMP + 'spec_obs_' + ID0 + '.cat'
-    fds  = np.loadtxt(file, comments='#')
-    nrs  = fds[:,0]
-    lams = fds[:,1]
-    fsp  = fds[:,2]
-    esp  = fds[:,3]
+    # file = DIR_TMP + 'spec_obs_' + ID0 + '.cat'
+    # fds  = np.loadtxt(file, comments='#')
+    # nrs  = fds[:,0]
+    # lams = fds[:,1]
+    # fsp  = fds[:,2]
+    # esp  = fds[:,3]
+    nrs, lams, fsp, esp = self.data['spec_obs']['NR'], self.data['spec_obs']['x'], self.data['spec_obs']['fy'], self.data['spec_obs']['ey']
 
     consp = (nrs<10000) & (lams/(1.+zrecom)>3600) & (lams/(1.+zrecom)<4200) & (esp>0)
     NSN = len(fsp[consp])

From 6673484441896ee37178f42a47fee9859aee0725 Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Fri, 9 Dec 2022 17:08:20 -0800
Subject: [PATCH 02/14] clean

---
 gsf/fitting.py        |  36 +--
 gsf/function_class.py |  62 ++--
 gsf/gsf.py            |  13 +-
 gsf/maketmp_filt.py   | 667 +++++++++++++++++++++---------------------
 gsf/writing.py        |   6 -
 5 files changed, 397 insertions(+), 387 deletions(-)

diff --git a/gsf/fitting.py b/gsf/fitting.py
index b688dfe..f0822e8 100644
--- a/gsf/fitting.py
+++ b/gsf/fitting.py
@@ -86,7 +86,6 @@ def update_input(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:
             Conversion factor from pixel-to-arcsec
 
         '''
-
         # Then register;
         self.inputs = inputs
         self.c = c
@@ -290,7 +289,7 @@ def update_input(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:
 
         # Tau comparison?
         # -> Deprecated;
-        self.ftaucomp = 0
+        # self.ftaucomp = 0
 
         # Check if func model for SFH;
         try:
@@ -646,6 +645,7 @@ def update_input(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:
 
         print('\n')
 
+
     def get_lines(self, LW0):
         fLW = np.zeros(len(LW0), dtype='int')
         LW = LW0
@@ -654,6 +654,10 @@ def get_lines(self, LW0):
 
     def read_data(self, Cz0:float, Cz1:float, Cz2:float, zgal:float, add_fir:bool=False, idman=None):
         '''
+        Purpose
+        -------
+        Read in observed data. Not model fluxes.
+
         Parameters
         ----------
         Cz0, Cz1 : float
@@ -674,11 +678,6 @@ def read_data(self, Cz0:float, Cz1:float, Cz2:float, zgal:float, add_fir:bool=Fa
         ##############
         # Spectrum
         ##############
-        # dat = ascii.read(self.DIR_TMP + 'spec_obs_' + self.ID + '.cat', format='no_header')
-        # NR = dat['col1']
-        # x = dat['col2']
-        # fy00 = dat['col3']
-        # ey00 = dat['col4']
         NR, x, fy00, ey00 = self.data['spec_obs']['NR'], self.data['spec_obs']['x'], self.data['spec_obs']['fy'], self.data['spec_obs']['ey']
         con0 = (NR<1000)
         xx0 = x[con0]
@@ -697,12 +696,6 @@ def read_data(self, Cz0:float, Cz1:float, Cz2:float, zgal:float, add_fir:bool=Fa
         # Broadband
         ##############
         try:
-            # dat = ascii.read(self.DIR_TMP + 'bb_obs_' + self.ID + '.cat', format='no_header')
-            # NRbb = dat['col1']
-            # xbb  = dat['col2']
-            # fybb = dat['col3']
-            # eybb = dat['col4']
-            # exbb = dat['col5']
             NRbb, xbb, fybb, eybb, exbb = self.data['bb_obs']['NR'], self.data['bb_obs']['x'], self.data['bb_obs']['fy'], self.data['bb_obs']['ey'], self.data['bb_obs']['ex']
         except: # if no BB;
             print('No BB data.')
@@ -740,13 +733,7 @@ def read_data(self, Cz0:float, Cz1:float, Cz2:float, zgal:float, add_fir:bool=Fa
 
         # Append data;
         if add_fir:
-            # dat_d = ascii.read(self.DIR_TMP + 'spec_dust_obs_' + self.ID + '.cat')
-            # nr_d = dat_d['col1']
-            # x_d = dat_d['col2']
-            # fy_d = dat_d['col3']
-            # ey_d = dat_d['col4']
             nr_d, x_d, fy_d, ey_d = self.data['spec_dust_obs']['NR'], self.data['spec_dust_obs']['x'], self.data['spec_dust_obs']['fy'], self.data['spec_dust_obs']['ey']
-
             NR = np.append(NR,nr_d)
             fy = np.append(fy,fy_d)
             ey = np.append(ey,ey_d)
@@ -1036,7 +1023,7 @@ def fit_redshift(self, xm_tmp, fm_tmp, delzz=0.01, ezmin=0.01, zliml=0.01,
                 fy_cz, ey_cz, x_cz, fm_tmp, xm_tmp/(1+self.zgal), 
                 self.zgal, self.z_prior, self.p_prior,
                 NR_cz, zliml, zlimu, self.nmc_cz, self.nwalk_cz, 
-                include_photometry=include_photometry_zfit
+                include_photometry=include_bb
                 )
 
             z_cz = np.percentile(res_cz.flatchain['z'], [16,50,84])
@@ -1596,7 +1583,7 @@ def get_shuffle(self, out, nshuf=3.0, amp=1e-4):
     def main(self, cornerplot:bool=True, specplot=1, sigz=1.0, ezmin=0.01, ferr=0,
             f_move:bool=False, verbose:bool=False, skip_fitz:bool=False, out=None, f_plot_accept:bool=True,
             f_shuffle:bool=True, amp_shuffle=1e-2, check_converge:bool=True, Zini=None, f_plot_chain:bool=True,
-            f_chind:bool=True, ncpu:int=0, f_prior_sfh:bool=False, norder_sfh_prior:int=3):
+            f_chind:bool=True, ncpu:int=0, f_prior_sfh:bool=False, norder_sfh_prior:int=3, include_bb=True):
         '''
         Main module of this script.
 
@@ -1686,7 +1673,7 @@ def main(self, cornerplot:bool=True, specplot=1, sigz=1.0, ezmin=0.01, ferr=0,
         if skip_fitz:
             flag_z = 'y'
         else:
-            flag_z = self.fit_redshift(xm_tmp, fm_tmp, delzz=0.001, include_bb=False)
+            flag_z = self.fit_redshift(xm_tmp, fm_tmp, delzz=0.001, include_bb=include_bb)
 
         #################################################
         # Gor for mcmc phase
@@ -2077,7 +2064,7 @@ def __init__(self, flatchain, var_names, params_value, res):
                 print('\n\n')
                 return False
 
-
+    """
     def quick_fit(self, specplot=1, sigz=1.0, ezmin=0.01, ferr=0, f_move=False, f_get_templates=False, Zini=None):
         '''
         Fits input data with a prepared template library, to get a chi-min result.
@@ -2121,10 +2108,11 @@ def quick_fit(self, specplot=1, sigz=1.0, ezmin=0.01, ferr=0, f_move=False, f_ge
             # Check redshift
             ########################
             if self.fzvis:
-                flag_z = self.fit_redshift(xm_tmp, fm_tmp)
+                flag_z = self.fit_redshift(xm_tmp, fm_tmp, include_bb=include_bb)
 
             self.fzmc = 1
             return out,chidef,Zbest, xm_tmp, fm_tmp
         else:
             self.fzmc = 1
             return out,chidef,Zbest
+    """
\ No newline at end of file
diff --git a/gsf/function_class.py b/gsf/function_class.py
index 3e72079..8e4aa9b 100644
--- a/gsf/function_class.py
+++ b/gsf/function_class.py
@@ -2,10 +2,12 @@
 import sys
 import scipy.interpolate as interpolate
 import asdf
+from astropy.io import fits
 
 from .function import *
 from .basic_func import Basic
 from .function_igm import dijkstra_igm_abs
+from .maketmp_filt import maketemp,maketemp_tau
 
 
 class Func:
@@ -52,8 +54,6 @@ def open_spec_fits(self, fall:int = 0, orig:bool = False):
         '''
         ID0 = self.MB.ID
         tau0= self.MB.tau0
-
-        from astropy.io import fits
         ZZ = self.ZZ
         AA = self.AA
         bfnc = self.MB.bfnc
@@ -94,16 +94,12 @@ def open_spec_dust_fits(self, fall:int = 0):
         '''
         Loads dust template in obs range.
         '''
-        from astropy.io import fits
         ID0 = self.MB.ID
         tau0= self.MB.tau0
         ZZ = self.ZZ
         AA = self.AA
         bfnc = self.MB.bfnc
 
-        # self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
-        # self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
-
         if fall == 0:
             app = ''
             hdu0 = self.MB.af['spec_dust']
@@ -136,7 +132,6 @@ def open_spec_neb_fits(self, fall:int = 0, orig:bool = False):
         '''
         Loads template in obs range.
         '''
-        from astropy.io import fits
         ID0 = self.MB.ID
         ZZ = self.ZZ
         AA = self.AA
@@ -179,15 +174,11 @@ def open_spec_fits_dir(self, nage:int, nz:int, kk, Av00:float, zgal:float, A00:f
         Load template in obs range.
         But for weird template.
         '''
-        from astropy.io import fits
         tau0= self.tau0
         ZZ = self.ZZ
         AA = self.AA
         bfnc = self.MB.bfnc
 
-        # self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
-        # self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
-
         app = 'all'
         hdu0 = self.MB.af['spec_full']
         DIR_TMP = self.DIR_TMP
@@ -199,8 +190,8 @@ def open_spec_fits_dir(self, nage:int, nz:int, kk, Av00:float, zgal:float, A00:f
         mshdu = self.MB.af0['ML']
         Ls = mshdu['Ls_%d'%nz] 
 
-        xx = hdu0['wavelength'] # at RF;
-        nr = np.arange(0,len(xx),1) #hdu0.data['colnum']
+        xx = hdu0['wavelength']
+        nr = np.arange(0,len(xx),1)
 
         lib = np.zeros((len(nr), 2+1), dtype='float')
         lib[:,0] = nr[:]
@@ -513,9 +504,47 @@ def tmp04(self, par, f_Alog:bool=True, nprec:int=1, pp:int = 0, f_val:bool=False
 
         # @@@ Filter convolution may need to happpen here
         if round(zmc,nprec) != round(self.MB.zgal,nprec):
-            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
-            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
-            yy_s = fint(xx_s)
+            if True:
+                fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
+                xx_s = xx / (1+self.MB.zgal) * (1+zmc)
+                yy_s = fint(xx_s)
+            else:
+                # This takes too much time;
+                # self.MB.zgal = zmc
+                # if self.MB.SFH_FORM == -99:
+                #     flag_suc = maketemp(self.MB, tau_lim=self.MB.tau_lim, nthin=self.MB.nthin, delwave=self.MB.delwave)
+                # else:
+                #     flag_suc = maketemp_tau(self.MB, tau_lim=self.MB.tau_lim, nthin=self.MB.nthin, delwave=self.MB.delwave)
+
+                # # Load Spectral library;
+                # self.lib = self.open_spec_fits(fall=0)
+                # self.lib_all = self.open_spec_fits(fall=1, orig=True)
+
+                # if self.MB.f_dust:
+                #     self.lib_dust = self.open_spec_dust_fits(fall=0)
+                #     self.lib_dust_all = self.open_spec_dust_fits(fall=1)
+                # if self.MB.fneb:
+                #     self.lib_neb = self.open_spec_neb_fits(fall=0)
+                #     self.lib_neb_all = self.open_spec_neb_fits(fall=1)
+
+                # if lib_all:
+                #     if aa == 0:
+                #         nr = self.MB.lib_all[:,0]
+                #         xx = self.MB.lib_all[:,1] # This is OBSERVED wavelength range at z=zgal
+                #         yy = A00 * self.MB.lib_all[:,coln]
+                #     else:
+                #         yy += A00 * self.MB.lib_all[:,coln]
+                # else:
+                #     if aa == 0:
+                #         nr = self.MB.lib[:,0]
+                #         xx = self.MB.lib[:,1] # This is OBSERVED wavelength range at z=zgal
+                #         yy = A00 * self.MB.lib[:,coln]
+                #     else:
+                #         yy += A00 * self.MB.lib[:,coln]
+
+                xx_s = xx
+                yy_s = yy
+
         else:
             xx_s = xx
             yy_s = yy
@@ -767,7 +796,6 @@ def open_spec_fits(self, fall:int=0, orig:bool=False):
         '''
         Loads template in obs range.
         '''
-        from astropy.io import fits
         ID0 = self.MB.ID
         ZZ = self.ZZ
         AA = self.AA
diff --git a/gsf/gsf.py b/gsf/gsf.py
index 507a42d..59f1b93 100644
--- a/gsf/gsf.py
+++ b/gsf/gsf.py
@@ -129,6 +129,11 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
     inputs = read_input(parfile)
 
     MB = Mainbody(inputs, c=3e18, Mpc_cm=3.08568025e+24, m0set=25.0, pixelscale=0.06, cosmo=cosmo, idman=idman, zman=zman)
+    
+    # Register some params;
+    MB.tau_lim = tau_lim
+    MB.nthin = nthin
+    MB.delwave = delwave
 
     if os.path.exists(MB.DIR_TMP) == False:
         os.mkdir(MB.DIR_TMP)
@@ -185,9 +190,9 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
         # 1. Start making redshifted templates, at z=MB.zgal.
         #
         if MB.SFH_FORM == -99:
-            flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+            flag_suc = maketemp(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
         else:
-            flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+            flag_suc = maketemp_tau(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
 
         #
         # 2. Main fitting part.
@@ -204,9 +209,9 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
 
             # Make temp at the new z
             if MB.SFH_FORM == -99:
-                flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+                flag_suc = maketemp(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
             else:
-                flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+                flag_suc = maketemp_tau(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
 
             print('\n\n')
             print('Going into another round with updated templates and redshift.')
diff --git a/gsf/maketmp_filt.py b/gsf/maketmp_filt.py
index 538c80d..4278e5b 100644
--- a/gsf/maketmp_filt.py
+++ b/gsf/maketmp_filt.py
@@ -293,7 +293,6 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
         Normalization of the stored templated. i.e. each template is in units of tmp_norm [Lsun].
     '''
     inputs = MB.inputs
-    ID = MB.ID
     age = MB.age
     nage = MB.nage
     Z = MB.Zall
@@ -302,10 +301,18 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
     zbest = MB.zgal
     tau0 = MB.tau0
 
+    delz = 1.0
+    wid_z = MB.zmcmax - MB.zmcmin
+    nz = int(wid_z/delz)
+    if nz%2 == 0:
+        nz += 1
+    MB.zbests = np.linspace(MB.zgal - wid_z/2., MB.zgal + wid_z/2., nz)
+    MB.zbests = np.arange(MB.zgal, MB.zgal + 0.01, delz)
+
     try:
         af = MB.af0
     except:
-        af = asdf.open(DIR_TMP + 'spec_all.asdf')
+        af = asdf.open(os.path.join(DIR_TMP, 'spec_all.asdf'))
         MB.af0 = af
 
     mshdu = af['ML']
@@ -391,7 +398,7 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
     #
     f_spec = False
     try:
-        spec_files = inputs['SPEC_FILE'] #.replace('$ID','%s'%(ID))
+        spec_files = inputs['SPEC_FILE']
         spec_files = [x.strip() for x in spec_files.split(',')]
         ninp0 = np.zeros(len(spec_files), dtype='int')
         for ff, spec_file in enumerate(spec_files):
@@ -405,9 +412,9 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
                 print('File, %s/%s, cannot be open.'%(DIR_EXTR,spec_file))
                 pass
         # Constructing arrays.
-        lm = np.zeros(np.sum(ninp0[:]),dtype='float')
-        fobs = np.zeros(np.sum(ninp0[:]),dtype='float')
-        eobs = np.zeros(np.sum(ninp0[:]),dtype='float')
+        lm = np.zeros(np.sum(ninp0[:]),dtype=float)
+        fobs = np.zeros(np.sum(ninp0[:]),dtype=float)
+        eobs = np.zeros(np.sum(ninp0[:]),dtype=float)
         fgrs = np.zeros(np.sum(ninp0[:]),dtype='int')  # FLAG for each grism.
         for ff, spec_file in enumerate(spec_files):
             try:
@@ -443,19 +450,19 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
             key_id = 'ID'
             id0 = fd0[key_id].astype('str')
 
-        ii0 = np.where(id0[:]==ID)
+        ii0 = np.where(id0[:]==MB.ID)
         try:
             if len(ii0[0]) == 0:
-                print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(ID))
+                print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(MB.ID))
                 return False
             id = fd0[key_id][ii0]
         except:
-            print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(ID))
+            print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(MB.ID))
             print(fd0)
             return False
 
-        fbb = np.zeros(len(SFILT), dtype='float')
-        ebb = np.zeros(len(SFILT), dtype='float')
+        fbb = np.zeros(len(SFILT), dtype=float)
+        ebb = np.zeros(len(SFILT), dtype=float)
 
         for ii in range(len(SFILT)):
             try:
@@ -466,6 +473,7 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
                 return False
 
     elif CAT_BB_IND: # if individual photometric catalog; made in get_sdss.py
+        unit = 'nu'
         fd0 = fits.open(DIR_EXTR + CAT_BB_IND)
         hd0 = fd0[1].header
         bunit_bb = float(hd0['bunit'][:5])
@@ -473,12 +481,11 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
         fbb0 = fd0[1].data['flux'] * bunit_bb
         ebb0 = 1/np.sqrt(fd0[1].data['inverse_variance']) * bunit_bb
 
-        unit  = 'nu'
         try:
             unit = inputs['UNIT_SPEC']
         except:
             print('No param for UNIT_SPEC is found.')
-            print('BB flux unit is assumed to Fnu.')
+            print('BB flux unit is assumed to F%s.'%unit)
             pass
 
         if unit == 'lambda':
@@ -493,8 +500,8 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
             ebb = ebb0
 
     else:
-        fbb = np.zeros(len(SFILT), dtype='float')
-        ebb = np.zeros(len(SFILT), dtype='float')
+        fbb = np.zeros(len(SFILT), dtype=float)
+        ebb = np.zeros(len(SFILT), dtype=float)
         for ii in range(len(SFILT)):
             fbb[ii] = 0
             ebb[ii] = -99 #1000
@@ -510,19 +517,19 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
                 key_id = 'ID'
                 id0 = fdd[key_id].astype('str')
 
-            ii0 = np.where(id0[:]==ID)
+            ii0 = np.where(id0[:]==MB.ID)
             try:
                 id = fd0[key_id][ii0]
             except:
-                print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(ID))
+                print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(MB.ID))
                 return False
         except:
             return False
             
         id = fdd[key_id]
 
-        fbb_d = np.zeros(len(DFILT), dtype='float')
-        ebb_d = np.zeros(len(DFILT), dtype='float')
+        fbb_d = np.zeros(len(DFILT), dtype=float)
+        ebb_d = np.zeros(len(DFILT), dtype=float)
         for ii in range(len(DFILT)):
             fbb_d[ii] = fdd['F%s'%(DFILT[ii])][ii0]
             ebb_d[ii] = fdd['E%s'%(DFILT[ii])][ii0]
@@ -531,208 +538,209 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
     # Get morphology;
     #################
     if f_spec:
-        LSF, lm = get_LSF(inputs, DIR_EXTR, ID, lm)
+        LSF, lm = get_LSF(inputs, DIR_EXTR, MB.ID, lm)
     else:
         lm = []
 
-    ####################################
-    # Start generating templates
-    ####################################
-    for zz in range(len(Z)):
-        for pp in range(len(tau0)):
-            Zbest = Z[zz]
-            Na = len(age)
-            Ntmp = 1
-            age_univ= MB.cosmo.age(zbest).value #, use_flat=True, **cosmo)
-
-            if zz == 0 and pp == 0:
-                if delwave>0:
-                    lm0_orig = spechdu['wavelength'][::nthin]
-                    lm0 = np.arange(lm0_orig.min(), lm0_orig.max(), delwave)
-                else:
-                    lm0 = spechdu['wavelength'][::nthin]
-                if not lammax == None and not MB.f_dust:
-                    lm0 = lm0[(lm0 * (zbest+1) < lammax)]
-
-            lmbest = np.zeros((Ntmp, len(lm0)), dtype='float')
-            fbest = np.zeros((Ntmp, len(lm0)), dtype='float')
-            lmbestbb = np.zeros((Ntmp, len(SFILT)), dtype='float')
-            fbestbb = np.zeros((Ntmp, len(SFILT)), dtype='float')
-
-            spec_mul = np.zeros((Na, len(lm0)), dtype='float')
-            spec_mul_nu = np.zeros((Na, len(lm0)), dtype='float')
-            spec_mul_nu_conv = np.zeros((Na, len(lm0)), dtype='float')
-
-            ftmpbb = np.zeros((Na, len(SFILT)), dtype='float')
-            ltmpbb = np.zeros((Na, len(SFILT)), dtype='float')
-
-            ftmp_nu_int = np.zeros((Na, len(lm)), dtype='float')
-            spec_av_tmp = np.zeros((Na, len(lm)), dtype='float')
-
-            ms = np.zeros(Na, dtype='float')
-            Ls = np.zeros(Na, dtype='float')
-            tau = np.zeros(Na, dtype='float')
-            sfr = np.zeros(Na, dtype='float')
-            ms[:] = mshdu['ms_'+str(zz)][:] # [:] is necessary.
-            Ls[:] = mshdu['Ls_'+str(zz)][:]
-            Fuv = np.zeros(Na, dtype='float')
-
-            for ss in range(Na):
-                wave = lm0
-                # Espec no more needed
-                if False:# fneb == 1 and MB.f_bpass==0:
-                    if delwave>0:
-                        fint = interpolate.interp1d(lm0_orig, spechdu['efspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin], kind='nearest', fill_value="extrapolate")
-                        spec_mul[ss] = fint(lm0)
-                    else:
-                        spec_mul[ss] = spechdu['efspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin]
-                else:
-                    if delwave>0:
-                        fint = interpolate.interp1d(lm0_orig, spechdu['fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin], kind='nearest', fill_value="extrapolate")
-                        spec_mul[ss] = fint(lm0)
-                    else:
-                        spec_mul[ss] = spechdu['fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin] # Lsun/A
-
-                ###################
-                # IGM attenuation.
-                ###################
-                if f_IGM:
-                    # spec_av_tmp = madau_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
-                    spec_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
-                    MB.x_HI = x_HI
-                    spec_mul[ss,:] = spec_av_tmp
-
-                # Distance;
-                DL = MB.cosmo.luminosity_distance(zbest).value * MB.Mpc_cm # Luminositydistance in cm
-                wavetmp = wave*(1.+zbest)
-
-                # Flam to Fnu
-                spec_mul_nu[ss,:] = flamtonu(wave, spec_mul[ss,:], m0set=MB.m0set)
-                spec_mul_nu[ss,:] *= MB.Lsun / (4.*np.pi*DL**2/(1.+zbest))
-                # (1.+zbest) takes acount of the change in delta lam by redshifting.
-                # Note that this is valid only when F_nu.
-                # When Flambda, /(1.+zbest) will be *(1.+zbest).
-
-                spec_mul_nu[ss,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
-                ms[ss] *= (1./Ls[ss])*tmp_norm # M/L; 1 unit template has this mass/solar.
-                try:
-                    tautmp = af['ML']['realtau_%d'%int(zz)]
-                    sfr[ss] = ms[ss] / (tautmp[ss]*1e9) # SFR per unit template, in units of Msolar/yr.
-                except:
-                    if zz == 0 and ss == 0:
-                        print('realtau entry not found. SFR is set to 0. (Or rerun maketmp_z0.py)')
-                    sfr[ss] = 0
-
-                if f_spec:
-                    ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu[ss,:])
-
-                ltmpbb[ss,:], ftmpbb[ss,:] = filconv(SFILT, wavetmp, spec_mul_nu[ss,:], DIR_FILT, MB=MB, f_regist=False)
-
-                # Convolution has to come after this?
-                if len(lm)>0:
-                    try:
-                        spec_mul_nu_conv[ss,:] = convolve(spec_mul_nu[ss], LSF, boundary='extend')
-                    except:
-                        #print('Error. No convolution is happening...')
-                        spec_mul_nu_conv[ss,:] = spec_mul_nu[ss]
-                        if zz==0 and ss==0:
-                            print('Kernel is too small. No convolution.')
-                else:
-                    spec_mul_nu_conv[ss,:] = spec_mul_nu[ss]
+    if MB.SFH_FORM == -99:
+        ####################################
+        # Start generating templates
+        ####################################
+        for zz in range(len(Z)):
+            for pp in range(len(tau0)):
+                Zbest = Z[zz]
+                Na = len(age)
+                Ntmp = 1
 
-                ##########################################
-                # Writing out the templates to fits table.
-                ##########################################
-                if ss == 0 and pp == 0 and zz == 0:
-                    # First file
-                    nd1 = np.arange(0,len(lm),1)
-                    nd3 = np.arange(10000,10000+len(ltmpbb[ss,:]),1)
-                    nd_ap = np.append(nd1,nd3)
-                    lm_ap = np.append(lm, ltmpbb[ss,:])
+                for nzz, zbest in enumerate(MB.zbests):
 
-                    # ASDF
-                    tree_spec.update({'wavelength':lm_ap})
-                    tree_spec.update({'colnum':nd_ap})
+                    age_univ= MB.cosmo.age(zbest).value
 
-                    # Second file
-                    # ASDF
-                    nd = np.arange(0,len(wavetmp),1)
-                    tree_spec_full.update({'wavelength':wavetmp})
-                    tree_spec_full.update({'colnum':nd})
-
-                # ASDF
-                # ???
-                spec_ap = np.append(ftmp_nu_int[ss,:], ftmpbb[ss,:])
-                tree_spec.update({'fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp): spec_ap})
-                tree_spec_full.update({'fspec_orig_'+str(zz)+'_'+str(ss)+'_'+str(pp): spec_mul_nu[ss,:]})
-                #tree_spec.update({'fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp): spec_mul_nu_conv[ss,:]})
-
-                # For nebular library;
-                # For every Z, but not for ss and pp.
-                if fneb == 1 and MB.f_bpass==0 and ss==0 and pp==0:
-                    if zz==0:
-                        spec_mul_neb = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
-                        spec_mul_neb_nu = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
-                        spec_mul_neb_nu_conv = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
-                        ftmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
-                        ltmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
-                        ftmp_neb_nu_int = np.zeros((len(Z), len(MB.logUs), len(lm)), dtype=float)
-
-                    for uu in range(len(MB.logUs)):
+                    if zz == 0 and pp == 0:
                         if delwave>0:
-                            fint = interpolate.interp1d(lm0_orig, spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin], kind='nearest', fill_value="extrapolate")
-                            spec_mul_neb[zz,uu,:] = fint(lm0)
+                            lm0_orig = spechdu['wavelength'][::nthin]
+                            lm0 = np.arange(lm0_orig.min(), lm0_orig.max(), delwave)
                         else:
-                            spec_mul_neb[zz,uu,:] = spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin]
-                        
-                        con_neb = (spec_mul_neb[zz,uu,:]<0)
-                        spec_mul_neb[zz,uu,:][con_neb] = 0
-                        
-                        if f_IGM:
-                            # spec_neb_av_tmp = madau_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
-                            spec_neb_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
-                            # spec_neb_av_tmp = masongronke_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
-                            spec_mul_neb[zz,uu,:] = spec_neb_av_tmp
+                            lm0 = spechdu['wavelength'][::nthin]
+                        if not lammax == None and not MB.f_dust:
+                            lm0 = lm0[(lm0 * (zbest+1) < lammax)]
+
+                    lmbest = np.zeros((Ntmp, len(lm0)), dtype=float)
+                    fbest = np.zeros((Ntmp, len(lm0)), dtype=float)
+                    lmbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
+                    fbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
+
+                    spec_mul = np.zeros((Na, len(lm0)), dtype=float)
+                    spec_mul_nu = np.zeros((Na, len(lm0)), dtype=float)
+                    spec_mul_nu_conv = np.zeros((Na, len(lm0)), dtype=float)
+
+                    ftmpbb = np.zeros((Na, len(SFILT)), dtype=float)
+                    ltmpbb = np.zeros((Na, len(SFILT)), dtype=float)
+
+                    ftmp_nu_int = np.zeros((Na, len(lm)), dtype=float)
+                    spec_av_tmp = np.zeros((Na, len(lm)), dtype=float)
+
+                    ms = np.zeros(Na, dtype=float)
+                    Ls = np.zeros(Na, dtype=float)
+                    tau = np.zeros(Na, dtype=float)
+                    sfr = np.zeros(Na, dtype=float)
+                    ms[:] = mshdu['ms_'+str(zz)][:] # [:] is necessary.
+                    Ls[:] = mshdu['Ls_'+str(zz)][:]
+                    Fuv = np.zeros(Na, dtype=float)
+
+                    for ss in range(Na):
+                        wave = lm0
+                        if delwave>0:
+                            fint = interpolate.interp1d(lm0_orig, spechdu['fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin], kind='nearest', fill_value="extrapolate")
+                            spec_mul[ss] = fint(lm0)
+                        else:
+                            spec_mul[ss] = spechdu['fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin] # Lsun/A
 
-                        spec_mul_neb_nu[zz,uu,:] = flamtonu(wave, spec_mul_neb[zz,uu,:], m0set=MB.m0set)
-                        
-                        spec_mul_neb_nu[zz,uu,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
-                        
-                        spec_mul_neb_nu[zz,uu,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
-                        ltmpbb_neb[zz,uu,:], ftmpbb_neb[zz,uu,:] = filconv(SFILT, wavetmp, spec_mul_neb_nu[zz,uu,:], DIR_FILT, MB=MB, f_regist=False)
+                        ###################
+                        # IGM attenuation.
+                        ###################
+                        if f_IGM:
+                            # spec_av_tmp = madau_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
+                            spec_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
+                            MB.x_HI = x_HI
+                            spec_mul[ss,:] = spec_av_tmp
+
+                        # Distance;
+                        DL = MB.cosmo.luminosity_distance(zbest).value * MB.Mpc_cm # Luminositydistance in cm
+                        wavetmp = wave*(1.+zbest)
+
+                        # Flam to Fnu
+                        spec_mul_nu[ss,:] = flamtonu(wave, spec_mul[ss,:], m0set=MB.m0set)
+                        spec_mul_nu[ss,:] *= MB.Lsun / (4.*np.pi*DL**2/(1.+zbest))
+                        # (1.+zbest) takes acount of the change in delta lam by redshifting.
+                        # Note that this is valid only when F_nu.
+                        # When Flambda, /(1.+zbest) will be *(1.+zbest).
+
+                        spec_mul_nu[ss,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
+                        ms[ss] *= (1./Ls[ss])*tmp_norm # M/L; 1 unit template has this mass/solar.
+                        try:
+                            tautmp = af['ML']['realtau_%d'%int(zz)]
+                            sfr[ss] = ms[ss] / (tautmp[ss]*1e9) # SFR per unit template, in units of Msolar/yr.
+                        except:
+                            if zz == 0 and ss == 0:
+                                print('realtau entry not found. SFR is set to 0. (Or rerun maketmp_z0.py)')
+                            sfr[ss] = 0
 
                         if f_spec:
-                            ftmp_neb_nu_int[zz,uu,:] = data_int(lm, wavetmp, spec_mul_neb_nu[zz,uu,:])
+                            ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu[ss,:])
 
+                        ltmpbb[ss,:], ftmpbb[ss,:] = filconv(SFILT, wavetmp, spec_mul_nu[ss,:], DIR_FILT, MB=MB, f_regist=False)
+
+                        # Convolution has to come after this?
                         if len(lm)>0:
                             try:
-                                spec_mul_neb_nu_conv[zz,uu,:] = convolve(spec_mul_neb_nu[zz,uu,:], LSF, boundary='extend')
+                                spec_mul_nu_conv[ss,:] = convolve(spec_mul_nu[ss], LSF, boundary='extend')
                             except:
-                                spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
+                                #print('Error. No convolution is happening...')
+                                spec_mul_nu_conv[ss,:] = spec_mul_nu[ss]
+                                if zz==0 and ss==0:
+                                    print('Kernel is too small. No convolution.')
                         else:
-                            spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
-
-                        tree_spec_full.update({'fspec_orig_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu[zz,uu,:]})
-                        tree_spec_full.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu_conv[zz,uu,:]})
-
-                        spec_neb_ap = np.append(ftmp_neb_nu_int[zz,uu,:], ftmpbb_neb[zz,uu,:])
-                        tree_spec.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_neb_ap})
+                            spec_mul_nu_conv[ss,:] = spec_mul_nu[ss]
+
+                        ##########################################
+                        # Writing out the templates to fits table.
+                        ##########################################
+                        if ss == 0 and pp == 0 and zz == 0:
+                            # First file
+                            nd1 = np.arange(0,len(lm),1)
+                            nd3 = np.arange(10000,10000+len(ltmpbb[ss,:]),1)
+                            nd_ap = np.append(nd1,nd3)
+                            lm_ap = np.append(lm, ltmpbb[ss,:])
+
+                            # ASDF
+                            tree_spec.update({'wavelength':lm_ap})
+                            tree_spec.update({'colnum':nd_ap})
+
+                            # Second file
+                            # ASDF
+                            nd = np.arange(0,len(wavetmp),1)
+                            if zbest == MB.zgal:
+                                tree_spec_full.update({'wavelength':wavetmp})
+                                tree_spec_full.update({'colnum':nd})
+
+                        # ASDF
+                        spec_ap = np.append(ftmp_nu_int[ss,:], ftmpbb[ss,:])
+                        tree_spec.update({'fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp): spec_ap})
+                        if zbest == MB.zgal:
+                            tree_spec_full.update({'fspec_orig_'+str(zz)+'_'+str(ss)+'_'+str(pp): spec_mul_nu[ss,:]})
+
+                        # For nebular library;
+                        # For every Z, but not for ss and pp.
+                        if fneb == 1 and MB.f_bpass==0 and ss==0 and pp==0:
+                            if zz==0:
+                                spec_mul_neb = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                                spec_mul_neb_nu = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                                spec_mul_neb_nu_conv = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                                ftmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
+                                ltmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
+                                ftmp_neb_nu_int = np.zeros((len(Z), len(MB.logUs), len(lm)), dtype=float)
+
+                            for uu in range(len(MB.logUs)):
+                                if delwave>0:
+                                    fint = interpolate.interp1d(lm0_orig, spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin], kind='nearest', fill_value="extrapolate")
+                                    spec_mul_neb[zz,uu,:] = fint(lm0)
+                                else:
+                                    spec_mul_neb[zz,uu,:] = spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin]
+                                
+                                con_neb = (spec_mul_neb[zz,uu,:]<0)
+                                spec_mul_neb[zz,uu,:][con_neb] = 0
+                                
+                                if f_IGM:
+                                    # spec_neb_av_tmp = madau_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
+                                    spec_neb_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
+                                    # spec_neb_av_tmp = masongronke_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
+                                    spec_mul_neb[zz,uu,:] = spec_neb_av_tmp
+
+                                spec_mul_neb_nu[zz,uu,:] = flamtonu(wave, spec_mul_neb[zz,uu,:], m0set=MB.m0set)
+                                
+                                spec_mul_neb_nu[zz,uu,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
+                                
+                                spec_mul_neb_nu[zz,uu,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
+                                ltmpbb_neb[zz,uu,:], ftmpbb_neb[zz,uu,:] = filconv(SFILT, wavetmp, spec_mul_neb_nu[zz,uu,:], DIR_FILT, MB=MB, f_regist=False)
+
+                                if f_spec:
+                                    ftmp_neb_nu_int[zz,uu,:] = data_int(lm, wavetmp, spec_mul_neb_nu[zz,uu,:])
+
+                                if len(lm)>0:
+                                    try:
+                                        spec_mul_neb_nu_conv[zz,uu,:] = convolve(spec_mul_neb_nu[zz,uu,:], LSF, boundary='extend')
+                                    except:
+                                        spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
+                                else:
+                                    spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
+
+                                if zbest == MB.zgal:
+                                    tree_spec_full.update({'fspec_orig_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu[zz,uu,:]})
+                                    tree_spec_full.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu_conv[zz,uu,:]})
+
+                                spec_neb_ap = np.append(ftmp_neb_nu_int[zz,uu,:], ftmpbb_neb[zz,uu,:])
+                                tree_spec.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_neb_ap})
+
+
+                    #########################
+                    # Summarize the ML
+                    #########################
+                    if pp == 0:
+                        # ML
+                        tree_ML.update({'ML_'+str(zz): ms})
+                        # SFR
+                        tree_SFR.update({'SFR_'+str(zz): sfr})
 
+    else:
+        print('Tau model comes here;')
 
-            #########################
-            # Summarize the ML
-            #########################
-            if pp == 0:
-                # ML
-                tree_ML.update({'ML_'+str(zz): ms})
-                # SFR
-                tree_SFR.update({'SFR_'+str(zz): sfr})
 
 
     #########################
     # Summarize the templates
     #########################
-    tree['id'] = ID
+    tree['id'] = MB.ID
     tree['z'] = zbest
     try:
         tree['x_HI'] = x_HI
@@ -813,7 +821,6 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
                 fnu_d = fnutonu(fnu_d, m0set=MB.m0set, m0input=-48.6)
                 Mdust_temp[tt] = 1. # Msun/temp, like Mass to light ratio.
 
-
             # ASDF
             tree_spec_dust_full.update({'fspec_'+str(tt): fnu_d})
 
@@ -837,137 +844,125 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
             tree_spec_dust.update({'fspec_'+str(tt): ftmpbb_d})
         # Md/temp;
         tree_spec_dust.update({'Mdust': Mdust_temp})
-
-        #plt.xscale('log')
-        #plt.xlim(1000,15000)
-        #plt.show()
-        #hoge
         tree.update({'spec_dust' : tree_spec_dust})
         tree.update({'spec_dust_full' : tree_spec_dust_full})
         print('dust updated.')
 
     # Save;
     af = asdf.AsdfFile(tree)
-    af.write_to(DIR_TMP + 'spec_all_' + ID + '.asdf', all_array_compression='zlib')
-
-    # Re-register
-    MB.af = af
+    af.write_to(DIR_TMP + 'spec_all_' + MB.ID + '.asdf', all_array_compression='zlib')
+    # Loading the redshifted template;
+    MB.af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
 
     ##########################################
     # For observation.
     # Write out for the Multi-component fitting.
     ##########################################
-    MB.data = {}
-    file_tmp = 'tmp_library.txt'
-    fw = open(file_tmp,'w')#DIR_TMP + 'spec_obs_' + ID + '.cat', 'w')
-    fw.write('# BB data (>%d) in this file are not used in fitting.\n'%(ncolbb))
-    for ii in range(len(lm)):
-        g_offset = 1000 * fgrs[ii]
-        if lm[ii]/(1.+zbest) > lamliml and lm[ii]/(1.+zbest) < lamlimu:
-            fw.write('%d %.5f %.5e %.5e\n'%(ii+g_offset, lm[ii], fobs[ii], eobs[ii]))
-        else:
-            fw.write('%d %.5f 0 1000\n'%(ii+g_offset, lm[ii]))
-
-    for ii in range(len(ltmpbb[0,:])):
-        if SFILT[ii] in SKIPFILT:# data point to be skiped;
-            fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii]))
-        elif  ebb[ii]>ebblim:
-            fw.write('%d %.5f 0 1000\n'%(ii+ncolbb, ltmpbb[0,ii]))
-        else:
-            fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii]))
-    fw.close()
-
-    # register;
-    dat = ascii.read(file_tmp, format='no_header')
-    NR = dat['col1']
-    x = dat['col2']
-    fy00 = dat['col3']
-    ey00 = dat['col4']
-    dict_spec_obs = {'NR':NR, 'x':x, 'fy':fy00, 'ey':ey00}
-    MB.data['spec_obs'] = dict_spec_obs
-
-    # fw = open(DIR_TMP + 'spec_dust_obs_' + ID + '.cat', 'w')
-    fw = open(file_tmp,'w')#DIR_TMP + 'spec_obs_' + ID + '.cat', 'w')
-    if MB.f_dust:
-        nbblast = len(ltmpbb[0,:])+len(lm)
-        for ii in range(len(ebb_d[:])):
-            if ebb_d[ii]>ebblim:
-                fw.write('%d %.5f 0 1000\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast]))
+    if True:
+        MB.data = {}
+        file_tmp = 'tmp_library_%s.txt'%MB.ID
+        file_tmp2 = 'tmp_library2%s.txt'%MB.ID
+        fw = open(file_tmp,'w')#
+        fw.write('# BB data (>%d) in this file are not used in fitting.\n'%(ncolbb))
+        for ii in range(len(lm)):
+            g_offset = 1000 * fgrs[ii]
+            if lm[ii]/(1.+zbest) > lamliml and lm[ii]/(1.+zbest) < lamlimu:
+                fw.write('%d %.5f %.5e %.5e\n'%(ii+g_offset, lm[ii], fobs[ii], eobs[ii]))
             else:
-                fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii]))
-    fw.close()
-    if MB.f_dust:
-        dat = ascii.read(file_tmp, format='no_header')
-        nr_d = dat['col1']
-        x_d = dat['col2']
-        fy_d = dat['col3']
-        ey_d = dat['col4']
-        dict_spec_fir_obs = {'NR':nr_d, 'x':x_d, 'fy':fy_d, 'ey':ey_d}
-        MB.data['spec_fir_obs'] = dict_spec_fir_obs
+                fw.write('%d %.5f 0 1000\n'%(ii+g_offset, lm[ii]))
 
-    # BB phot
-    fw = open(file_tmp,'w')#DIR_TMP + 'spec_obs_' + ID + '.cat', 'w')
-    # fw = open(DIR_TMP + 'bb_obs_' + ID + '.cat', 'w')
-    file_tmp2 = 'tmp_library2.txt'
-    fw_rem = open(file_tmp2, 'w')
-    # fw_rem = open(DIR_TMP + 'bb_obs_' + ID + '_removed.cat', 'w')
-    for ii in range(len(ltmpbb[0,:])):
-        if SFILT[ii] in SKIPFILT:# data point to be skiped;
-            fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii], FWFILT[ii]/2., SFILT[ii]))
-            fw_rem.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
-        elif ebb[ii]>ebblim:
-            fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
-        elif ebb[ii]<=0:
-            fw.write('%d %.5f 0 -99 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
-        else:
-            fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
-    fw.close()
-    fw_rem.close()
-    # register;
-    dat = ascii.read(file_tmp, format='no_header')
-    NRbb = dat['col1']
-    xbb  = dat['col2']
-    fybb = dat['col3']
-    eybb = dat['col4']
-    exbb = dat['col5']
-    dict_bb_obs = {'NR':NRbb, 'x':xbb, 'fy':fybb, 'ey':eybb, 'ex':exbb}
-    MB.data['bb_obs'] = dict_bb_obs
-    try:
-        dat = ascii.read(file_tmp2, format='no_header')
-        x_ex = dat['col2']
-        fy_ex = dat['col3']
-        ey_ex = dat['col4']
-        ex_ex = dat['col5']
-        dict_bb_obs_removed = {'NR':NR_ex, 'x':x_ex, 'fy':fy_ex, 'ey':ey_ex, 'ex':ex_ex}
-        MB.data['bb_obs_removed'] = dict_bb_obs_removed
-    except:
-        pass
+        for ii in range(len(ltmpbb[0,:])):
+            if SFILT[ii] in SKIPFILT:# data point to be skiped;
+                fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii]))
+            elif  ebb[ii]>ebblim:
+                fw.write('%d %.5f 0 1000\n'%(ii+ncolbb, ltmpbb[0,ii]))
+            else:
+                fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii]))
+        fw.close()
 
-    # Dust; Not sure where this is being used...
-    fw = open(file_tmp,'w')
-    # fw = open(DIR_TMP + 'bb_dust_obs_' + ID + '.cat', 'w')
-    if MB.f_dust:
-        for ii in range(len(ebb_d[:])):
-            if ebb_d[ii]>ebblim:
-                fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], DFWFILT[ii]/2., DFILT[ii]))
+        # register;
+        dat = ascii.read(file_tmp, format='no_header')
+        NR = dat['col1']
+        x = dat['col2']
+        fy00 = dat['col3']
+        ey00 = dat['col4']
+        dict_spec_obs = {'NR':NR, 'x':x, 'fy':fy00, 'ey':ey00}
+        MB.data['spec_obs'] = dict_spec_obs
+
+        fw = open(file_tmp,'w')
+        if MB.f_dust:
+            nbblast = len(ltmpbb[0,:])+len(lm)
+            for ii in range(len(ebb_d[:])):
+                if ebb_d[ii]>ebblim:
+                    fw.write('%d %.5f 0 1000\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast]))
+                else:
+                    fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii]))
+        fw.close()
+        if MB.f_dust:
+            dat = ascii.read(file_tmp, format='no_header')
+            nr_d = dat['col1']
+            x_d = dat['col2']
+            fy_d = dat['col3']
+            ey_d = dat['col4']
+            dict_spec_fir_obs = {'NR':nr_d, 'x':x_d, 'fy':fy_d, 'ey':ey_d}
+            MB.data['spec_fir_obs'] = dict_spec_fir_obs
+
+        # BB phot
+        fw = open(file_tmp,'w')
+        fw_rem = open(file_tmp2, 'w')
+        for ii in range(len(ltmpbb[0,:])):
+            if SFILT[ii] in SKIPFILT:# data point to be skiped;
+                fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii], FWFILT[ii]/2., SFILT[ii]))
+                fw_rem.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
+            elif ebb[ii]>ebblim:
+                fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
+            elif ebb[ii]<=0:
+                fw.write('%d %.5f 0 -99 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
             else:
-                fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii], DFWFILT[ii]/2., DFILT[ii]))
-    fw.close()
-    if MB.f_dust:
+                fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
+        fw.close()
+        fw_rem.close()
+        # register;
         dat = ascii.read(file_tmp, format='no_header')
-        nr_bb_d = dat['col1']
-        x_bb_d = dat['col2']
-        fy_bb_d = dat['col3']
-        ey_bb_d = dat['col4']
-        dict_bb_fir_obs = {'NR':nr_bb_d, 'x':x_bb_d, 'fy':fy_bb_d, 'ey':ey_bb_d}
-        MB.data['bb_fir_obs'] = dict_bb_fir_obs
-
-    print('Done making templates at z=%.2f.\n'%zbest)
-    os.system('rm %s %s'%(file_tmp, file_tmp2))
+        NRbb = dat['col1']
+        xbb  = dat['col2']
+        fybb = dat['col3']
+        eybb = dat['col4']
+        exbb = dat['col5']
+        dict_bb_obs = {'NR':NRbb, 'x':xbb, 'fy':fybb, 'ey':eybb, 'ex':exbb}
+        MB.data['bb_obs'] = dict_bb_obs
+        try:
+            dat = ascii.read(file_tmp2, format='no_header')
+            NR_ex = dat['col1']
+            x_ex = dat['col2']
+            fy_ex = dat['col3']
+            ey_ex = dat['col4']
+            ex_ex = dat['col5']
+            dict_bb_obs_removed = {'NR':NR_ex, 'x':x_ex, 'fy':fy_ex, 'ey':ey_ex, 'ex':ex_ex}
+            MB.data['bb_obs_removed'] = dict_bb_obs_removed
+        except:
+            pass
 
-    # Loading template;
-    MB.af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
-    MB.af0 = asdf.open(MB.DIR_TMP + 'spec_all.asdf')
+        # Dust; Not sure where this is being used...
+        fw = open(file_tmp,'w')
+        if MB.f_dust:
+            for ii in range(len(ebb_d[:])):
+                if ebb_d[ii]>ebblim:
+                    fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], DFWFILT[ii]/2., DFILT[ii]))
+                else:
+                    fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii], DFWFILT[ii]/2., DFILT[ii]))
+        fw.close()
+        if MB.f_dust:
+            dat = ascii.read(file_tmp, format='no_header')
+            nr_bb_d = dat['col1']
+            x_bb_d = dat['col2']
+            fy_bb_d = dat['col3']
+            ey_bb_d = dat['col4']
+            dict_bb_fir_obs = {'NR':nr_bb_d, 'x':x_bb_d, 'fy':fy_bb_d, 'ey':ey_bb_d}
+            MB.data['bb_fir_obs'] = dict_bb_fir_obs
+
+        print('Done making templates at z=%.2f.\n'%zbest)
+        os.system('rm %s %s'%(file_tmp, file_tmp2))
 
     return True
 
@@ -1096,9 +1091,9 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
                 print('File, %s/%s, cannot be open.'%(DIR_EXTR,spec_file))
                 pass
         # Constructing arrays.
-        lm = np.zeros(np.sum(ninp0[:]),dtype='float')
-        fobs = np.zeros(np.sum(ninp0[:]),dtype='float')
-        eobs = np.zeros(np.sum(ninp0[:]),dtype='float')
+        lm = np.zeros(np.sum(ninp0[:]),dtype=float)
+        fobs = np.zeros(np.sum(ninp0[:]),dtype=float)
+        eobs = np.zeros(np.sum(ninp0[:]),dtype=float)
         fgrs = np.zeros(np.sum(ninp0[:]),dtype='int')  # FLAG for each grism.
         for ff, spec_file in enumerate(spec_files):
             try:
@@ -1141,8 +1136,8 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
             print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(ID))
             return False
 
-        fbb = np.zeros(len(SFILT), dtype='float')
-        ebb = np.zeros(len(SFILT), dtype='float')
+        fbb = np.zeros(len(SFILT), dtype=float)
+        ebb = np.zeros(len(SFILT), dtype=float)
 
         for ii in range(len(SFILT)):
             fbb[ii] = fd0['F%s'%(SFILT[ii])][ii0]
@@ -1176,8 +1171,8 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
             ebb = ebb0
 
     else:
-        fbb = np.zeros(len(SFILT), dtype='float')
-        ebb = np.zeros(len(SFILT), dtype='float')
+        fbb = np.zeros(len(SFILT), dtype=float)
+        ebb = np.zeros(len(SFILT), dtype=float)
         for ii in range(len(SFILT)):
             fbb[ii] = 0
             ebb[ii] = -99
@@ -1193,8 +1188,8 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
             print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(ID))
             return False
 
-        fbb_d = np.zeros(len(DFILT), dtype='float')
-        ebb_d = np.zeros(len(DFILT), dtype='float')
+        fbb_d = np.zeros(len(DFILT), dtype=float)
+        ebb_d = np.zeros(len(DFILT), dtype=float)
         for ii in range(len(DFILT)):
             fbb_d[ii] = fdd['F%s'%(DFILT[ii])][ii0]
             ebb_d[ii] = fdd['E%s'%(DFILT[ii])][ii0]
@@ -1229,26 +1224,26 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
                     lm0 = lm0[(lm0 * (zbest+1) < lammax)]
                 wave = lm0
 
-            lmbest = np.zeros((Ntmp, len(lm0)), dtype='float')
-            fbest = np.zeros((Ntmp, len(lm0)), dtype='float')
-            lmbestbb = np.zeros((Ntmp, len(SFILT)), dtype='float')
-            fbestbb = np.zeros((Ntmp, len(SFILT)), dtype='float')
+            lmbest = np.zeros((Ntmp, len(lm0)), dtype=float)
+            fbest = np.zeros((Ntmp, len(lm0)), dtype=float)
+            lmbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
+            fbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
 
-            spec_mul = np.zeros((Na, len(lm0)), dtype='float')
-            spec_mul_nu = np.zeros((Na, len(lm0)), dtype='float')
-            spec_mul_nu_conv = np.zeros((Na, len(lm0)), dtype='float')
+            spec_mul = np.zeros((Na, len(lm0)), dtype=float)
+            spec_mul_nu = np.zeros((Na, len(lm0)), dtype=float)
+            spec_mul_nu_conv = np.zeros((Na, len(lm0)), dtype=float)
 
-            ftmpbb = np.zeros((Na, len(SFILT)), dtype='float')
-            ltmpbb = np.zeros((Na, len(SFILT)), dtype='float')
+            ftmpbb = np.zeros((Na, len(SFILT)), dtype=float)
+            ltmpbb = np.zeros((Na, len(SFILT)), dtype=float)
 
-            ftmp_nu_int = np.zeros((Na, len(lm)), dtype='float')
-            spec_av_tmp = np.zeros((Na, len(lm)), dtype='float')
+            ftmp_nu_int = np.zeros((Na, len(lm)), dtype=float)
+            spec_av_tmp = np.zeros((Na, len(lm)), dtype=float)
 
-            ms = np.zeros(Na, dtype='float')
-            Ls = np.zeros(Na, dtype='float')
+            ms = np.zeros(Na, dtype=float)
+            Ls = np.zeros(Na, dtype=float)
             ms[:] = mshdu['ms_'+str(zz)+'_'+str(tt)][:] # [:] is necessary.
             Ls[:] = mshdu['Ls_'+str(zz)+'_'+str(tt)][:]
-            Fuv = np.zeros(Na, dtype='float')
+            Fuv = np.zeros(Na, dtype=float)
 
             for ss in range(Na):
                 #print(ss,tt,zz)
diff --git a/gsf/writing.py b/gsf/writing.py
index 2acfa6c..bd271c3 100644
--- a/gsf/writing.py
+++ b/gsf/writing.py
@@ -154,12 +154,6 @@ def get_param(self, res, fitc, tcalc=1., burnin=-1):
     ############
     # Get SN.
     ############
-    # file = DIR_TMP + 'spec_obs_' + ID0 + '.cat'
-    # fds  = np.loadtxt(file, comments='#')
-    # nrs  = fds[:,0]
-    # lams = fds[:,1]
-    # fsp  = fds[:,2]
-    # esp  = fds[:,3]
     nrs, lams, fsp, esp = self.data['spec_obs']['NR'], self.data['spec_obs']['x'], self.data['spec_obs']['fy'], self.data['spec_obs']['ey']
 
     consp = (nrs<10000) & (lams/(1.+zrecom)>3600) & (lams/(1.+zrecom)<4200) & (esp>0)

From 08e7bb187208046507123103ee1f562dde3a9dd5 Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Fri, 9 Dec 2022 22:47:37 -0800
Subject: [PATCH 03/14] clean

---
 gsf/function.py       |  20 ++
 gsf/function_class.py | 497 +++++++++++++++++-------------------------
 gsf/gsf.py            |   2 +-
 gsf/maketmp_filt.py   |   2 -
 gsf/maketmp_z0.py     |   6 +-
 gsf/plot_sfh.py       |   4 +-
 6 files changed, 225 insertions(+), 306 deletions(-)

diff --git a/gsf/function.py b/gsf/function.py
index fef73ea..2dff9f3 100644
--- a/gsf/function.py
+++ b/gsf/function.py
@@ -654,6 +654,26 @@ def savecpkl(data, cpklfile, verbose=True):
     f.close()
 
 
+def apply_dust(yy, xx, nr, Av, dust_model=0):
+	'''
+	xx : float array
+		RF Wavelength
+	'''
+	if dust_model == 0:
+		yyd, xxd, nrd = dust_calz(xx, yy, Av, nr)
+	elif dust_model == 1:
+		yyd, xxd, nrd = dust_mw(xx, yy, Av, nr)
+	elif dust_model == 2: # LMC
+		yyd, xxd, nrd = dust_gen(xx, yy, Av, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
+	elif dust_model == 3: # SMC
+		yyd, xxd, nrd = dust_gen(xx, yy, Av, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
+	elif dust_model == 4: # Kriek&Conroy with gamma=-0.2
+		yyd, xxd, nrd = dust_kc(xx, yy, Av, nr, Rv=4.05, gamma=-0.2)
+	else:
+		yyd, xxd, nrd = dust_calz(xx, yy, Av, nr)
+	return yyd, xxd, nrd
+
+
 def dust_gen(lm, fl, Av, nr, Rv=4.05, gamma=-0.05, Eb=3.0, lmlimu=3.115, lmv=5000/10000, f_Alam=False):
     '''
     For general purpose (Noll+09).
diff --git a/gsf/function_class.py b/gsf/function_class.py
index 8e4aa9b..c87c183 100644
--- a/gsf/function_class.py
+++ b/gsf/function_class.py
@@ -169,7 +169,7 @@ def open_spec_neb_fits(self, fall:int = 0, orig:bool = False):
         return lib
 
 
-    def open_spec_fits_dir(self, nage:int, nz:int, kk, Av00:float, zgal:float, A00:float, f_IGM=True):
+    def open_spec_fits_dir(self, nage:int, nz:int, kk, Av:float, zgal:float, A00:float, f_IGM=True):
         '''
         Load template in obs range.
         But for weird template.
@@ -205,19 +205,7 @@ def open_spec_fits_dir(self, nage:int, nz:int, kk, Av00:float, zgal:float, A00:f
         yy = flamtonu(xx, yy0, m0set=self.MB.m0set)
         lib[:,2] = yy[:]
 
-        if self.dust_model == 0: # Calzetti
-            yyd, xxd, nrd = dust_calz(xx, yy, Av00, nr)
-        elif self.dust_model == 1: # MW
-            yyd, xxd, nrd = dust_mw(xx, yy, Av00, nr)
-        elif self.dust_model == 2: # LMC
-            yyd, xxd, nrd = dust_gen(xx, yy, Av00, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
-        elif self.dust_model == 3: # SMC
-            yyd, xxd, nrd = dust_gen(xx, yy, Av00, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
-        elif self.dust_model == 4: # Kriek&Conroy with gamma=-0.2
-            yyd, xxd, nrd = dust_kc(xx, yy, Av00, nr, Rv=4.05, gamma=-0.2)
-        else:
-            print('No valid entry. Dust model is set to Calzetti')
-            yyd, xxd, nrd = dust_calz(xx, yy, Av00, nr)
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
 
         xxd *= (1.+zgal)
         nrd_yyd = np.zeros((len(nrd),3), dtype='float')
@@ -282,39 +270,26 @@ def get_template(self, lib, Amp:float = 1.0, T:float = 1.0, Av:float = 0.0, Z:fl
         xx = lib[:, 1] # This is OBSERVED wavelength range at z=zgal
         yy = lib[:, coln]
 
-        if self.dust_model == 0:
-            yyd, xxd, nrd = dust_calz(xx/(1.+zgal), yy, Av, nr)
-        elif self.dust_model == 1:
-            yyd, xxd, nrd = dust_mw(xx/(1.+zgal), yy, Av, nr)
-        elif self.dust_model == 2: # LMC
-            yyd, xxd, nrd = dust_gen(xx/(1.+zgal), yy, Av, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
-        elif self.dust_model == 3: # SMC
-            yyd, xxd, nrd = dust_gen(xx/(1.+zgal), yy, Av, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
-        elif self.dust_model == 4: # Kriek&Conroy with gamma=-0.2
-            yyd, xxd, nrd = dust_kc(xx/(1.+zgal), yy, Av, nr, Rv=4.05, gamma=-0.2)
-        else:
-            yyd, xxd, nrd = dust_calz(xx/(1.+zgal), yy, Av, nr)
-
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
         xxd *= (1.+zgal)
 
-        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-        nrd_yyd[:,0] = nrd[:]
-        nrd_yyd[:,1] = yyd[:]
-        nrd_yyd[:,2] = xxd[:]
-
-        b = nrd_yyd
-        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-        yyd_sort = nrd_yyd_sort[:,1]
-        xxd_sort = nrd_yyd_sort[:,2]
+        if self.dust_model != 0:
+            # This may be needed when not calzetti model
+            nrd_yyd = np.zeros((len(nrd),3), dtype=float)
+            nrd_yyd[:,0] = nrd[:]
+            nrd_yyd[:,1] = yyd[:]
+            nrd_yyd[:,2] = xxd[:]
+            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
+            nrd[:],yyd[:],xxd[:] = nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
 
         if f_bb:
-            fil_cen, fil_flux = filconv_fast(self.MB, xxd_sort, Amp * yyd_sort)
-            return Amp * yyd_sort, xxd_sort, fil_flux, fil_cen
+            fil_cen, fil_flux = filconv_fast(self.MB, xxd, Amp * yyd)
+            return Amp * yyd, xxd, fil_flux, fil_cen
         else:
-            return Amp * yyd_sort, xxd_sort
+            return Amp * yyd, xxd
 
 
-    def tmp03(self, A00, Av00, nmodel, Z, zgal, lib):
+    def tmp03(self, A00, Av, nmodel, Z, zgal, lib):
         '''
         '''
         tau0= self.tau0 #[0.01,0.02,0.03]
@@ -334,19 +309,7 @@ def tmp03(self, A00, Av00, nmodel, Z, zgal, lib):
         xx  = lib[:,1] # This is OBSERVED wavelength range at z=zgal
         yy  = lib[:,coln]
 
-        if self.dust_model == 0:
-            yyd, xxd, nrd = dust_calz(xx/(1.+zgal), yy, Av00, nr)
-        elif self.dust_model == 1:
-            yyd, xxd, nrd = dust_mw(xx/(1.+zgal), yy, Av00, nr)
-        elif self.dust_model == 2: # LMC
-            yyd, xxd, nrd = dust_gen(xx/(1.+zgal), yy, Av00, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
-        elif self.dust_model == 3: # SMC
-            yyd, xxd, nrd = dust_gen(xx/(1.+zgal), yy, Av00, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
-        elif self.dust_model == 4: # Kriek&Conroy with gamma=-0.2
-            yyd, xxd, nrd = dust_kc(xx/(1.+zgal), yy, Av00, nr, Rv=4.05, gamma=-0.2)
-        else:
-            yyd, xxd, nrd = dust_calz(xx/(1.+zgal), yy, Av00, nr)
-
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
 
         xxd *= (1.+zgal)
 
@@ -363,7 +326,7 @@ def tmp03(self, A00, Av00, nmodel, Z, zgal, lib):
         return A00 * yyd_sort, xxd_sort
 
 
-    def tmp03_neb(self, A00, Av00, logU, nmodel, Z, zgal, lib, f_apply_dust=True, EBVratio=2.27):
+    def tmp03_neb(self, A00, Av, logU, nmodel, Z, zgal, lib, f_apply_dust=True, EBVratio=2.27):
         '''
         EBVratio : float
             E(B-V)_neb / E(B-V)_st. 
@@ -378,7 +341,7 @@ def tmp03_neb(self, A00, Av00, logU, nmodel, Z, zgal, lib, f_apply_dust=True, EB
         NU = len(self.MB.logUs)
 
         # Dust attenuation to nebulae
-        Av00 *= EBVratio
+        Av *= EBVratio
 
         pp0 = np.random.uniform(low=0, high=len(tau0), size=(1,))
         pp = int(pp0[0])
@@ -392,18 +355,7 @@ def tmp03_neb(self, A00, Av00, logU, nmodel, Z, zgal, lib, f_apply_dust=True, EB
         yy = lib[:,coln]
 
         if f_apply_dust:
-            if self.dust_model == 0:
-                yyd, xxd, nrd = dust_calz(xx/(1.+zgal), yy, Av00, nr)
-            elif self.dust_model == 1:
-                yyd, xxd, nrd = dust_mw(xx/(1.+zgal), yy, Av00, nr)
-            elif self.dust_model == 2: # LMC
-                yyd, xxd, nrd = dust_gen(xx/(1.+zgal), yy, Av00, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
-            elif self.dust_model == 3: # SMC
-                yyd, xxd, nrd = dust_gen(xx/(1.+zgal), yy, Av00, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
-            elif self.dust_model == 4: # Kriek&Conroy with gamma=-0.2
-                yyd, xxd, nrd = dust_kc(xx/(1.+zgal), yy, Av00, nr, Rv=4.05, gamma=-0.2)
-            else:
-                yyd, xxd, nrd = dust_calz(xx/(1.+zgal), yy, Av00, nr)
+            yyd, xxd, nrd = apply_dust(yy, xx/(1+zgal), nr, Av, dust_model=self.dust_model)
         else:
             yyd, xxd, nrd = yy, xx, nr
 
@@ -422,44 +374,33 @@ def tmp03_neb(self, A00, Av00, logU, nmodel, Z, zgal, lib, f_apply_dust=True, EB
         return A00 * yyd_sort, xxd_sort
 
 
-    def tmp04(self, par, f_Alog:bool=True, nprec:int=1, pp:int = 0, f_val:bool=False, lib_all:bool=False, f_nrd:bool=False,
-        f_IGM=True):
+    def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None):
         '''
-        Makes model template with a given param set.
-        Also dust attenuation.
+        Purpose
+        -------
+        get total flux for a given set of parameter.
 
         Parameters
         ----------
-        nprec : int
-            Precision when redshift is refined. 
+        par : library
+            contains parameters
+
+        Returns
+        -------
+        xx : 
+            OBSERVED wavelength range at z=MB.zgal
         '''
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
         Mtot:float = 0
-
-        if f_val:
-            par = par.params
-
-        if self.MB.fzmc == 1:
-            try:
-                zmc = par['zmc'].value
-            except:
-                zmc = self.MB.zgal
-        else:
-            zmc = self.MB.zgal
-
-        # AV limit;
-        if par['Av'] < self.MB.Avmin:
-            par['Av'] = self.MB.Avmin
-        if par['Av'] > self.MB.Avmax:
-            par['Av'] = self.MB.Avmax
-        Av00 = par['Av']
+        if lib == None:
+            if lib_all:
+                lib = self.MB.lib_all
+            else:
+                lib = self.MB.lib
 
         for aa in range(self.MB.npeak):
             if self.MB.ZEVOL==1 or aa == 0:
                 Z = par['Z'+str(aa)]
-                NZ = bfnc.Z2NZ(Z)
+                NZ = self.MB.bfnc.Z2NZ(Z)
 
             # Check limit;
             if par['A'+str(aa)] < self.MB.Amin:
@@ -479,71 +420,143 @@ def tmp04(self, par, f_Alog:bool=True, nprec:int=1, pp:int = 0, f_val:bool=False
             else:
                 A00 = par['A'+str(aa)]
 
-            coln = int(2 + pp*len(ZZ)*len(AA) + NZ*len(AA) + aa)
+            coln = int(2 + pp*len(self.ZZ)*len(self.AA) + NZ*len(self.AA) + aa)
 
-            sedpar = self.MB.af['ML'] # For M/L
-            mslist = sedpar['ML_'+str(NZ)][aa]
+            mslist = self.MB.af['ML']['ML_'+str(NZ)][aa]
             Mtot += 10**(par['A%d'%aa] + np.log10(mslist))
 
+            if aa == 0:
+                nr = lib[:,0]
+                xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
+                yy = A00 * lib[:,coln]
+            else:
+                yy += A00 * lib[:,coln]
+
+        return nr, xx, yy, Mtot
+
+
+    def get_total_flux_neb(self, par, f_Alog=True, lib_all=True, lib=None):
+        '''
+        Purpose
+        -------
+        get total flux for a given set of parameter.
+
+        Parameters
+        ----------
+        par : library
+            contains parameters
+        '''
+        if lib == None:
             if lib_all:
-                if aa == 0:
-                    nr = self.MB.lib_all[:,0]
-                    xx = self.MB.lib_all[:,1] # This is OBSERVED wavelength range at z=zgal
-                    yy = A00 * self.MB.lib_all[:,coln]
-                else:
-                    yy += A00 * self.MB.lib_all[:,coln]
+                lib = self.MB.lib_neb_all
             else:
-                if aa == 0:
-                    nr = self.MB.lib[:,0]
-                    xx = self.MB.lib[:,1] # This is OBSERVED wavelength range at z=zgal
-                    yy = A00 * self.MB.lib[:,coln]
-                else:
-                    yy += A00 * self.MB.lib[:,coln]
-        
+                lib = self.MB.lib_neb
+
+        aa = 0
+        if self.MB.ZEVOL==1 or aa == 0:
+            Z = par['Z'+str(aa)]
+            NZ = self.MB.bfnc.Z2NZ(Z)
+
+        try:
+            Aneb = par['Aneb']
+            logU = par['logU']
+            nlogU = np.argmin(np.abs(self.MB.logUs - logU))
+        except: # This is exception for initial minimizing;
+            Aneb = -99
+            logU = self.MB.logUs[0]
+            nlogU = 0
+
+        # logU
+        NU = self.MB.nlogU
+
+        # Check limit;
+        if Aneb < self.MB.Amin:
+            Aneb = self.MB.Amin
+        if Aneb > self.MB.Amax:
+            Aneb = self.MB.Amax
+        # Z limit:
+        if aa == 0 or self.MB.ZEVOL == 1:
+            if par['Z%d'%aa] < self.MB.Zmin:
+                par['Z%d'%aa] = self.MB.Zmin
+            if par['Z%d'%aa] > self.MB.Zmax:
+                par['Z%d'%aa] = self.MB.Zmax
+
+        # Is A in logspace?
+        if f_Alog:
+            A00 = 10**Aneb
+        else:
+            A00 = Aneb
+
+        coln = int(2 + NZ*NU + nlogU)
+
+        if aa == 0:
+            nr = lib[:,0]
+            xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
+            yy = A00 * lib[:,coln]
+        else:
+            yy += A00 * lib[:,coln]
+
+        return nr, xx, yy
+
+
+    def tmp04(self, par, f_Alog:bool=True, nprec:int=1, pp:int = 0, f_val:bool=False, lib_all:bool=False, f_nrd:bool=False,
+        f_IGM=True, deltaz_lim=99):
+        '''
+        Makes model template with a given param set.
+        Also dust attenuation.
+
+        Parameters
+        ----------
+        nprec : int
+            Precision when redshift is refined. 
+        deltaz_lim : float
+            limit in np.abs(zmc - self.MB.zgal).
+        '''
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+
+        if f_val:
+            par = par.params
+
+        if self.MB.fzmc == 1:
+            try:
+                zmc = par['zmc'].value
+            except:
+                zmc = self.MB.zgal
+        else:
+            zmc = self.MB.zgal
+
+        # AV limit;
+        if par['Av'] < self.MB.Avmin:
+            par['Av'] = self.MB.Avmin
+        if par['Av'] > self.MB.Avmax:
+            par['Av'] = self.MB.Avmax
+
+        nr, xx, yy, Mtot = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp)
         self.MB.logMtmp = np.log10(Mtot)
 
         # @@@ Filter convolution may need to happpen here
         if round(zmc,nprec) != round(self.MB.zgal,nprec):
-            if True:
-                fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
-                xx_s = xx / (1+self.MB.zgal) * (1+zmc)
-                yy_s = fint(xx_s)
-            else:
+            if np.abs(zmc - self.MB.zgal) > deltaz_lim:
+                print('!!! zmc (%.3f) is exploring too far from zgal (%.3f).'%(zmc, self.MB.zgal))
                 # This takes too much time;
-                # self.MB.zgal = zmc
-                # if self.MB.SFH_FORM == -99:
-                #     flag_suc = maketemp(self.MB, tau_lim=self.MB.tau_lim, nthin=self.MB.nthin, delwave=self.MB.delwave)
-                # else:
-                #     flag_suc = maketemp_tau(self.MB, tau_lim=self.MB.tau_lim, nthin=self.MB.nthin, delwave=self.MB.delwave)
-
-                # # Load Spectral library;
-                # self.lib = self.open_spec_fits(fall=0)
-                # self.lib_all = self.open_spec_fits(fall=1, orig=True)
-
-                # if self.MB.f_dust:
-                #     self.lib_dust = self.open_spec_dust_fits(fall=0)
-                #     self.lib_dust_all = self.open_spec_dust_fits(fall=1)
-                # if self.MB.fneb:
-                #     self.lib_neb = self.open_spec_neb_fits(fall=0)
-                #     self.lib_neb_all = self.open_spec_neb_fits(fall=1)
-
-                # if lib_all:
-                #     if aa == 0:
-                #         nr = self.MB.lib_all[:,0]
-                #         xx = self.MB.lib_all[:,1] # This is OBSERVED wavelength range at z=zgal
-                #         yy = A00 * self.MB.lib_all[:,coln]
-                #     else:
-                #         yy += A00 * self.MB.lib_all[:,coln]
-                # else:
-                #     if aa == 0:
-                #         nr = self.MB.lib[:,0]
-                #         xx = self.MB.lib[:,1] # This is OBSERVED wavelength range at z=zgal
-                #         yy = A00 * self.MB.lib[:,coln]
-                #     else:
-                #         yy += A00 * self.MB.lib[:,coln]
-
+                self.MB.zgal = zmc
+                if self.MB.SFH_FORM == -99:
+                    flag_suc = maketemp(self.MB, tau_lim=self.MB.tau_lim, nthin=self.MB.nthin, delwave=self.MB.delwave)
+                else:
+                    flag_suc = maketemp_tau(self.MB, tau_lim=self.MB.tau_lim, nthin=self.MB.nthin, delwave=self.MB.delwave)
+                # Load Spectral library;
+                self.lib = self.open_spec_fits(fall=0)
+                self.lib_all = self.open_spec_fits(fall=1, orig=True)
+                # Get total flux;
+                nr, xx, yy, Mtot = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp)
                 xx_s = xx
                 yy_s = yy
+            else:
+                fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
+                xx_s = xx / (1+self.MB.zgal) * (1+zmc)
+                yy_s = fint(xx_s)
 
         else:
             xx_s = xx
@@ -552,18 +565,8 @@ def tmp04(self, par, f_Alog:bool=True, nprec:int=1, pp:int = 0, f_val:bool=False
         xx = xx_s
         yy = yy_s
 
-        if self.dust_model == 0:
-            yyd, xxd, nrd = dust_calz(xx/(1.+zmc), yy, Av00, nr)
-        elif self.dust_model == 1:
-            yyd, xxd, nrd = dust_mw(xx/(1.+zmc), yy, Av00, nr)
-        elif self.dust_model == 2: # LMC
-            yyd, xxd, nrd = dust_gen(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
-        elif self.dust_model == 3: # SMC
-            yyd, xxd, nrd = dust_gen(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
-        elif self.dust_model == 4: # Kriek&Conroy with gamma=-0.2
-            yyd, xxd, nrd = dust_kc(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.2)
-        else:
-            yyd, xxd, nrd = dust_calz(xx/(1.+zmc), yy, Av00, nr)
+        # Apply dust;
+        yyd, xxd, nrd = apply_dust(yy, xx/(1+zmc), nr, par['Av'], dust_model=self.dust_model)
 
         xxd *= (1.+zmc)
         if self.dust_model != 0:
@@ -616,63 +619,15 @@ def tmp04_neb(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib_al
             par['Av'] = self.MB.Avmin
         if par['Av'] > self.MB.Avmax:
             par['Av'] = self.MB.Avmax
-        Av00 = par['Av']
+        Av = par['Av']
 
         # Dust attenuation to nebulae
-        Av00 *= EBVratio
+        Av *= EBVratio
 
-        aa = 0
-        if self.MB.ZEVOL==1 or aa == 0:
-            Z = par['Z'+str(aa)]
-            NZ = bfnc.Z2NZ(Z)
+        # Get total flux;
+        nr, xx, yy = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=lib_all)
 
-        try:
-            Aneb = par['Aneb']
-            logU = par['logU']
-            nlogU = np.argmin(np.abs(self.MB.logUs - logU))
-        except: # This is exception for initial minimizing;
-            Aneb = -99
-            logU = self.MB.logUs[0]
-            nlogU = 0
-
-        # logU
-        NU = self.MB.nlogU
-
-        # Check limit;
-        if Aneb < self.MB.Amin:
-            Aneb = self.MB.Amin
-        if Aneb > self.MB.Amax:
-            Aneb = self.MB.Amax
-        # Z limit:
-        if aa == 0 or self.MB.ZEVOL == 1:
-            if par['Z%d'%aa] < self.MB.Zmin:
-                par['Z%d'%aa] = self.MB.Zmin
-            if par['Z%d'%aa] > self.MB.Zmax:
-                par['Z%d'%aa] = self.MB.Zmax
-
-        # Is A in logspace?
-        if f_Alog:
-            A00 = 10**Aneb
-        else:
-            A00 = Aneb
-
-        coln = int(2 + NZ*NU + nlogU)
-
-        if lib_all:
-            if aa == 0:
-                nr = self.MB.lib_neb_all[:,0]
-                xx = self.MB.lib_neb_all[:,1] # This is OBSERVED wavelength range at z=zgal
-                yy = A00 * self.MB.lib_neb_all[:,coln]
-            else:
-                yy += A00 * self.MB.lib_neb_all[:,coln]
-        else:
-            if aa == 0:
-                nr = self.MB.lib_neb[:, 0]
-                xx = self.MB.lib_neb[:, 1] # This is OBSERVED wavelength range at z=zgal
-                yy = A00 * self.MB.lib_neb[:,coln]
-            else:
-                yy += A00 * self.MB.lib_neb[:,coln]
-        
+        # Redshift refinement;
         if round(zmc,nprec) != round(self.MB.zgal,nprec):
             xx_s = xx / (1+self.MB.zgal) * (1+zmc)
             fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
@@ -685,18 +640,7 @@ def tmp04_neb(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib_al
         yy = yy_s
 
         if f_apply_dust:
-            if self.dust_model == 0:
-                yyd, xxd, nrd = dust_calz(xx/(1.+zmc), yy, Av00, nr)
-            elif self.dust_model == 1:
-                yyd, xxd, nrd = dust_mw(xx/(1.+zmc), yy, Av00, nr)
-            elif self.dust_model == 2: # LMC
-                yyd, xxd, nrd = dust_gen(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
-            elif self.dust_model == 3: # SMC
-                yyd, xxd, nrd = dust_gen(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
-            elif self.dust_model == 4: # Kriek&Conroy with gamma=-0.2
-                yyd, xxd, nrd = dust_kc(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.2)
-            else:
-                yyd, xxd, nrd = dust_calz(xx/(1.+zmc), yy, Av00, nr)
+            yyd, xxd, nrd = apply_dust(yy, xx/(1+zmc), nr, Av, dust_model=self.dust_model)
             xxd *= (1.+zmc)
 
             if self.dust_model != 0:
@@ -840,8 +784,6 @@ def open_spec_dust_fits(self, fall:int = 0):
         '''
         ID0 = self.MB.ID
         tau0= self.MB.tau0
-
-        from astropy.io import fits
         ZZ = self.ZZ
         AA = self.AA
         bfnc = self.MB.bfnc #Basic(ZZ)
@@ -882,8 +824,6 @@ def open_spec_neb_fits(self, fall=0, orig=False):
         Loads template in obs range.
         '''
         ID0 = self.MB.ID
-
-        from astropy.io import fits
         ZZ = self.ZZ
         AA = self.AA
         bfnc = self.MB.bfnc
@@ -920,20 +860,16 @@ def open_spec_neb_fits(self, fall=0, orig=False):
         return lib
 
 
-    def open_spec_fits_dir(self, nage, nz, kk, Av00, zgal, A00):
+    def open_spec_fits_dir(self, nage, nz, kk, Av, zgal, A00):
         '''
         Loads template in obs range.
         But for weird template.
         '''
-        from astropy.io import fits
         tau0= self.tau0
         ZZ = self.ZZ
         AA = self.AA
         bfnc = self.MB.bfnc
 
-        # self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
-        # self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
-
         app = 'all'
         hdu0 = self.MB.af['spec_full']
         DIR_TMP = self.DIR_TMP
@@ -960,32 +896,22 @@ def open_spec_fits_dir(self, nage, nz, kk, Av00, zgal, A00):
         yy = flamtonu(xx, yy0, m0set=self.MB.m0set)
         lib[:,2] = yy[:]
 
-        if self.dust_model == 0: # Calzetti
-            yyd, xxd, nrd = dust_calz(xx, yy, Av00, nr)
-        elif self.dust_model == 1: # MW
-            yyd, xxd, nrd = dust_mw(xx, yy, Av00, nr)
-        elif self.dust_model == 2: # LMC
-            yyd, xxd, nrd = dust_gen(xx, yy, Av00, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
-        elif self.dust_model == 3: # SMC
-            yyd, xxd, nrd = dust_gen(xx, yy, Av00, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
-        elif self.dust_model == 4: # Kriek&Conroy with gamma=-0.2
-            yyd, xxd, nrd = dust_kc(xx, yy, Av00, nr, Rv=4.05, gamma=-0.2)
-        else:
-            print('No valid entry. Dust model is set to Calzetti')
-            yyd, xxd, nrd = dust_calz(xx, yy, Av00, nr)
-
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
         xxd *= (1.+zgal)
-        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-        nrd_yyd[:,0] = nrd[:]
-        nrd_yyd[:,1] = yyd[:]
-        nrd_yyd[:,2] = xxd[:]
 
-        b = nrd_yyd
-        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-        yyd_sort = nrd_yyd_sort[:,1]
-        xxd_sort = nrd_yyd_sort[:,2]
+        if self.dust_model == 0:
+            return A00 * yyd, xxd
+        else:
+            nrd_yyd = np.zeros((len(nrd),3), dtype='float')
+            nrd_yyd[:,0] = nrd[:]
+            nrd_yyd[:,1] = yyd[:]
+            nrd_yyd[:,2] = xxd[:]
 
-        return A00 * yyd_sort, xxd_sort
+            b = nrd_yyd
+            nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
+            yyd_sort = nrd_yyd_sort[:,1]
+            xxd_sort = nrd_yyd_sort[:,2]
+            return A00 * yyd_sort, xxd_sort
 
 
     def tmp04(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, lib_all=False, f_nrd=False):
@@ -1021,7 +947,7 @@ def tmp04(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, lib_a
                 par['Av'] = self.MB.Avmin
             if par['Av'] > self.MB.Avmax:
                 par['Av'] = self.MB.Avmax
-        Av00 = par['Av']
+        Av = par['Av']
 
         for aa in range(self.MB.npeak):
             if self.MB.ZEVOL==1 or aa == 0:
@@ -1095,18 +1021,8 @@ def tmp04(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, lib_a
         xx = xx_s
         yy = yy_s
 
-        if self.dust_model == 0:
-            yyd, xxd, nrd = dust_calz(xx/(1.+zmc), yy, Av00, nr)
-        elif self.dust_model == 1:
-            yyd, xxd, nrd = dust_mw(xx/(1.+zmc), yy, Av00, nr)
-        elif self.dust_model == 2: # LMC
-            yyd, xxd, nrd = dust_gen(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
-        elif self.dust_model == 3: # SMC
-            yyd, xxd, nrd = dust_gen(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
-        elif self.dust_model == 4: # Kriek&Conroy with gamma=-0.2
-            yyd, xxd, nrd = dust_kc(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.2)
-        else:
-            yyd, xxd, nrd = dust_calz(xx/(1.+zmc), yy, Av00, nr)
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zmc), nr, Av, dust_model=self.dust_model)
+
         xxd *= (1.+zmc)
 
         if self.dust_model == 0:
@@ -1164,7 +1080,7 @@ def tmp04_dust(self, par, nprec=1):
 
   
     def tmp04_neb(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, 
-        lib_all=False, f_nrd=False, f_apply_dust=True):
+        lib_all=False, lib=None, f_nrd=False, f_apply_dust=True):
         '''
         Makes model template with a given param set.
         Also dust attenuation.
@@ -1180,6 +1096,12 @@ def tmp04_neb(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False,
         Mtot = 0
         pp = 0
 
+        if lib == None:
+            if lib_all:
+                lib = self.MB.lib_all
+            else:
+                lib = self.MB.lib
+
         if f_val:
             par = par.params
 
@@ -1197,7 +1119,7 @@ def tmp04_neb(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False,
                 par['Av'] = self.MB.Avmin
             if par['Av'] > self.MB.Avmax:
                 par['Av'] = self.MB.Avmax
-        Av00 = par['Av']
+        Av = par['Av']
 
         try:
             Aneb = par['Aneb']
@@ -1234,20 +1156,12 @@ def tmp04_neb(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False,
             NZ = np.argmin(np.abs(self.MB.Zall-Z))
 
         coln = int(2 + NZ*NU + nlogU)
-        if lib_all:
-            if aa == 0:
-                nr = self.MB.lib_neb_all[:, 0]
-                xx = self.MB.lib_neb_all[:, 1] # This is OBSERVED wavelength range at z=zgal
-                yy = A00 * self.MB.lib_neb_all[:, coln]
-            else:
-                yy += A00 * self.MB.lib_neb_all[:, coln]
+        if aa == 0:
+            nr = lib[:, 0]
+            xx = lib[:, 1] # This is OBSERVED wavelength range at z=zgal
+            yy = A00 * lib[:, coln]
         else:
-            if aa == 0:
-                nr = self.MB.lib_neb[:, 0]
-                xx = self.MB.lib_neb[:, 1] # This is OBSERVED wavelength range at z=zgal
-                yy = A00 * self.MB.lib_neb[:, coln]
-            else:
-                yy += A00 * self.MB.lib_neb[:, coln]
+            yy += A00 * lib[:, coln]
         
         if round(zmc,nprec) != round(self.MB.zgal,nprec):
             xx_s = xx / (1+self.MB.zgal) * (1+zmc)
@@ -1261,18 +1175,7 @@ def tmp04_neb(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False,
         yy = yy_s
 
         if f_apply_dust:
-            if self.dust_model == 0:
-                yyd, xxd, nrd = dust_calz(xx/(1.+zmc), yy, Av00, nr)
-            elif self.dust_model == 1:
-                yyd, xxd, nrd = dust_mw(xx/(1.+zmc), yy, Av00, nr)
-            elif self.dust_model == 2: # LMC
-                yyd, xxd, nrd = dust_gen(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
-            elif self.dust_model == 3: # SMC
-                yyd, xxd, nrd = dust_gen(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
-            elif self.dust_model == 4: # Kriek&Conroy with gamma=-0.2
-                yyd, xxd, nrd = dust_kc(xx/(1.+zmc), yy, Av00, nr, Rv=4.05, gamma=-0.2)
-            else:
-                yyd, xxd, nrd = dust_calz(xx/(1.+zmc), yy, Av00, nr)
+            yyd, xxd, nrd = apply_dust(yy, xx/(1.+zmc), nr, Av, dust_model=self.dust_model)
             xxd *= (1.+zmc)
 
             nrd_yyd = np.zeros((len(nrd),3), dtype=float)
diff --git a/gsf/gsf.py b/gsf/gsf.py
index 59f1b93..6a68a92 100644
--- a/gsf/gsf.py
+++ b/gsf/gsf.py
@@ -250,7 +250,7 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
             from .plot_sed import plot_sed_tau as plot_sed            
 
         if not skip_sfh:
-            plot_sfh(MB, f_comp=MB.ftaucomp, fil_path=MB.DIR_FILT, mmax=mmax,
+            plot_sfh(MB, fil_path=MB.DIR_FILT, mmax=mmax,
             dust_model=MB.dust_model, DIR_TMP=MB.DIR_TMP, f_silence=True, 
             f_SFMS=f_SFMS, f_symbol=f_symbol, skip_zhist=skip_zhist, tau_lim=tau_lim, tset_SFR_SED=tset_SFR_SED)
 
diff --git a/gsf/maketmp_filt.py b/gsf/maketmp_filt.py
index 4278e5b..abafc1f 100644
--- a/gsf/maketmp_filt.py
+++ b/gsf/maketmp_filt.py
@@ -1,4 +1,3 @@
-# The purpose of this code is to figure out Z and redshift (with 1-sig range).
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy
@@ -12,7 +11,6 @@
 from astropy.modeling.models import Moffat1D
 from astropy.convolution import convolve, convolve_fft
 
-# Custom modules
 from .function import *
 from .function_igm import *
 
diff --git a/gsf/maketmp_z0.py b/gsf/maketmp_z0.py
index 5ae0682..249bf17 100644
--- a/gsf/maketmp_z0.py
+++ b/gsf/maketmp_z0.py
@@ -1,8 +1,7 @@
 import numpy as np
-from astropy.io import fits
 import matplotlib.pyplot as plt
 import os
-from astropy.io import ascii
+from astropy.io import ascii,fits
 
 from .function import get_ind
 
@@ -248,8 +247,7 @@ def make_tmp_z0(MB, lammin=100, lammax=160000, tau_lim=0.001, force_no_neb=False
     af.write_to(DIR_TMP + file_out, all_array_compression='zlib')
 
 
-def make_tmp_z0_bpass(MB, lammin=100, lammax=160000, Zforce=None, \
-    Zsun=0.02):
+def make_tmp_z0_bpass(MB, lammin=100, lammax=160000, Zforce=None, Zsun=0.02):
     '''
     This is for the preparation of default template, with BPASS templates, at z=0.
     Should be run before SED fitting.
diff --git a/gsf/plot_sfh.py b/gsf/plot_sfh.py
index 58ff4a4..026c23e 100644
--- a/gsf/plot_sfh.py
+++ b/gsf/plot_sfh.py
@@ -18,7 +18,7 @@
 from .function_igm import *
 
 
-def plot_sfh(MB, f_comp=0, flim=0.01, lsfrl=-3, mmax=1000, Txmin=0.08, Txmax=4, lmmin=5, fil_path='./FILT/',
+def plot_sfh(MB, flim=0.01, lsfrl=-3, mmax=1000, Txmin=0.08, Txmax=4, lmmin=5, fil_path='./FILT/',
     dust_model=0, f_SFMS=False, f_symbol=True, verbose=False, f_silence=True, DIR_TMP=None,
     f_log_sfh=True, dpi=250, TMIN=0.0001, tau_lim=0.01, skip_zhist=False, tset_SFR_SED=0.1, f_axis_force=True):
     '''
@@ -842,7 +842,7 @@ def sfr_tau(t0, tau0, Z=0.0, sfh=0, tt=np.arange(0,13,0.1), Mtot=1.,
     return tt, yy, yyms
 
 
-def plot_sfh_tau(MB, f_comp=0, flim=0.01, lsfrl=-1, mmax=1000, Txmin=0.08, Txmax=4, lmmin=8.5, fil_path='./FILT/',
+def plot_sfh_tau(MB, flim=0.01, lsfrl=-1, mmax=1000, Txmin=0.08, Txmax=4, lmmin=8.5, fil_path='./FILT/',
     dust_model=0, f_SFMS=False, f_symbol=True, verbose=False, f_silence=True, DIR_TMP=None,
     f_log_sfh=True, dpi=250, TMIN=0.0001, tau_lim=0.01, skip_zhist=True, tset_SFR_SED=0.1):
     '''

From c08adeadc61c423402143146465d88371ca8fe41 Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Sat, 10 Dec 2022 13:45:29 -0800
Subject: [PATCH 04/14] minor improvement

---
 gsf/function.py       | 165 +++++++++++++++++++++++++++++++++++-------
 gsf/function_class.py |  72 ++++++++++--------
 gsf/gsf.py            |  10 +++
 gsf/writing.py        |   1 +
 4 files changed, 191 insertions(+), 57 deletions(-)

diff --git a/gsf/function.py b/gsf/function.py
index 2dff9f3..d31c168 100644
--- a/gsf/function.py
+++ b/gsf/function.py
@@ -5,6 +5,7 @@
 import pickle as cPickle
 import os
 import scipy.interpolate as interpolate
+from scipy.interpolate import interp1d
 import logging
 
 ################
@@ -1107,30 +1108,33 @@ def filconv(band0, l0, f0, DIR, fw=False, f_regist=True, MB=None):
     '''
     Parameters
     ----------
-    f0 : 
+    f0 : float array
         Flux for spectrum, in fnu
-    l0 : 
+    l0 : float array
         Wavelength for spectrum, in AA (that matches filter response curve's.)
-    f_regist : 
-        If True, read filter response curve.
+    f_regist : bool
+        If True, read filter response curves and register those to MB.
     '''
     if MB==None:
         f_regist = True
     if f_regist:
         lfil_lib = {}
         ffil_lib = {}
-        
-    fnu = np.zeros(len(band0), dtype='float')
-    lcen = np.zeros(len(band0), dtype='float')
-    if fw == True:
-        fwhm = np.zeros(len(band0), dtype='float')
 
+    fnu = np.zeros_like(band0, dtype=float)
+    lcen = np.zeros_like(band0, dtype=float)
+    if fw:
+        fwhm = np.zeros_like(band0, dtype=float)
+    
     for ii in range(len(band0)):
         if not f_regist:
             try:
                 lfil = MB.lfil_lib['%s'%str(band0[ii])]
                 ffil = MB.ffil_lib['%s'%str(band0[ii])]
-                if fw == True:
+                lmin = MB.lfil_lib['%s_lmin'%str(band0[ii])]
+                lmax = MB.lfil_lib['%s_lmax'%str(band0[ii])]
+                lcen[ii] = MB.lfil_lib['%s_lcen'%str(band0[ii])]
+                if fw:
                     fwhm = MB.filt_fwhm
             except:
                 f_regist = True
@@ -1142,35 +1146,42 @@ def filconv(band0, l0, f0, DIR, fw=False, f_regist=True, MB=None):
             lfil = fd[:,1]
             ffil = fd[:,2]
             ffil /= np.max(ffil)
-            if fw == True:
+            lmin = np.min(lfil)
+            lmax = np.max(lfil)
+
+            if fw:
                 ffil_cum = np.cumsum(ffil)
                 ffil_cum/= ffil_cum.max()
                 con = (ffil_cum>0.05) & (ffil_cum<0.95)
-                fwhm[ii] = np.max(lfil[con]) - np.min(lfil[con])       
+                fwhm[ii] = np.max(lfil[con]) - np.min(lfil[con])
+
+            con = (l0>lmin) & (l0<lmax)
+            delw = np.nanmin(np.diff(l0))
+            if delw > np.nanmin(np.diff(ffil)):
+                lfil_new = np.arange(lmin,lmax,delw)
+                fint = interpolate.interp1d(lfil, ffil, kind='nearest', fill_value="extrapolate")
+                ffil = fint(lfil_new)
+                lfil = lfil_new
 
             lfil_lib['%s'%str(band0[ii])] = lfil
             ffil_lib['%s'%str(band0[ii])] = ffil
 
-        lmin  = np.min(lfil)
-        lmax  = np.max(lfil)
-        imin  = 0
-        imax  = 0
+            lcen[ii] = np.sum(lfil*ffil)/np.sum(ffil)
+            lfil_lib['%s_lmin'%str(band0[ii])] = lmin
+            lfil_lib['%s_lmax'%str(band0[ii])] = lmax
+            lfil_lib['%s_lcen'%str(band0[ii])] = lcen[ii]
 
         con = (l0>lmin) & (l0<lmax) #& (f0>0)
-        lcen[ii]  = np.sum(lfil*ffil)/np.sum(ffil)
         if len(l0[con])>1:
-            lamS,spec = l0[con], f0[con] # Two columns with wavelength and flux density
-            lamF,filt = lfil, ffil # Two columns with wavelength and response in the range [0,1]
-            fint = interpolate.interp1d(lamF, filt, kind='nearest', fill_value="extrapolate")
-            filt_int = fint(lamS)
-            wht = 1.
+            fint = interp1d(lfil, ffil, kind='nearest', fill_value="extrapolate")
+            filt_int = fint(l0[con])
 
             # This does not work sometimes;
-            delS = lamS[1]-lamS[0]
-            I1 = np.sum(spec/lamS**2*c*filt_int*lamS*delS)   #Denominator for Fnu
-            I2 = np.sum(filt_int/lamS*delS)                  #Numerator
+            #delS = l0[con][1]-l0[con][0]
+            I1 = np.sum(f0[con]/l0[con]**2*c*filt_int*l0[con])
+            I2 = np.sum(filt_int/l0[con])
             if I2>0:
-                fnu[ii] = I1/I2/c         #Average flux density
+                fnu[ii] = I1/I2/c
             else:
                 fnu[ii] = 0
         else:
@@ -1179,7 +1190,7 @@ def filconv(band0, l0, f0, DIR, fw=False, f_regist=True, MB=None):
     if MB != None and f_regist:
         MB.lfil_lib = lfil_lib
         MB.ffil_lib = ffil_lib
-        if fw == True:
+        if fw:
             MB.filt_fwhm = fwhm
         
     if fw:
@@ -1188,6 +1199,106 @@ def filconv(band0, l0, f0, DIR, fw=False, f_regist=True, MB=None):
         return lcen, fnu
 
 
+"""
+def filconv(band0, l0, f0, DIR, fw=False, f_regist=True, MB=None):
+    '''
+    This one does not improve much.
+    
+    Parameters
+    ----------
+    f0 : float array
+        Flux for spectrum, in fnu
+    l0 : float array
+        Wavelength for spectrum, in AA (that matches filter response curve's.)
+    f_regist : bool
+        If True, read filter response curves and register those to MB.
+    '''
+    if MB==None:
+        f_regist = True
+    if f_regist:
+        lfil_lib = {}
+        ffil_lib = {}
+
+    if fw:
+        fwhm = np.zeros_like(band0, dtype=float)
+
+    if not f_regist:
+        try:
+            ffil_lib = MB.ffil_lib
+            if fw:
+                fwhm = MB.filt_fwhm
+        except:
+            f_regist = True
+            ffil_lib = {}
+            # lfils = np.zeros_like(band0, dtype=float)
+            # ffils = np.zeros_like(band0, dtype=float)
+            # lmins = np.zeros_like(band0, dtype=float)
+            # lmaxs = np.zeros_like(band0, dtype=float)
+
+    if f_regist:
+        lcen = np.zeros_like(band0, dtype=float)
+        for ii in range(len(band0)):
+            ffil_lib['%s'%band0[ii]] = {}
+            
+            fd = np.loadtxt(DIR + '%s.fil'%str(band0[ii]), comments='#')
+            lfil = fd[:,1]
+            ffil = fd[:,2]
+            ffil /= np.max(ffil)
+            lmin = np.min(lfil)
+            lmax = np.max(lfil)
+
+            if fw:
+                ffil_cum = np.cumsum(ffil)
+                ffil_cum/= ffil_cum.max()
+                con = (ffil_cum>0.05) & (ffil_cum<0.95)
+                fwhm[ii] = np.max(lfil[con]) - np.min(lfil[con])
+
+            con = (l0>lmin) & (l0<lmax)
+            delw = np.nanmin(np.diff(l0))
+            if delw > np.nanmin(np.diff(ffil)):
+                lfil_new = np.arange(lmin,lmax,delw)
+                fint = interpolate.interp1d(lfil, ffil, kind='nearest', fill_value="extrapolate")
+                ffil = fint(lfil_new)
+                lfil = lfil_new
+
+            ffil_lib['%s'%band0[ii]]['lfil'] = lfil
+            ffil_lib['%s'%band0[ii]]['ffil'] = ffil
+            ffil_lib['%s'%band0[ii]]['lmin'] = lmin
+            ffil_lib['%s'%band0[ii]]['lmax'] = lmax
+            lcen[ii] = np.sum(lfil*ffil)/np.sum(ffil)
+        
+        ffil_lib['lcen'] = lcen
+
+    fnu = np.zeros_like(band0, dtype=float)
+    for ii in range(len(band0)):
+        con = (l0>ffil_lib['%s'%band0[ii]]['lmin']) & (l0<ffil_lib['%s'%band0[ii]]['lmax']) #& (f0>0)
+
+        if len(l0[con])>1:
+            fint = interp1d(ffil_lib['%s'%band0[ii]]['lfil'], ffil_lib['%s'%band0[ii]]['ffil'], kind='nearest', fill_value="extrapolate")
+            filt_int = fint(l0[con])
+
+            # This does not work sometimes;
+            I1 = np.sum(f0[con]/l0[con]**2*c*filt_int*l0[con])
+            I2 = np.sum(filt_int/l0[con])
+            if I2>0:
+                fnu[ii] = I1/I2/c
+            else:
+                fnu[ii] = 0
+        else:
+            fnu[ii] = 0
+
+    if MB != None and f_regist:
+        MB.ffil_lib = ffil_lib
+        if fw:
+            MB.filt_fwhm = fwhm
+        
+    if fw:
+        return ffil_lib['lcen'], fnu, fwhm
+    else:
+        return ffil_lib['lcen'], fnu
+"""
+
+
 def fil_fwhm(band0, DIR):
     '''
     Parameters
diff --git a/gsf/function_class.py b/gsf/function_class.py
index c87c183..f8828ad 100644
--- a/gsf/function_class.py
+++ b/gsf/function_class.py
@@ -374,7 +374,7 @@ def tmp03_neb(self, A00, Av, logU, nmodel, Z, zgal, lib, f_apply_dust=True, EBVr
         return A00 * yyd_sort, xxd_sort
 
 
-    def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None):
+    def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_get_Mtot=False, f_check_limit=True):
         '''
         Purpose
         -------
@@ -402,17 +402,18 @@ def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None):
                 Z = par['Z'+str(aa)]
                 NZ = self.MB.bfnc.Z2NZ(Z)
 
-            # Check limit;
-            if par['A'+str(aa)] < self.MB.Amin:
-                par['A'+str(aa)] = self.MB.Amin
-            if par['A'+str(aa)] > self.MB.Amax:
-                par['A'+str(aa)] = self.MB.Amax
-            # Z limit:
-            if aa == 0 or self.MB.ZEVOL == 1:
-                if par['Z%d'%aa] < self.MB.Zmin:
-                    par['Z%d'%aa] = self.MB.Zmin
-                if par['Z%d'%aa] > self.MB.Zmax:
-                    par['Z%d'%aa] = self.MB.Zmax
+            if f_check_limit:
+                # Check limit;
+                if par['A'+str(aa)] < self.MB.Amin:
+                    par['A'+str(aa)] = self.MB.Amin
+                if par['A'+str(aa)] > self.MB.Amax:
+                    par['A'+str(aa)] = self.MB.Amax
+                # Z limit:
+                if aa == 0 or self.MB.ZEVOL == 1:
+                    if par['Z%d'%aa] < self.MB.Zmin:
+                        par['Z%d'%aa] = self.MB.Zmin
+                    if par['Z%d'%aa] > self.MB.Zmax:
+                        par['Z%d'%aa] = self.MB.Zmax
 
             # Is A in logspace?
             if f_Alog:
@@ -422,8 +423,9 @@ def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None):
 
             coln = int(2 + pp*len(self.ZZ)*len(self.AA) + NZ*len(self.AA) + aa)
 
-            mslist = self.MB.af['ML']['ML_'+str(NZ)][aa]
-            Mtot += 10**(par['A%d'%aa] + np.log10(mslist))
+            if f_get_Mtot:
+                mslist = self.MB.af['ML']['ML_'+str(NZ)][aa]
+                Mtot += 10**(par['A%d'%aa] + np.log10(mslist))
 
             if aa == 0:
                 nr = lib[:,0]
@@ -432,7 +434,10 @@ def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None):
             else:
                 yy += A00 * lib[:,coln]
 
-        return nr, xx, yy, Mtot
+        if f_get_Mtot:
+            return nr, xx, yy, Mtot
+        else:
+            return nr, xx, yy
 
 
     def get_total_flux_neb(self, par, f_Alog=True, lib_all=True, lib=None):
@@ -500,7 +505,7 @@ def get_total_flux_neb(self, par, f_Alog=True, lib_all=True, lib=None):
 
 
     def tmp04(self, par, f_Alog:bool=True, nprec:int=1, pp:int = 0, f_val:bool=False, lib_all:bool=False, f_nrd:bool=False,
-        f_IGM=True, deltaz_lim=99):
+        f_IGM=True, deltaz_lim=0.1):
         '''
         Makes model template with a given param set.
         Also dust attenuation.
@@ -533,24 +538,31 @@ def tmp04(self, par, f_Alog:bool=True, nprec:int=1, pp:int = 0, f_val:bool=False
         if par['Av'] > self.MB.Avmax:
             par['Av'] = self.MB.Avmax
 
-        nr, xx, yy, Mtot = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp)
-        self.MB.logMtmp = np.log10(Mtot)
+        nr, xx, yy = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, f_get_Mtot=False)
+        # self.MB.logMtmp = np.log10(Mtot)
 
         # @@@ Filter convolution may need to happpen here
         if round(zmc,nprec) != round(self.MB.zgal,nprec):
-            if np.abs(zmc - self.MB.zgal) > deltaz_lim:
-                print('!!! zmc (%.3f) is exploring too far from zgal (%.3f).'%(zmc, self.MB.zgal))
-                # This takes too much time;
-                self.MB.zgal = zmc
-                if self.MB.SFH_FORM == -99:
-                    flag_suc = maketemp(self.MB, tau_lim=self.MB.tau_lim, nthin=self.MB.nthin, delwave=self.MB.delwave)
-                else:
-                    flag_suc = maketemp_tau(self.MB, tau_lim=self.MB.tau_lim, nthin=self.MB.nthin, delwave=self.MB.delwave)
-                # Load Spectral library;
-                self.lib = self.open_spec_fits(fall=0)
-                self.lib_all = self.open_spec_fits(fall=1, orig=True)
+            if np.abs(zmc - self.MB.zgal) > deltaz_lim and not self.MB.f_spec:
+                # print('!!! zmc (%.3f) is exploring too far from zgal (%.3f).'%(zmc, self.MB.zgal))
+
+                # @@@ This only work for BB only data set.
                 # Get total flux;
-                nr, xx, yy, Mtot = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp)
+                if lib_all:
+                    nr_full, xx_full, yy_full = nr, xx, yy
+                else:
+                    nr_full, xx_full, yy_full = self.get_total_flux(par, f_Alog=f_Alog, lib_all=True, pp=pp, f_get_Mtot=False) # Get full spectrum; OBSERVED wavelength range at z=MB.zgal;
+                
+                xx, yy = filconv(self.MB.filts, xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, self.MB.DIR_FILT, MB=self.MB, f_regist=False)
+
+                if False:
+                    import matplotlib.pyplot as plt
+                    plt.plot(xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, color='r')
+                    plt.scatter(ltmpbb, ftmpbb, color='r')
+                    plt.plot(xx_full, yy_full, color='b')
+                    plt.scatter(xx, yy, color='b')
+                    plt.show()
+                    hoge
                 xx_s = xx
                 yy_s = yy
             else:
diff --git a/gsf/gsf.py b/gsf/gsf.py
index 6a68a92..3b727f8 100644
--- a/gsf/gsf.py
+++ b/gsf/gsf.py
@@ -271,6 +271,16 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
     '''
 
     if fplt == 6:
+        # Use the final redshift;
+        from astropy.io import fits
+        hd_sum = fits.open(os.path.join(MB.DIR_OUT, 'summary_%s.fits'%MB.ID))[0].header
+        MB.zgal = hd_sum['ZMC']
+        
+        if MB.SFH_FORM == -99:
+            flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+        else:
+            flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+
         if MB.SFH_FORM == -99:
             from .plot_sed import plot_corner_physparam_frame,plot_corner_physparam_summary
             plot_corner_physparam_summary(MB)
diff --git a/gsf/writing.py b/gsf/writing.py
index bd271c3..0c93fc4 100644
--- a/gsf/writing.py
+++ b/gsf/writing.py
@@ -1,5 +1,6 @@
 import numpy as np
 from astropy.io import fits
+from astropy import units as u
 import asdf
 import gsf
 from .function import filconv, calc_Dn4

From 20d52be4f6109a569ecb01bb9a37dfb3836d26ab Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Sat, 10 Dec 2022 14:39:28 -0800
Subject: [PATCH 05/14] massive clean in function_class.

---
 gsf/fitting.py            |   8 +-
 gsf/function_class.py     | 488 ++++++++++++++++++--------------------
 gsf/plot_sed.py           |  49 ++--
 gsf/posterior_flexible.py |   4 +-
 gsf/posterior_nested.py   |  24 +-
 5 files changed, 272 insertions(+), 301 deletions(-)

diff --git a/gsf/fitting.py b/gsf/fitting.py
index f0822e8..ad33581 100644
--- a/gsf/fitting.py
+++ b/gsf/fitting.py
@@ -1500,8 +1500,8 @@ def prepare_class(self, add_fir=None):
             self.lib_dust = self.fnc.open_spec_dust_fits(fall=0)
             self.lib_dust_all = self.fnc.open_spec_dust_fits(fall=1)
         if self.fneb:
-            self.lib_neb = self.fnc.open_spec_neb_fits(fall=0)
-            self.lib_neb_all = self.fnc.open_spec_neb_fits(fall=1)
+            self.lib_neb = self.fnc.open_spec_fits(fall=0, f_neb=True)
+            self.lib_neb_all = self.fnc.open_spec_fits(fall=1, f_neb=True)
 
         if add_fir == None:
             add_fir = self.f_dust
@@ -1661,7 +1661,7 @@ def main(self, cornerplot:bool=True, specplot=1, sigz=1.0, ezmin=0.01, ferr=0,
             Av_tmp = out.params['Av'].value
             AA_tmp = np.zeros(len(self.age), dtype='float')
             ZZ_tmp = np.zeros(len(self.age), dtype='float')
-            nrd_tmp, fm_tmp, xm_tmp = self.fnc.tmp04(out, f_val=True, f_nrd=True)
+            nrd_tmp, fm_tmp, xm_tmp = self.fnc.get_template(out, f_val=True, f_nrd=True, f_neb=False)
         else:
             csq = out.chisqr
             rcsq = out.redchi
@@ -2102,7 +2102,7 @@ def quick_fit(self, specplot=1, sigz=1.0, ezmin=0.01, ferr=0, f_move=False, f_ge
             Av_tmp = out.params['Av'].value
             AA_tmp = np.zeros(len(self.age), dtype='float')
             ZZ_tmp = np.zeros(len(self.age), dtype='float')
-            fm_tmp, xm_tmp = self.fnc.tmp04(out, f_val=True)
+            fm_tmp, xm_tmp = self.fnc.get_template(out, f_val=True)
 
             ########################
             # Check redshift
diff --git a/gsf/function_class.py b/gsf/function_class.py
index f8828ad..584fdd8 100644
--- a/gsf/function_class.py
+++ b/gsf/function_class.py
@@ -48,15 +48,16 @@ def __init__(self, MB, dust_model:int = 0):
         self.f_af0 = False
 
 
-    def open_spec_fits(self, fall:int = 0, orig:bool = False):
+    def open_spec_fits(self, fall:int = 0, orig:bool = False, f_neb=False):
         '''
         Load template in obs range.
         '''
         ID0 = self.MB.ID
-        tau0= self.MB.tau0
         ZZ = self.ZZ
         AA = self.AA
         bfnc = self.MB.bfnc
+        tau0 = np.arange(0,1,1)
+        ntau0 = len(tau0)
 
         # ASDF;
         if fall == 0:
@@ -66,25 +67,44 @@ def open_spec_fits(self, fall:int = 0, orig:bool = False):
             app = 'all_'
             hdu0 = self.MB.af['spec_full']
 
-        DIR_TMP = self.DIR_TMP
-        for pp in range(self.MB.ntau0):
-            for zz in range(len(ZZ)):
-                Z = ZZ[zz]
-                NZ = bfnc.Z2NZ(Z)
-                if zz == 0 and pp == 0:
-                    nr = hdu0['colnum']
-                    xx = hdu0['wavelength']
-                    lib = np.zeros((len(nr), 2+len(AA)*len(ZZ)*len(tau0)), dtype='float')
-                    lib[:,0] = nr[:]
-                    lib[:,1] = xx[:]
+        if not f_neb:
+            DIR_TMP = self.DIR_TMP
+            for pp in range(ntau0):
+                for zz in range(len(ZZ)):
+                    Z = ZZ[zz]
+                    NZ = bfnc.Z2NZ(Z)
+                    if zz == 0 and pp == 0:
+                        nr = hdu0['colnum']
+                        xx = hdu0['wavelength']
+                        lib = np.zeros((len(nr), 2+len(AA)*len(ZZ)*len(tau0)), dtype='float')
+                        lib[:,0] = nr[:]
+                        lib[:,1] = xx[:]
 
-                for aa in range(self.MB.npeak):
-                    coln = int(2+aa)
+                    for aa in range(self.MB.npeak):
+                        coln = int(2+aa)
+                        if orig:
+                            colname = 'fspec_orig_' + str(zz) + '_' + str(aa) + '_' + str(pp)
+                        else:
+                            colname = 'fspec_' + str(zz) + '_' + str(aa) + '_' + str(pp)
+                        colnall = int(2 + pp*len(ZZ)*len(AA) + zz*len(AA) + aa) # 2 takes account of wavelength and AV columns.
+                        lib[:,colnall] = hdu0[colname]
+        else:
+            NZ = len(ZZ)
+            NU = len(self.MB.logUs)
+            for zz,Z in enumerate(ZZ):
+                for uu,logU in enumerate(self.MB.logUs):
+                    if zz == 0 and uu == 0:
+                        nr = hdu0['colnum']
+                        xx = hdu0['wavelength']
+                        coln = int(2 + NZ * NU)
+                        lib = np.zeros((len(nr), coln), dtype=float)
+                        lib[:,0] = nr[:]
+                        lib[:,1] = xx[:]
                     if orig:
-                        colname = 'fspec_orig_' + str(zz) + '_' + str(aa) + '_' + str(pp)
+                        colname = 'fspec_orig_nebular_Z%d'%zz + '_logU%d'%uu
                     else:
-                        colname = 'fspec_' + str(zz) + '_' + str(aa) + '_' + str(pp)
-                    colnall = int(2 + pp*len(ZZ)*len(AA) + zz*len(AA) + aa) # 2 takes account of wavelength and AV columns.
+                        colname = 'fspec_nebular_Z%d'%zz + '_logU%d'%uu
+                    colnall = int(2 + zz * NU + uu) # 2 takes account of wavelength and AV columns.
                     lib[:,colnall] = hdu0[colname]
 
         return lib
@@ -128,6 +148,7 @@ def open_spec_dust_fits(self, fall:int = 0):
         return lib
 
 
+    """
     def open_spec_neb_fits(self, fall:int = 0, orig:bool = False):
         '''
         Loads template in obs range.
@@ -167,8 +188,9 @@ def open_spec_neb_fits(self, fall:int = 0, orig:bool = False):
                 lib[:,colnall] = hdu0[colname]
 
         return lib
+    """
 
-
+    """
     def open_spec_fits_dir(self, nage:int, nz:int, kk, Av:float, zgal:float, A00:float, f_IGM=True):
         '''
         Load template in obs range.
@@ -219,160 +241,7 @@ def open_spec_fits_dir(self, nage:int, nz:int, kk, Av:float, zgal:float, A00:flo
         xxd_sort = nrd_yyd_sort[:,2]
 
         return A00 * yyd_sort, xxd_sort
-
-
-    def get_template(self, lib, Amp:float = 1.0, T:float = 1.0, Av:float = 0.0, Z:float = 0.0, zgal:float = 1.0, f_bb:bool = False, fneb=False):
-        '''
-        Gets an element template given a set of parameters.
-        Not necessarily the most efficient way, but easy to use.
-
-        Parameters:
-        -----------
-        lib : dict
-            library dictionary.
-        Amp : float
-            Amplitude of the target template. Note that each template has Lbol = 1e10Lsun.
-        T : float
-            Age, in Gyr.
-        Av : float
-            Dust attenuation, in mag.
-        Z : float
-            Metallicity, in log(Z/Zsun).
-        zgal : float
-            Redshift.
-        f_bb: bool
-            If calculate bb photometry for the spectrum requested.
-
-        Returns
-            flux : float array. Flux in Fnu. 
-            wavelength : float array. Wave in AA.
-            lcen, lflux : , if f_bb==True.
-
-        '''
-        bfnc = self.MB.bfnc
-        DIR_TMP = self.MB.DIR_TMP 
-        NZ  = bfnc.Z2NZ(Z)
-
-        pp0 = np.random.uniform(low=0, high=self.MB.ntau0, size=(1,))
-        pp  = int(pp0[0])
-        if pp>=self.MB.ntau0:
-            pp += -1
-
-        nmodel = np.argmin(np.abs(T-self.age[:]))
-        if T - self.age[nmodel] != 0:
-            print('T=%.2f is not found in age library. T=%.2f is used.'%(T,self.age[nmodel]))
-
-        if fneb:
-            coln = int(2 + pp*len(self.ZZ)*1 + NZ*1 + 0)
-        else:
-            coln = int(2 + pp*len(self.ZZ)*len(self.AA) + NZ*len(self.AA) + nmodel)
-        nr = lib[:, 0]
-        xx = lib[:, 1] # This is OBSERVED wavelength range at z=zgal
-        yy = lib[:, coln]
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
-        xxd *= (1.+zgal)
-
-        if self.dust_model != 0:
-            # This may be needed when not calzetti model
-            nrd_yyd = np.zeros((len(nrd),3), dtype=float)
-            nrd_yyd[:,0] = nrd[:]
-            nrd_yyd[:,1] = yyd[:]
-            nrd_yyd[:,2] = xxd[:]
-            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
-            nrd[:],yyd[:],xxd[:] = nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
-
-        if f_bb:
-            fil_cen, fil_flux = filconv_fast(self.MB, xxd, Amp * yyd)
-            return Amp * yyd, xxd, fil_flux, fil_cen
-        else:
-            return Amp * yyd, xxd
-
-
-    def tmp03(self, A00, Av, nmodel, Z, zgal, lib):
-        '''
-        '''
-        tau0= self.tau0 #[0.01,0.02,0.03]
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc #Basic(ZZ)
-        DIR_TMP = self.MB.DIR_TMP #'./templates/'
-        NZ  = bfnc.Z2NZ(Z)
-
-        pp0 = np.random.uniform(low=0, high=len(tau0), size=(1,))
-        pp  = int(pp0[0])
-        if pp>=len(tau0):
-            pp += -1
-
-        coln= int(2 + pp*len(ZZ)*len(AA) + NZ*len(AA) + nmodel)
-        nr  = lib[:,0]
-        xx  = lib[:,1] # This is OBSERVED wavelength range at z=zgal
-        yy  = lib[:,coln]
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
-
-        xxd *= (1.+zgal)
-
-        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-        nrd_yyd[:,0] = nrd[:]
-        nrd_yyd[:,1] = yyd[:]
-        nrd_yyd[:,2] = xxd[:]
-
-        b = nrd_yyd
-        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-        yyd_sort = nrd_yyd_sort[:,1]
-        xxd_sort = nrd_yyd_sort[:,2]
-
-        return A00 * yyd_sort, xxd_sort
-
-
-    def tmp03_neb(self, A00, Av, logU, nmodel, Z, zgal, lib, f_apply_dust=True, EBVratio=2.27):
-        '''
-        EBVratio : float
-            E(B-V)_neb / E(B-V)_st. 
-            Useful table in https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf
-        '''
-        tau0 = self.tau0
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-        DIR_TMP = self.MB.DIR_TMP
-        NZ = bfnc.Z2NZ(Z)
-        NU = len(self.MB.logUs)
-
-        # Dust attenuation to nebulae
-        Av *= EBVratio
-
-        pp0 = np.random.uniform(low=0, high=len(tau0), size=(1,))
-        pp = int(pp0[0])
-        if pp>=len(tau0):
-            pp += -1
-
-        nlogU = np.argmin(np.abs(self.MB.logUs - logU))
-        coln = int(2 + NZ*NU + nlogU)
-        nr = lib[:,0]
-        xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
-        yy = lib[:,coln]
-
-        if f_apply_dust:
-            yyd, xxd, nrd = apply_dust(yy, xx/(1+zgal), nr, Av, dust_model=self.dust_model)
-        else:
-            yyd, xxd, nrd = yy, xx, nr
-
-        xxd *= (1.+zgal)
-
-        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-        nrd_yyd[:,0] = nrd[:]
-        nrd_yyd[:,1] = yyd[:]
-        nrd_yyd[:,2] = xxd[:]
-
-        b = nrd_yyd
-        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-        yyd_sort = nrd_yyd_sort[:,1]
-        xxd_sort = nrd_yyd_sort[:,2]
-
-        return A00 * yyd_sort, xxd_sort
-
+    """
 
     def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_get_Mtot=False, f_check_limit=True):
         '''
@@ -383,7 +252,7 @@ def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_get_M
         Parameters
         ----------
         par : library
-            contains parameters
+            contains parameters. Needs to include: ['Z','A']
 
         Returns
         -------
@@ -503,100 +372,104 @@ def get_total_flux_neb(self, par, f_Alog=True, lib_all=True, lib=None):
 
         return nr, xx, yy
 
-
-    def tmp04(self, par, f_Alog:bool=True, nprec:int=1, pp:int = 0, f_val:bool=False, lib_all:bool=False, f_nrd:bool=False,
-        f_IGM=True, deltaz_lim=0.1):
+    """
+    def get_template_single(self, A00, Av, nmodel, Z, zgal, lib):
         '''
-        Makes model template with a given param set.
-        Also dust attenuation.
-
-        Parameters
-        ----------
-        nprec : int
-            Precision when redshift is refined. 
-        deltaz_lim : float
-            limit in np.abs(zmc - self.MB.zgal).
         '''
+        tau0= self.tau0
         ZZ = self.ZZ
         AA = self.AA
         bfnc = self.MB.bfnc
+        DIR_TMP = self.MB.DIR_TMP
+        NZ = bfnc.Z2NZ(Z)
 
-        if f_val:
-            par = par.params
+        pp0 = np.random.uniform(low=0, high=len(tau0), size=(1,))
+        pp = int(pp0[0])
+        if pp>=len(tau0):
+            pp += -1
 
-        if self.MB.fzmc == 1:
-            try:
-                zmc = par['zmc'].value
-            except:
-                zmc = self.MB.zgal
-        else:
-            zmc = self.MB.zgal
+        coln = int(2 + pp*len(ZZ)*len(AA) + NZ*len(AA) + nmodel)
+        nr = lib[:,0]
+        xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
+        yy = lib[:,coln]
 
-        # AV limit;
-        if par['Av'] < self.MB.Avmin:
-            par['Av'] = self.MB.Avmin
-        if par['Av'] > self.MB.Avmax:
-            par['Av'] = self.MB.Avmax
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
 
-        nr, xx, yy = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, f_get_Mtot=False)
-        # self.MB.logMtmp = np.log10(Mtot)
+        xxd *= (1.+zgal)
 
-        # @@@ Filter convolution may need to happpen here
-        if round(zmc,nprec) != round(self.MB.zgal,nprec):
-            if np.abs(zmc - self.MB.zgal) > deltaz_lim and not self.MB.f_spec:
-                # print('!!! zmc (%.3f) is exploring too far from zgal (%.3f).'%(zmc, self.MB.zgal))
+        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
+        nrd_yyd[:,0] = nrd[:]
+        nrd_yyd[:,1] = yyd[:]
+        nrd_yyd[:,2] = xxd[:]
 
-                # @@@ This only work for BB only data set.
-                # Get total flux;
-                if lib_all:
-                    nr_full, xx_full, yy_full = nr, xx, yy
-                else:
-                    nr_full, xx_full, yy_full = self.get_total_flux(par, f_Alog=f_Alog, lib_all=True, pp=pp, f_get_Mtot=False) # Get full spectrum; OBSERVED wavelength range at z=MB.zgal;
-                
-                xx, yy = filconv(self.MB.filts, xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, self.MB.DIR_FILT, MB=self.MB, f_regist=False)
-
-                if False:
-                    import matplotlib.pyplot as plt
-                    plt.plot(xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, color='r')
-                    plt.scatter(ltmpbb, ftmpbb, color='r')
-                    plt.plot(xx_full, yy_full, color='b')
-                    plt.scatter(xx, yy, color='b')
-                    plt.show()
-                    hoge
-                xx_s = xx
-                yy_s = yy
-            else:
-                fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
-                xx_s = xx / (1+self.MB.zgal) * (1+zmc)
-                yy_s = fint(xx_s)
+        b = nrd_yyd
+        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
+        yyd_sort = nrd_yyd_sort[:,1]
+        xxd_sort = nrd_yyd_sort[:,2]
 
-        else:
-            xx_s = xx
-            yy_s = yy
+        return A00 * yyd_sort, xxd_sort
+    """
 
-        xx = xx_s
-        yy = yy_s
+    def get_template_single(self, A00, Av, nmodel, Z, zgal, lib, logU=None, f_apply_dust=True, EBVratio=2.27):
+        '''
+        EBVratio : float
+            E(B-V)_neb / E(B-V)_st. 
+            Useful table in https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf
 
-        # Apply dust;
-        yyd, xxd, nrd = apply_dust(yy, xx/(1+zmc), nr, par['Av'], dust_model=self.dust_model)
+        Notes
+        -----
+        This function is only used in plot_sed.py.
+        Common function for mebular and nonnebular temlates.
+        '''
+        tau0 = self.tau0
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+        DIR_TMP = self.MB.DIR_TMP
+        NZ = bfnc.Z2NZ(Z)
 
-        xxd *= (1.+zmc)
-        if self.dust_model != 0:
-            # This may be needed when not calzetti model
-            nrd_yyd = np.zeros((len(nrd),3), dtype=float)
-            nrd_yyd[:,0] = nrd[:]
-            nrd_yyd[:,1] = yyd[:]
-            nrd_yyd[:,2] = xxd[:]
-            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
-            nrd[:],yyd[:],xxd[:] = nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
+        pp0 = np.random.uniform(low=0, high=len(tau0), size=(1,))
+        pp = int(pp0[0])
+        if pp>=len(tau0):
+            pp += -1
 
-        if not f_nrd:
-            return yyd[:],xxd[:] 
+        if logU != None:
+            NU = len(self.MB.logUs)
+
+            # Dust attenuation to nebulae
+            Av *= EBVratio
+            nlogU = np.argmin(np.abs(self.MB.logUs - logU))
+            coln = int(2 + NZ*NU + nlogU)
         else:
-            return nrd[:],yyd[:],xxd[:]
+            coln = int(2 + pp*len(ZZ)*len(AA) + NZ*len(AA) + nmodel)
+
+        # 
+        nr = lib[:,0]
+        xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
+        yy = lib[:,coln]
+
+        if f_apply_dust:
+            yyd, xxd, nrd = apply_dust(yy, xx/(1+zgal), nr, Av, dust_model=self.dust_model)
+        else:
+            yyd, xxd, nrd = yy, xx, nr
+
+        xxd *= (1.+zgal)
+
+        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
+        nrd_yyd[:,0] = nrd[:]
+        nrd_yyd[:,1] = yyd[:]
+        nrd_yyd[:,2] = xxd[:]
+
+        b = nrd_yyd
+        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
+        yyd_sort = nrd_yyd_sort[:,1]
+        xxd_sort = nrd_yyd_sort[:,2]
+
+        return A00 * yyd_sort, xxd_sort
 
 
-    def tmp04_neb(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib_all:bool=False, f_nrd:bool=False, f_apply_dust:bool=True, EBVratio:float=2.27):
+    def get_template(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib_all:bool=False, f_nrd:bool=False, 
+        f_apply_dust:bool=True, f_IGM=True, deltaz_lim=0.1, f_neb=False, EBVratio:float=2.27):
         '''
         Makes model template for a nebular emission.
         This is only for nubular template, with len(A) = 1. Thus, aa=0 during the code.
@@ -610,6 +483,13 @@ def tmp04_neb(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib_al
         EBVratio : float
             E(B-V)_neb / E(B-V)_st. 
             Useful table in https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf
+        f_neb : bool
+            Expect to explore nebular template or not.
+
+        Notes
+        -----
+        This function is only used in plot_sed.py.
+        Common function for mebular and nonnebular temlates.
         '''
         ZZ = self.ZZ
         AA = self.AA
@@ -633,17 +513,37 @@ def tmp04_neb(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib_al
             par['Av'] = self.MB.Avmax
         Av = par['Av']
 
-        # Dust attenuation to nebulae
-        Av *= EBVratio
+        if f_neb:
+            # Dust attenuation to nebulae
+            Av *= EBVratio
 
-        # Get total flux;
-        nr, xx, yy = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=lib_all)
+        if f_neb:
+            # Get total flux;
+            nr, xx, yy = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=lib_all)
+        else:
+            nr, xx, yy = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all)
 
-        # Redshift refinement;
+        # @@@ Filter convolution may need to happpen here
         if round(zmc,nprec) != round(self.MB.zgal,nprec):
-            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
-            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
-            yy_s = fint(xx_s)
+            if np.abs(zmc - self.MB.zgal) > deltaz_lim and not self.MB.f_spec:
+                # print('!!! zmc (%.3f) is exploring too far from zgal (%.3f).'%(zmc, self.MB.zgal))
+
+                # @@@ This only work for BB only data set.
+                # Get total flux;
+                if lib_all:
+                    nr_full, xx_full, yy_full = nr, xx, yy
+                else:
+                    if f_neb:
+                        nr_full, xx_full, yy_full = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=True)
+                    else:
+                        nr_full, xx_full, yy_full = self.get_total_flux(par, f_Alog=f_Alog, lib_all=True)
+
+                xx_s, yy_s = filconv(self.MB.filts, xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, self.MB.DIR_FILT, MB=self.MB, f_regist=False)
+            else:
+                fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
+                xx_s = xx / (1+self.MB.zgal) * (1+zmc)
+                yy_s = fint(xx_s)
+
         else:
             xx_s = xx
             yy_s = yy
@@ -715,6 +615,76 @@ def tmp04_dust(self, par, nprec=1):
         return yy_s, xx_s
 
 
+"""
+## Duplicated. To be deprecated.
+    def get_template(self, lib, Amp:float = 1.0, T:float = 1.0, Av:float = 0.0, Z:float = 0.0, zgal:float = 1.0, f_bb:bool = False, fneb=False):
+        '''
+        Gets an element template given a set of parameters.
+        Not necessarily the most efficient way, but easy to use.
+
+        Parameters:
+        -----------
+        lib : dict
+            library dictionary.
+        Amp : float
+            Amplitude of the target template. Note that each template has Lbol = 1e10Lsun.
+        T : float
+            Age, in Gyr.
+        Av : float
+            Dust attenuation, in mag.
+        Z : float
+            Metallicity, in log(Z/Zsun).
+        zgal : float
+            Redshift.
+        f_bb: bool
+            If calculate bb photometry for the spectrum requested.
+
+        Returns
+            flux : float array. Flux in Fnu. 
+            wavelength : float array. Wave in AA.
+            lcen, lflux : , if f_bb==True.
+
+        '''
+        bfnc = self.MB.bfnc
+        DIR_TMP = self.MB.DIR_TMP 
+        NZ  = bfnc.Z2NZ(Z)
+
+        pp0 = np.random.uniform(low=0, high=self.MB.ntau0, size=(1,))
+        pp  = int(pp0[0])
+        if pp>=self.MB.ntau0:
+            pp += -1
+
+        nmodel = np.argmin(np.abs(T-self.age[:]))
+        if T - self.age[nmodel] != 0:
+            print('T=%.2f is not found in age library. T=%.2f is used.'%(T,self.age[nmodel]))
+
+        if fneb:
+            coln = int(2 + pp*len(self.ZZ)*1 + NZ*1 + 0)
+        else:
+            coln = int(2 + pp*len(self.ZZ)*len(self.AA) + NZ*len(self.AA) + nmodel)
+        nr = lib[:, 0]
+        xx = lib[:, 1] # This is OBSERVED wavelength range at z=zgal
+        yy = lib[:, coln]
+
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
+        xxd *= (1.+zgal)
+
+        if self.dust_model != 0:
+            # This may be needed when not calzetti model
+            nrd_yyd = np.zeros((len(nrd),3), dtype=float)
+            nrd_yyd[:,0] = nrd[:]
+            nrd_yyd[:,1] = yyd[:]
+            nrd_yyd[:,2] = xxd[:]
+            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
+            nrd[:],yyd[:],xxd[:] = nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
+
+        if f_bb:
+            fil_cen, fil_flux = filconv_fast(self.MB, xxd, Amp * yyd)
+            return Amp * yyd, xxd, fil_flux, fil_cen
+        else:
+            return Amp * yyd, xxd
+"""
+
 class Func_tau:
     '''
     '''
@@ -926,7 +896,7 @@ def open_spec_fits_dir(self, nage, nz, kk, Av, zgal, A00):
             return A00 * yyd_sort, xxd_sort
 
 
-    def tmp04(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, lib_all=False, f_nrd=False):
+    def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, lib_all=False, f_nrd=False):
         '''
         Makes model template with a given param set.
         Also dust attenuation.
@@ -1091,7 +1061,7 @@ def tmp04_dust(self, par, nprec=1):
         return yy_s, xx_s
 
   
-    def tmp04_neb(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, 
+    def get_template_neb(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, 
         lib_all=False, lib=None, f_nrd=False, f_apply_dust=True):
         '''
         Makes model template with a given param set.
diff --git a/gsf/plot_sed.py b/gsf/plot_sed.py
index 156d98a..6a5bd89 100644
--- a/gsf/plot_sed.py
+++ b/gsf/plot_sed.py
@@ -385,8 +385,8 @@ def gaus(x,a,x0,sigma):
         MB.lib_dust = fnc.open_spec_dust_fits(fall=0)
         MB.lib_dust_all = fnc.open_spec_dust_fits(fall=1)
     if MB.fneb:
-        lib_neb = MB.fnc.open_spec_neb_fits(fall=0)
-        lib_neb_all = MB.fnc.open_spec_neb_fits(fall=1, orig=True)
+        lib_neb = MB.fnc.open_spec_fits(fall=0, f_neb=True)
+        lib_neb_all = MB.fnc.open_spec_fits(fall=1, orig=True, f_neb=True)
 
     iimax = len(nage)-1
 
@@ -442,8 +442,9 @@ def gaus(x,a,x0,sigma):
     for jj in range(len(age)):
         ii = int(len(nage) - jj - 1) # from old to young templates.
         if jj == 0:
-            y0, x0 = fnc.tmp03(A50[ii], AAv[0], ii, Z50[ii], zbes, lib_all)
-            y0p, x0p = fnc.tmp03(A50[ii], AAv[0], ii, Z50[ii], zbes, lib)
+            y0, x0 = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib_all)
+            y0p, x0p = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib)
+
             ysum = y0
             ysump = y0p
             nopt = len(ysump)
@@ -460,14 +461,14 @@ def gaus(x,a,x0,sigma):
 
             if MB.fneb: 
                 # Only at one age pixel;
-                y0_r, x0_tmp = fnc.tmp03_neb(Aneb50, AAv[0], logU50, ii, Z50[ii], zbes, lib_neb_all)
-                y0p, x0p = fnc.tmp03_neb(Aneb50, AAv[0], logU50, ii, Z50[ii], zbes, lib_neb)
+                y0_r, x0_tmp = fnc.get_template_single(Aneb50, AAv[0], ii, Z50[ii], zbes, lib_neb_all, logU=logU50)
+                y0p, x0p = fnc.get_template_single(Aneb50, AAv[0], ii, Z50[ii], zbes, lib_neb, logU=logU50)
                 ysum += y0_r
                 ysump[:nopt] += y0p
 
         else:
-            y0_r, x0_tmp = fnc.tmp03(A50[ii], AAv[0], ii, Z50[ii], zbes, lib_all)
-            y0p, x0p = fnc.tmp03(A50[ii], AAv[0], ii, Z50[ii], zbes, lib)
+            y0_r, x0_tmp = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib_all)
+            y0p, x0p = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib)
             ysum += y0_r
             ysump[:nopt] += y0p
 
@@ -687,7 +688,7 @@ def gaus(x,a,x0,sigma):
                     ZZ_tmp = MB.ZFIX
 
             if ss == MB.aamin[0]:
-                mod0_tmp, xm_tmp = fnc.tmp03(AA_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_all)
+                mod0_tmp, xm_tmp = fnc.get_template_single(AA_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_all)
                 fm_tmp = mod0_tmp.copy()
                 fm_tmp_nl = mod0_tmp.copy()
                 if MB.fneb:
@@ -696,13 +697,13 @@ def gaus(x,a,x0,sigma):
                         logU_tmp = MB.logUFIX
                     else:
                         logU_tmp = samples['logU'][nr]
-                    mod0_tmp, xm_tmp = fnc.tmp03_neb(Aneb_tmp, Av_tmp, logU_tmp, ss, ZZ_tmp, zmc, lib_neb_all)
+                    mod0_tmp, xm_tmp = fnc.get_template_single(Aneb_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_neb_all, logU=logU_tmp)
                     fm_tmp += mod0_tmp
                     # Make no emission line template;
-                    mod0_tmp_nl, xm_tmp_nl = fnc.tmp03_neb(0, Av_tmp, logU_tmp, ss, ZZ_tmp, zmc, lib_neb_all)
+                    mod0_tmp_nl, xm_tmp_nl = fnc.get_template_single(0, Av_tmp, ss, ZZ_tmp, zmc, lib_neb_all, logU=logU_tmp)
                     fm_tmp_nl += mod0_tmp_nl
             else:
-                mod0_tmp, xx_tmp = fnc.tmp03(AA_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_all)
+                mod0_tmp, xx_tmp = fnc.get_template_single(AA_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_all)
                 fm_tmp += mod0_tmp
                 fm_tmp_nl += mod0_tmp
 
@@ -3024,15 +3025,15 @@ def density_estimation(m1, m2):
             # SED
             flim = 0.05
             if ss == 0:
-                y0, x0 = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all)
-                y0p, x0p = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib)
+                y0, x0 = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all)
+                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib)
                 ysump = y0p #* 1e18
                 ysum = y0  #* 1e18
                 if AA_tmp/Asum > flim:
                     ax0.plot(x0, y0 * c/ np.square(x0) / d, '--', lw=.1, color=col[ii], zorder=-1, label='', alpha=0.01)
             else:
-                y0_r, x0_tmp = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all)
-                y0p, x0p = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib)
+                y0_r, x0_tmp = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all)
+                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib)
                 ysump += y0p  #* 1e18
                 ysum += y0_r #* 1e18
                 if AA_tmp/Asum > flim:
@@ -3456,15 +3457,15 @@ def plot_corner_physparam_cumulative_frame(ID, Zall=np.arange(-1.2,0.4249,0.05),
             # SED
             flim = 0.05
             if ss == 0:
-                y0, x0   = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
-                y0p, x0p = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
+                y0, x0   = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
+                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
                 ysump = y0p #* 1e18
                 ysum  = y0  #* 1e18
                 if AA_tmp/Asum > flim:
                     ax0.plot(x0, y0 * c/ np.square(x0) / d, '--', lw=0.1, color=col[ii], zorder=-1, label='', alpha=0.1)
             else:
-                y0_r, x0_tmp = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
-                y0p, x0p  = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
+                y0_r, x0_tmp = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
+                y0p, x0p  = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
                 ysump += y0p  #* 1e18
                 ysum  += y0_r #* 1e18
                 if AA_tmp/Asum > flim:
@@ -3953,8 +3954,8 @@ def plot_corner_physparam_frame(ID, PA, Zall=np.arange(-1.2,0.4249,0.05), age=[0
             # SED
             flim = 0.05
             if ss == 0:
-                y0, x0   = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
-                y0p, x0p = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
+                y0, x0   = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
+                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
                 ysump = y0p #* 1e18
                 ysum  = y0  #* 1e18
                 if AA_tmp/Asum > flim:
@@ -3965,8 +3966,8 @@ def plot_corner_physparam_frame(ID, PA, Zall=np.arange(-1.2,0.4249,0.05), age=[0
                 ax1.fill_between(xx1, xx1*0, xx1*0+SF[ii], lw=1, facecolor=col[ii], zorder=1, label='', alpha=0.5)
                 #ax2.plot(age[ii], ZM[ii], marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
             else:
-                y0_r, x0_tmp = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
-                y0p, x0p     = fnc.tmp03(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
+                y0_r, x0_tmp = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
+                y0p, x0p     = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
                 ysump += y0p  #* 1e18
                 ysum  += y0_r #* 1e18
                 if AA_tmp/Asum > flim:
diff --git a/gsf/posterior_flexible.py b/gsf/posterior_flexible.py
index 0a3766e..ea6a858 100644
--- a/gsf/posterior_flexible.py
+++ b/gsf/posterior_flexible.py
@@ -38,7 +38,7 @@ def residual(self, pars, fy:float, ey:float, wht:float, f_fir:bool, out:bool=Fal
         else:
             vals = pars.valuesdict()
 
-        model, x1 = self.mb.fnc.tmp04(vals)
+        model, x1 = self.mb.fnc.get_template(vals)
 
         if self.mb.f_dust:
             model_dust, x1_dust = self.mb.fnc.tmp04_dust(vals)
@@ -52,7 +52,7 @@ def residual(self, pars, fy:float, ey:float, wht:float, f_fir:bool, out:bool=Fal
 
         if self.mb.fneb:
             n_optir = self.mb.n_optir
-            model_neb, x1_neb = self.mb.fnc.tmp04_neb(vals)
+            model_neb, x1_neb = self.mb.fnc.get_template(vals, f_neb=True)
             model[:n_optir] += model_neb
 
         if self.mb.ferr:
diff --git a/gsf/posterior_nested.py b/gsf/posterior_nested.py
index eace2c1..3ba1761 100644
--- a/gsf/posterior_nested.py
+++ b/gsf/posterior_nested.py
@@ -18,19 +18,19 @@ def __init__(self, mainbody, params=None):
 
     def residual(self, pars, fy, ey, wht, f_fir=False, out=False):
         '''
-        Input:
-        ======
+        Parameters
+        ----------
         out   : model as second output. For lnprob func.
         f_fir : Bool. If dust component is on or off.
 
-        Returns:
-        ========
+        Returns
+        -------
         residual of model and data.
 
         '''
 
         vals = pars #.valuesdict()
-        model, x1 = self.mb.fnc.tmp04(vals)
+        model, x1 = self.mb.fnc.get_template(vals)
 
         if self.mb.f_dust:
             model_dust, x1_dust = self.mb.fnc.tmp04_dust(vals)
@@ -78,19 +78,19 @@ def get_dict(self, pars):
 
     def residual_nest(self, pars, fy, ey, wht, f_fir=False, out=False):
         '''
-        Input:
-        ======
+        Parameters
+        ----------
         out   : model as second output. For lnprob func.
         f_fir : Bool. If dust component is on or off.
 
-        Returns:
-        ========
+        Returns
+        -------
         residual of model and data.
 
         '''
 
         vals = self.get_dict(pars)
-        model, x1 = self.mb.fnc.tmp04(vals)
+        model, x1 = self.mb.fnc.get_template(vals)
 
         if self.mb.f_dust:
             model_dust, x1_dust = self.mb.fnc.tmp04_dust(vals)
@@ -167,8 +167,8 @@ def prior_transform(self, pars):
 
     def lnlike(self, pars, fy, ey, wht, f_fir, f_chind=True, SNlim=1.0, lnpreject=-np.inf):
         '''
-        Returns:
-        ========
+        Returns
+        -------
         log likelihood
 
         Note:

From 1e4de8a9d78daa0c222d2f7c338a139432a6da11 Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Sat, 10 Dec 2022 14:42:33 -0800
Subject: [PATCH 06/14] docs

---
 docs/_build/doctrees/auto/gsf.doctree        | Bin 813730 -> 916799 bytes
 docs/_build/doctrees/environment.pickle      | Bin 67246 -> 68880 bytes
 docs/_build/doctrees/parameters.doctree      | Bin 55559 -> 56349 bytes
 docs/_build/html/_sources/parameters.rst.txt |   7 +-
 docs/_build/html/auto/gsf.html               | 340 ++++++++++++-------
 docs/_build/html/genindex.html               |  87 ++---
 docs/_build/html/objects.inv                 | Bin 1696 -> 1765 bytes
 docs/_build/html/parameters.html             |  30 +-
 docs/_build/html/searchindex.js              |   2 +-
 9 files changed, 277 insertions(+), 189 deletions(-)

diff --git a/docs/_build/doctrees/auto/gsf.doctree b/docs/_build/doctrees/auto/gsf.doctree
index fc4d7cf18ea0a1c72dbbd46d6c2d26a1770e34eb..e59245d17a1d269b765887833a73b57b6b1423de 100644
GIT binary patch
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diff --git a/docs/_build/doctrees/parameters.doctree b/docs/_build/doctrees/parameters.doctree
index e8d8aaa087ef40eda3b21a5ac96d20c2e612c35e..2c0131be086e151451bc9a7ea941df269ba6ae7a 100644
GIT binary patch
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diff --git a/docs/_build/html/_sources/parameters.rst.txt b/docs/_build/html/_sources/parameters.rst.txt
index 6e13098..52f1070 100644
--- a/docs/_build/html/_sources/parameters.rst.txt
+++ b/docs/_build/html/_sources/parameters.rst.txt
@@ -81,7 +81,7 @@ An example config file can be generated by executing the following command (TBD)
      - Comma separated list for spectral files. Path is relative to DIR_EXTR.
    * - MORP
      - str
-     - Profile shape, if grism spectra are provided. Will be used to convolve templates. "moffat", "gauss", or none.
+     - "moffat", "gauss", or none. Profile shape, if grism spectra are provided. Will be used to convolve templates.
    * - MORP_FILE
      - str
      - Ascii file for morphology parameters. 
@@ -136,6 +136,9 @@ An example config file can be generated by executing the following command (TBD)
    * - AMIN
      - str 
      - (Optional) Minimum value for amplitude, in normal logarithmic scale.
+   * - DUST_MODEL
+     - int
+     - Dust attenuation model (default 0). 0 for Calzetti. 1 for MW. 2 for LCM. 3 for SMC. 4 for Kriek & Conroy (2013).
    * - AVMAX
      - float 
      - (Optional) Maximum value for Av (dust attenuation in V-band), in mag.
@@ -171,7 +174,7 @@ An example config file can be generated by executing the following command (TBD)
      - 
 
 
-**Parameters for a specific target:**
+**Parameters specific for the target:**
 
 .. list-table::
    :widths: 10 5 20
diff --git a/docs/_build/html/auto/gsf.html b/docs/_build/html/auto/gsf.html
index a8a4426..3228c32 100644
--- a/docs/_build/html/auto/gsf.html
+++ b/docs/_build/html/auto/gsf.html
@@ -144,7 +144,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 </dl>
 <dl class="py class">
 <dt class="sig sig-object py" id="gsf.fitting.Mainbody">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">gsf.fitting.</span></span><span class="sig-name descname"><span class="pre">Mainbody</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3e+18</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Mpc_cm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3.08568025e+24</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m0set</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixelscale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Lsun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3.839e+33</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cosmo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">gsf.fitting.</span></span><span class="sig-name descname"><span class="pre">Mainbody</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3e+18</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Mpc_cm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3.08568025e+24</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m0set</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">25.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixelscale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Lsun</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3.839e+33</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cosmo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody" title="Permalink to this definition"></a></dt>
 <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
 <p>The list of (possible) <cite>Mainbody</cite> attributes is given below:</p>
 <dl class="simple">
@@ -169,10 +169,14 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.fitting.Mainbody.fit_redshift">
-<span class="sig-name descname"><span class="pre">fit_redshift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delzz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ezmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zliml</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zlimu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">snlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_bb_zfit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_line_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.fit_redshift" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">fit_redshift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delzz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ezmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zliml</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zlimu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">snlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_bb_zfit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_line_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_lambda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_bb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_exclude_negative</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.fit_redshift" title="Permalink to this definition"></a></dt>
 <dd><p>Find the best-fit redshift, before going into a big fit, through an interactive inspection.
-This module is effective only when spec data is provided.</p>
+This module is effective only when spec data is provided.
+When spectrun is provided, this does redshift fit, but <strong>by using the SED model guessed from the BB photometry</strong>.
+Thus, providing good BB photometry is critical in this step.</p>
 <dl class="simple">
+<dt>xm_tmp, fm_tmp<span class="classifier">float array</span></dt><dd><p>SED model.</p>
+</dd>
 <dt>delzz<span class="classifier">float</span></dt><dd><p>Delta z in redshift search space</p>
 </dd>
 <dt>zliml<span class="classifier">float</span></dt><dd><p>Lower limit range for redshift</p>
@@ -183,12 +187,14 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 </dd>
 <dt>snlim<span class="classifier">float</span></dt><dd><p>SN limit for data points. Those below the number will be cut from the fit.</p>
 </dd>
-<dt>f_bb_zfit<span class="classifier">bool</span></dt><dd><p>Redshift fitting if only BB data. If False, returns nothing.</p>
+<dt>include_bb<span class="classifier">bool</span></dt><dd><p>Turn this True if Redshift fitting is requred for only BB data.</p>
 </dd>
 <dt>f_line_check<span class="classifier">bool</span></dt><dd><p>If True, line masking.</p>
 </dd>
 <dt>priors<span class="classifier">dict, optional</span></dt><dd><p>Dictionary that contains z (redshift grid) and chi2 (chi-square).</p>
 </dd>
+<dt>f_exclude_negative<span class="classifier">bool</span></dt><dd><p>Exclude negative fluxes in spectrum.</p>
+</dd>
 </dl>
 <p>Spectral data must be provided to make this work.</p>
 </dd></dl>
@@ -206,7 +212,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.fitting.Mainbody.get_zdist">
-<span class="sig-name descname"><span class="pre">get_zdist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f_interact</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.get_zdist" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">get_zdist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f_interact</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_ascii</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.get_zdist" title="Permalink to this definition"></a></dt>
 <dd><p>Saves a plot of z-distribution.</p>
 <dl class="simple">
 <dt>f_interact<span class="classifier">bool</span></dt><dd><p>If true, this module returns figure ax.</p>
@@ -216,7 +222,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.fitting.Mainbody.main">
-<span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cornerplot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">specplot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ezmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ferr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_move</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_fitz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_plot_accept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amp_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_converge</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Zini</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_plot_chain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.main" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cornerplot</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">specplot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ezmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ferr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_move</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_fitz</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_plot_accept</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_shuffle</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amp_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_converge</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Zini</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_plot_chain</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_chind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ncpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_prior_sfh</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norder_sfh_prior</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_bb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.main" title="Permalink to this definition"></a></dt>
 <dd><p>Main module of this script.</p>
 <dl class="simple">
 <dt>ferr<span class="classifier">float</span></dt><dd><p>Used for error parameter.</p>
@@ -243,21 +249,11 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <span class="sig-name descname"><span class="pre">prepare_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">add_fir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.prepare_class" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="gsf.fitting.Mainbody.quick_fit">
-<span class="sig-name descname"><span class="pre">quick_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">specplot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ezmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ferr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_move</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_get_templates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Zini</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.quick_fit" title="Permalink to this definition"></a></dt>
-<dd><p>Fits input data with a prepared template library, to get a chi-min result.</p>
-<dl class="simple">
-<dt>Zini<span class="classifier">array</span></dt><dd><p>Array for initial values for metallicity.</p>
-</dd>
-</dl>
-<p>out, chidef, Zbest, xm_tmp, fm_tmp (if f_get_templates is set True).</p>
-</dd></dl>
-
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.fitting.Mainbody.read_data">
-<span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Cz0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Cz1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zgal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_fir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.read_data" title="Permalink to this definition"></a></dt>
-<dd><dl class="simple">
+<span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Cz0</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Cz1</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Cz2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zgal</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_fir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.read_data" title="Permalink to this definition"></a></dt>
+<dd><p>Read in observed data. Not model fluxes.</p>
+<dl class="simple">
 <dt>Cz0, Cz1<span class="classifier">float</span></dt><dd><p>Normalization coefficients for grism spectra. Cz0 for G102, Cz1 for G141.</p>
 </dd>
 <dt>zgal<span class="classifier">float</span></dt><dd><p>Current redshift estimate.</p>
@@ -269,7 +265,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.fitting.Mainbody.search_redshift">
-<span class="sig-name descname"><span class="pre">search_redshift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zliml</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zlimu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delzz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'powell'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.search_redshift" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">search_redshift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zliml</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zlimu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">6.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delzz</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lines</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'powell'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.search_redshift" title="Permalink to this definition"></a></dt>
 <dd><p>This module explores the redshift space to find the best redshift and probability distribution.</p>
 <dl class="simple">
 <dt>dict<span class="classifier">dictionary</span></dt><dd><p>Dictionary that includes input data.</p>
@@ -304,7 +300,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.fitting.Mainbody.update_input">
-<span class="sig-name descname"><span class="pre">update_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3e+18</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Mpc_cm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3.08568025e+24</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m0set</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixelscale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Lsun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3.839e+33</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cosmo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.update_input" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">update_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3e+18</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Mpc_cm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3.08568025e+24</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m0set</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">25.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixelscale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Lsun</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3.839e+33</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cosmo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigz</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody.update_input" title="Permalink to this definition"></a></dt>
 <dd><p>The purpose of this module is to register/update the parameter attributes in <cite>Mainbody</cite>
 by visiting the configuration file.</p>
 <dl class="simple">
@@ -338,6 +334,15 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dd><p>SFH</p>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="gsf.function.apply_dust">
+<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">apply_dust</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">yy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Av</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dust_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.apply_dust" title="Permalink to this definition"></a></dt>
+<dd><dl class="simple">
+<dt>xx<span class="classifier">float array</span></dt><dd><p>RF Wavelength</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function.calc_Dn4">
 <span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">calc_Dn4</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.calc_Dn4" title="Permalink to this definition"></a></dt>
@@ -398,6 +403,11 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 </dl>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="gsf.function.delta">
+<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">delta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">A</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.delta" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function.detect_line">
 <span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">detect_line</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xcont</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ycont</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wycont</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zgal</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.detect_line" title="Permalink to this definition"></a></dt>
@@ -410,7 +420,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function.dust_calz">
-<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">dust_calz</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fl</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Av</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Rv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lmlimu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3.115</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alam</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.dust_calz" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">dust_calz</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fl</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Av</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Rv</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">4.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lmlimu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3.115</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alam</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.dust_calz" title="Permalink to this definition"></a></dt>
 <dd><dl class="simple">
 <dt>lm<span class="classifier">float array</span></dt><dd><p>wavelength, at RF.</p>
 </dd>
@@ -501,11 +511,11 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dt class="sig sig-object py" id="gsf.function.filconv">
 <span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">filconv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">band0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DIR</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_regist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">MB</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.filconv" title="Permalink to this definition"></a></dt>
 <dd><dl class="simple">
-<dt>f0<span class="classifier"></span></dt><dd><p>Flux for spectrum, in fnu</p>
+<dt>f0<span class="classifier">float array</span></dt><dd><p>Flux for spectrum, in fnu</p>
 </dd>
-<dt>l0<span class="classifier"></span></dt><dd><p>Wavelength for spectrum, in AA (that matches filter response curve’s.)</p>
+<dt>l0<span class="classifier">float array</span></dt><dd><p>Wavelength for spectrum, in AA (that matches filter response curve’s.)</p>
 </dd>
-<dt>f_regist<span class="classifier"></span></dt><dd><p>If True, read filter response curve.</p>
+<dt>f_regist<span class="classifier">bool</span></dt><dd><p>If True, read filter response curves and register those to MB.</p>
 </dd>
 </dl>
 </dd></dl>
@@ -548,8 +558,14 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function.fnutolam">
-<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">fnutolam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fnu</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m0set</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.fnutolam" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">fnutolam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fnu</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m0set</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">48.6</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.fnutolam" title="Permalink to this definition"></a></dt>
 <dd><p>Converts from Fnu to Flam, from mag zeropoint of m0set (to -48.6).</p>
+<dl class="simple">
+<dt>m0set<span class="classifier">float</span></dt><dd><p>current magzp.</p>
+</dd>
+<dt>m0<span class="classifier">float</span></dt><dd><p>target magzp. The default, -48.6, is for flam (erg/s/cm2/lambda).</p>
+</dd>
+</dl>
 </dd></dl>
 
 <dl class="py function">
@@ -596,13 +612,14 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function.get_Fuv">
 <span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">get_Fuv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lmtmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ftmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1400</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1500</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.get_Fuv" title="Permalink to this definition"></a></dt>
-<dd><dl class="simple">
+<dd><p>Get RF UV (or any wavelength) flux density.</p>
+<dl class="simple">
 <dt>lmtmp<span class="classifier"></span></dt><dd><p>Rest-frame wavelength, in AA.</p>
 </dd>
 <dt>ftmp<span class="classifier"></span></dt><dd><p>Fnu</p>
 </dd>
 </dl>
-<p>Convolved flux density. Not integrated sum.</p>
+<p>Flux density estimated over lmin:lmax. Not integrated sum.</p>
 </dd></dl>
 
 <dl class="py function">
@@ -627,14 +644,9 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">get_chi2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ey</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wht3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ysump</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ndim_eff</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">SNlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_chind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xbb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_ex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.get_chi2" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
-<dl class="py function">
-<dt class="sig sig-object py" id="gsf.function.get_filt">
-<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">get_filt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">LIBFILT</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">NFILT</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.get_filt" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
-
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function.get_fit">
-<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">get_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">yer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsfh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Del.'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.get_fit" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">get_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">yer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsfh</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'Del.'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.get_fit" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
@@ -652,10 +664,22 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function.get_leastsq">
-<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">get_leastsq</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MB</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ZZtmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fneld</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">age</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">residual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ey</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wht</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ID0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chidef</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Zbest</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_keep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.get_leastsq" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">get_leastsq</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MB</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ZZtmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fneld</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">age</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">residual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ey</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wht</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ID0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chidef</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Zbest</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_keep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_only_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.get_leastsq" title="Permalink to this definition"></a></dt>
 <dd><p>Get initial parameters at various Z</p>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="gsf.function.get_uvbeta">
+<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">get_uvbeta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zbes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam_blue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1650</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam_red</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2300</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.get_uvbeta" title="Permalink to this definition"></a></dt>
+<dd><p>get UV beta_lambda slope.</p>
+<dl class="simple">
+<dt>lm<span class="classifier">float array</span></dt><dd><p>in lambda</p>
+</dd>
+<dt>flam<span class="classifier">float array</span></dt><dd><p>in flambda</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function.loadcpkl">
 <span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">loadcpkl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cpklfile</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.loadcpkl" title="Permalink to this definition"></a></dt>
@@ -704,6 +728,11 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dd><p>Save data into cpklfile.</p>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="gsf.function.str2bool">
+<span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">str2bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.str2bool" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function.write_input">
 <span class="sig-prename descclassname"><span class="pre">gsf.function.</span></span><span class="sig-name descname"><span class="pre">write_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gsf.input'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function.write_input" title="Permalink to this definition"></a></dt>
@@ -716,79 +745,93 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <span id="gsf-function-class-module"></span><h2>gsf.function_class module<a class="headerlink" href="#module-gsf.function_class" title="Permalink to this headline"></a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="gsf.function_class.Func">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">gsf.function_class.</span></span><span class="sig-name descname"><span class="pre">Func</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MB</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dust_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">gsf.function_class.</span></span><span class="sig-name descname"><span class="pre">Func</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MB</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dust_model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func" title="Permalink to this definition"></a></dt>
 <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
 <p>The list of (possible) <cite>Func</cite> attributes is given below:</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.function_class.Func.get_template">
-<span class="sig-name descname"><span class="pre">get_template</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lib</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Amp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Av</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zgal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_bb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.get_template" title="Permalink to this definition"></a></dt>
-<dd><p>Gets an element template given a set of parameters.
-Not necessarily the most efficient way, but easy to use.</p>
+<span class="sig-name descname"><span class="pre">get_template</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_nrd</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_apply_dust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_IGM</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deltaz_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_neb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EBVratio</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2.27</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.get_template" title="Permalink to this definition"></a></dt>
+<dd><p>Makes model template for a nebular emission.
+This is only for nubular template, with len(A) = 1. Thus, aa=0 during the code.</p>
 <dl class="simple">
-<dt>lib<span class="classifier">dict</span></dt><dd><p>library dictionary.</p>
+<dt>nprec<span class="classifier">int</span></dt><dd><p>Precision when redshift is refined.</p>
 </dd>
-<dt>Amp<span class="classifier">float</span></dt><dd><p>Amplitude of the target template. Note that each template has Lbol = 1e10Lsun.</p>
+<dt>f_apply_dust<span class="classifier">bool</span></dt><dd><p>Apply dust attenuation to nebular emission?</p>
 </dd>
-<dt>T<span class="classifier">float</span></dt><dd><p>Age, in Gyr.</p>
+<dt>EBVratio<span class="classifier">float</span></dt><dd><p>E(B-V)_neb / E(B-V)_st. 
+Useful table in <a class="reference external" href="https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf">https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf</a></p>
 </dd>
-<dt>Av<span class="classifier">float</span></dt><dd><p>Dust attenuation, in mag.</p>
+<dt>f_neb<span class="classifier">bool</span></dt><dd><p>Expect to explore nebular template or not.</p>
 </dd>
-<dt>Z<span class="classifier">float</span></dt><dd><p>Metallicity, in log(Z/Zsun).</p>
+</dl>
+<p>This function is only used in plot_sed.py.
+Common function for mebular and nonnebular temlates.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="gsf.function_class.Func.get_template_single">
+<span class="sig-name descname"><span class="pre">get_template_single</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A00</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Av</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nmodel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zgal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logU</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_apply_dust</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EBVratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.27</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.get_template_single" title="Permalink to this definition"></a></dt>
+<dd><dl class="simple">
+<dt>EBVratio<span class="classifier">float</span></dt><dd><p>E(B-V)_neb / E(B-V)_st. 
+Useful table in <a class="reference external" href="https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf">https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf</a></p>
 </dd>
-<dt>zgal<span class="classifier">float</span></dt><dd><p>Redshift.</p>
+</dl>
+<p>This function is only used in plot_sed.py.
+Common function for mebular and nonnebular temlates.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="gsf.function_class.Func.get_total_flux">
+<span class="sig-name descname"><span class="pre">get_total_flux</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_get_Mtot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_check_limit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.get_total_flux" title="Permalink to this definition"></a></dt>
+<dd><p>get total flux for a given set of parameter.</p>
+<dl class="simple">
+<dt>par<span class="classifier">library</span></dt><dd><p>contains parameters. Needs to include: [‘Z’,’A’]</p>
 </dd>
-<dt>f_bb: bool</dt><dd><p>If calculate bb photometry for the spectrum requested.</p>
+</dl>
+<dl class="simple">
+<dt>xx<span class="classifier"></span></dt><dd><p>OBSERVED wavelength range at z=MB.zgal</p>
 </dd>
-<dt>Returns</dt><dd><p>flux : float array. Flux in Fnu. 
-wavelength : float array. Wave in AA.
-lcen, lflux : , if f_bb==True.</p>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="gsf.function_class.Func.get_total_flux_neb">
+<span class="sig-name descname"><span class="pre">get_total_flux_neb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.get_total_flux_neb" title="Permalink to this definition"></a></dt>
+<dd><p>get total flux for a given set of parameter.</p>
+<dl class="simple">
+<dt>par<span class="classifier">library</span></dt><dd><p>contains parameters</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.function_class.Func.open_spec_dust_fits">
-<span class="sig-name descname"><span class="pre">open_spec_dust_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.open_spec_dust_fits" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">open_spec_dust_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.open_spec_dust_fits" title="Permalink to this definition"></a></dt>
 <dd><p>Loads dust template in obs range.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.function_class.Func.open_spec_fits">
-<span class="sig-name descname"><span class="pre">open_spec_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.open_spec_fits" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">open_spec_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orig</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_neb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.open_spec_fits" title="Permalink to this definition"></a></dt>
 <dd><p>Load template in obs range.</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func.open_spec_fits_dir">
-<span class="sig-name descname"><span class="pre">open_spec_fits_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kk</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Av00</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zgal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">A00</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.open_spec_fits_dir" title="Permalink to this definition"></a></dt>
-<dd><p>Load template in obs range.
-But for weird template.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func.open_spec_neb_fits">
-<span class="sig-name descname"><span class="pre">open_spec_neb_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.open_spec_neb_fits" title="Permalink to this definition"></a></dt>
-<dd><p>Loads template in obs range.</p>
+<dt class="sig sig-object py" id="gsf.function_class.Func.tmp04_dust">
+<span class="sig-name descname"><span class="pre">tmp04_dust</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.tmp04_dust" title="Permalink to this definition"></a></dt>
+<dd><p>Makes model template with a given param setself.
+Also dust attenuation.</p>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func.tmp03">
-<span class="sig-name descname"><span class="pre">tmp03</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A00</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Av00</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nmodel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zgal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.tmp03" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func.tmp03_neb">
-<span class="sig-name descname"><span class="pre">tmp03_neb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A00</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Av00</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logU</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nmodel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zgal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_apply_dust</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EBVratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.27</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.tmp03_neb" title="Permalink to this definition"></a></dt>
-<dd><dl class="simple">
-<dt>EBVratio<span class="classifier">float</span></dt><dd><p>E(B-V)_neb / E(B-V)_st. 
-Useful table in <a class="reference external" href="https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf">https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf</a></p>
-</dd>
-</dl>
 </dd></dl>
 
+<dl class="py class">
+<dt class="sig sig-object py" id="gsf.function_class.Func_tau">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">gsf.function_class.</span></span><span class="sig-name descname"><span class="pre">Func_tau</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MB</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dust_model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
 <dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func.tmp04">
-<span class="sig-name descname"><span class="pre">tmp04</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_nrd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.tmp04" title="Permalink to this definition"></a></dt>
+<dt class="sig sig-object py" id="gsf.function_class.Func_tau.get_template">
+<span class="sig-name descname"><span class="pre">get_template</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_nrd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.get_template" title="Permalink to this definition"></a></dt>
 <dd><p>Makes model template with a given param set.
 Also dust attenuation.</p>
 <dl class="simple">
@@ -798,48 +841,31 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func.tmp04_dust">
-<span class="sig-name descname"><span class="pre">tmp04_dust</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.tmp04_dust" title="Permalink to this definition"></a></dt>
-<dd><p>Makes model template with a given param setself.
+<dt class="sig sig-object py" id="gsf.function_class.Func_tau.get_template_neb">
+<span class="sig-name descname"><span class="pre">get_template_neb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_nrd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_apply_dust</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.get_template_neb" title="Permalink to this definition"></a></dt>
+<dd><p>Makes model template with a given param set.
 Also dust attenuation.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func.tmp04_neb">
-<span class="sig-name descname"><span class="pre">tmp04_neb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_nrd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_apply_dust</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EBVratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.27</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.tmp04_neb" title="Permalink to this definition"></a></dt>
-<dd><p>Makes model template for a nebular emission.</p>
 <dl class="simple">
 <dt>nprec<span class="classifier">int</span></dt><dd><p>Precision when redshift is refined.</p>
 </dd>
-<dt>f_apply_dust<span class="classifier">bool</span></dt><dd><p>Apply dust attenuation to nebular emission?</p>
-</dd>
-<dt>EBVratio<span class="classifier">float</span></dt><dd><p>E(B-V)_neb / E(B-V)_st. 
-Useful table in <a class="reference external" href="https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf">https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf</a></p>
-</dd>
 </dl>
 </dd></dl>
 
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="gsf.function_class.Func_tau">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">gsf.function_class.</span></span><span class="sig-name descname"><span class="pre">Func_tau</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MB</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dust_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau" title="Permalink to this definition"></a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.function_class.Func_tau.open_spec_dust_fits">
-<span class="sig-name descname"><span class="pre">open_spec_dust_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.open_spec_dust_fits" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">open_spec_dust_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.open_spec_dust_fits" title="Permalink to this definition"></a></dt>
 <dd><p>Load dust template in obs range.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.function_class.Func_tau.open_spec_fits">
-<span class="sig-name descname"><span class="pre">open_spec_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.open_spec_fits" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">open_spec_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orig</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.open_spec_fits" title="Permalink to this definition"></a></dt>
 <dd><p>Loads template in obs range.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.function_class.Func_tau.open_spec_fits_dir">
-<span class="sig-name descname"><span class="pre">open_spec_fits_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kk</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Av00</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zgal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">A00</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.open_spec_fits_dir" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">open_spec_fits_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kk</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Av</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zgal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">A00</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.open_spec_fits_dir" title="Permalink to this definition"></a></dt>
 <dd><p>Loads template in obs range.
 But for weird template.</p>
 </dd></dl>
@@ -850,17 +876,6 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dd><p>Loads template in obs range.</p>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func_tau.tmp04">
-<span class="sig-name descname"><span class="pre">tmp04</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_nrd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.tmp04" title="Permalink to this definition"></a></dt>
-<dd><p>Makes model template with a given param set.
-Also dust attenuation.</p>
-<dl class="simple">
-<dt>nprec<span class="classifier">int</span></dt><dd><p>Precision when redshift is refined.</p>
-</dd>
-</dl>
-</dd></dl>
-
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.function_class.Func_tau.tmp04_dust">
 <span class="sig-name descname"><span class="pre">tmp04_dust</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.tmp04_dust" title="Permalink to this definition"></a></dt>
@@ -868,38 +883,57 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 Also dust attenuation.</p>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func_tau.tmp04_neb">
-<span class="sig-name descname"><span class="pre">tmp04_neb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_nrd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_apply_dust</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.tmp04_neb" title="Permalink to this definition"></a></dt>
-<dd><p>Makes model template with a given param set.
-Also dust attenuation.</p>
-<dl class="simple">
-<dt>nprec<span class="classifier">int</span></dt><dd><p>Precision when redshift is refined.</p>
-</dd>
-</dl>
-</dd></dl>
-
 </dd></dl>
 
 </section>
 <section id="module-gsf.function_igm">
 <span id="gsf-function-igm-module"></span><h2>gsf.function_igm module<a class="headerlink" href="#module-gsf.function_igm" title="Permalink to this headline"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="gsf.function_igm.dijkstra_igm_abs">
+<span class="sig-prename descclassname"><span class="pre">gsf.function_igm.</span></span><span class="sig-name descname"><span class="pre">dijkstra_igm_abs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xtmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ytmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cosmo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xLL</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1216.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ckms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">R_b1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_v_0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">600</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_x</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_igm.dijkstra_igm_abs" title="Permalink to this definition"></a></dt>
+<dd><p>Apply IMG-attenuation of Dijikstra (2014).
+<a class="reference external" href="https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1323358014000332">https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1323358014000332</a></p>
+<dl class="simple">
+<dt>xtmp<span class="classifier">float array</span></dt><dd><p>Rest-frame wavelength, in AA.</p>
+</dd>
+<dt>ytmp<span class="classifier">float array</span></dt><dd><p>flux, in f_lambda.</p>
+</dd>
+<dt>zin<span class="classifier"></span></dt><dd><p>target redshift of IGM application</p>
+</dd>
+<dt>R_b1<span class="classifier">float</span></dt><dd><p>Bubble size, in Mpc</p>
+</dd>
+</dl>
+<p>IGM attenuated flux.</p>
+</dd></dl>
+
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function_igm.get_H">
 <span class="sig-prename descclassname"><span class="pre">gsf.function_igm.</span></span><span class="sig-name descname"><span class="pre">get_H</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_igm.get_H" title="Permalink to this definition"></a></dt>
 <dd><p>Voigt function</p>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="gsf.function_igm.get_XI">
+<span class="sig-prename descclassname"><span class="pre">gsf.function_igm.</span></span><span class="sig-name descname"><span class="pre">get_XI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_igm.get_XI" title="Permalink to this definition"></a></dt>
+<dd><p>Very simplified model.</p>
+</dd></dl>
+
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function_igm.get_column">
 <span class="sig-prename descclassname"><span class="pre">gsf.function_igm.</span></span><span class="sig-name descname"><span class="pre">get_column</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">zin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cosmo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Mpc_cm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3.08568025e+24</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z_r</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_igm.get_column" title="Permalink to this definition"></a></dt>
-<dd><p>HI column density of IGM at zin, in cm^-3.</p>
+<dd><p>HI column density of IGM at zin, in cm^-2.</p>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="gsf.function_igm.get_dtdz">
+<span class="sig-prename descclassname"><span class="pre">gsf.function_igm.</span></span><span class="sig-name descname"><span class="pre">get_dtdz</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">z</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtdzs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_igm.get_dtdz" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.function_igm.get_nH">
 <span class="sig-prename descclassname"><span class="pre">gsf.function_igm.</span></span><span class="sig-name descname"><span class="pre">get_nH</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">z</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_igm.get_nH" title="Permalink to this definition"></a></dt>
-<dd><p>HI density in IGM.</p>
+<dd><p>Get HI density by using Cen &amp; Haiman 2000.</p>
+<p>HI density in IGM, in cm^-3</p>
 </dd></dl>
 
 <dl class="py function">
@@ -925,12 +959,25 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <p>IGM attenuated flux.</p>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="gsf.function_igm.masongronke_igm_abs">
+<span class="sig-prename descclassname"><span class="pre">gsf.function_igm.</span></span><span class="sig-name descname"><span class="pre">masongronke_igm_abs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xtmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ytmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cosmo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xLL</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1216.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3e+18</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ckms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xLLL</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1400</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_igm.masongronke_igm_abs" title="Permalink to this definition"></a></dt>
+<dd><dl class="simple">
+<dt>xtmp<span class="classifier">float array</span></dt><dd><p>Rest-frame wavelength, in AA.</p>
+</dd>
+<dt>ytmp<span class="classifier">float array</span></dt><dd><p>flux, in f_lambda.</p>
+</dd>
+</dl>
+<p>zin : target redshift of IGM application</p>
+<p>IGM attenuated flux.</p>
+</dd></dl>
+
 </section>
 <section id="module-gsf.gsf">
 <span id="gsf-gsf-module"></span><h2>gsf.gsf module<a class="headerlink" href="#module-gsf.gsf" title="Permalink to this headline"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.gsf.run_gsf_all">
-<span class="sig-prename descclassname"><span class="pre">gsf.gsf.</span></span><span class="sig-name descname"><span class="pre">run_gsf_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parfile</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fplt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cornerplot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_symbol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_SFMS</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_fill</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_sed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figpdf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_sfh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_fancyplot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_zhist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tset_SFR_SED</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amp_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Zini</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nthin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delwave</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.gsf.run_gsf_all" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">gsf.gsf.</span></span><span class="sig-name descname"><span class="pre">run_gsf_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parfile</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fplt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cornerplot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_symbol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_SFMS</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_fill</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_sed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figpdf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_sfh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_fancyplot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_zhist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tset_SFR_SED</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amp_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Zini</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nthin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delwave</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_plot_resid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-19</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_plot_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_prior_sfh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norder_sfh_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.gsf.run_gsf_all" title="Permalink to this definition"></a></dt>
 <dd><p>gsf pipeline, which runs all steps.</p>
 <dl class="simple">
 <dt>delwave<span class="classifier">float</span></dt><dd><p>If &gt;0, the input templates get smoothing to delwave. 
@@ -964,7 +1011,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.maketmp_filt.get_spectrum_draine">
-<span class="sig-prename descclassname"><span class="pre">gsf.maketmp_filt.</span></span><span class="sig-name descname"><span class="pre">get_spectrum_draine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lambda_d</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DL</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zbest</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">numin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">numax</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ndmodel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DIR_DUST</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'./DL07spec/'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.055</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.maketmp_filt.get_spectrum_draine" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">gsf.maketmp_filt.</span></span><span class="sig-name descname"><span class="pre">get_spectrum_draine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lambda_d</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DL</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zbest</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">numin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">numax</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ndmodel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DIR_DUST</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'./DL07spec/'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.055</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m0set</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.maketmp_filt.get_spectrum_draine" title="Permalink to this definition"></a></dt>
 <dd><dl class="simple">
 <dt>lambda_d<span class="classifier">array</span></dt><dd><p>Wavelength array, in AA.</p>
 </dd>
@@ -981,7 +1028,8 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.maketmp_filt.maketemp">
 <span class="sig-prename descclassname"><span class="pre">gsf.maketmp_filt.</span></span><span class="sig-name descname"><span class="pre">maketemp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MB</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ebblim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000000000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lamliml</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lamlimu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ncolbb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmp_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000000000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nthin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delwave</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lammax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_IGM</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.maketmp_filt.maketemp" title="Permalink to this definition"></a></dt>
-<dd><p>Make SPECTRA at given z and filter set.</p>
+<dd><p>Make SPECTRA at given z and filter set.
+Also, after v1.8, through this function library and data are register to the main class object, MB.</p>
 <dl class="simple">
 <dt>inputs<span class="classifier">str</span></dt><dd><p>Configuration file.</p>
 </dd>
@@ -1038,7 +1086,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <span id="gsf-maketmp-z0-module"></span><h2>gsf.maketmp_z0 module<a class="headerlink" href="#module-gsf.maketmp_z0" title="Permalink to this headline"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.maketmp_z0.make_tmp_z0">
-<span class="sig-prename descclassname"><span class="pre">gsf.maketmp_z0.</span></span><span class="sig-name descname"><span class="pre">make_tmp_z0</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MB</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lammin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lammax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">160000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_no_neb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Zforce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.maketmp_z0.make_tmp_z0" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">gsf.maketmp_z0.</span></span><span class="sig-name descname"><span class="pre">make_tmp_z0</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MB</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lammin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lammax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">160000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_no_neb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Zforce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_mp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.maketmp_z0.make_tmp_z0" title="Permalink to this definition"></a></dt>
 <dd><p>This is for the preparation of default template, with FSPS, at z=0.
 Should be run before SED fitting.</p>
 <dl class="simple">
@@ -2116,9 +2164,16 @@ <h2>gsf.plot_sfh_logA module<a class="headerlink" href="#gsf-plot-sfh-loga-modul
 <dd><p>A function used for chi2 calculation for non-detection in lnprob.</p>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="gsf.posterior_flexible.Post.get_sfh_prior">
+<span class="sig-name descname"><span class="pre">get_sfh_prior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.posterior_flexible.Post.get_sfh_prior" title="Permalink to this definition"></a></dt>
+<dd><p>Fit SFH with an n-order polynomial and reflect the residual to the prior.
+Fit should be done in log-log space.</p>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.posterior_flexible.Post.lnprob_emcee">
-<span class="sig-name descname"><span class="pre">lnprob_emcee</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pos</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pars</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ey</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wht</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_fir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_chind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">SNlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lnpreject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_like</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gauss_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.posterior_flexible.Post.lnprob_emcee" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">lnprob_emcee</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pos</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pars</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ey</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wht</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_fir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_chind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">SNlim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_scale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lnpreject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_like</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat_prior</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gauss_prior</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_prior_sfh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha_sfh_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norder_sfh_prior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.posterior_flexible.Post.lnprob_emcee" title="Permalink to this definition"></a></dt>
 <dd><dl class="simple">
 <dt>f_chind<span class="classifier">bool</span></dt><dd><p>If true, includes non-detection in likelihood calculation.</p>
 </dd>
@@ -2139,7 +2194,7 @@ <h2>gsf.plot_sfh_logA module<a class="headerlink" href="#gsf-plot-sfh-loga-modul
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.posterior_flexible.Post.residual">
-<span class="sig-name descname"><span class="pre">residual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pars</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ey</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wht</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_fir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_penlize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.posterior_flexible.Post.residual" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">residual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pars</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ey</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wht</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_fir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_penlize</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_only_spec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.posterior_flexible.Post.residual" title="Permalink to this definition"></a></dt>
 <dd><dl class="simple">
 <dt>out<span class="classifier"></span></dt><dd><p>model as second output. For lnprob func.</p>
 </dd>
@@ -2246,7 +2301,7 @@ <h2>gsf.version module<a class="headerlink" href="#gsf-version-module" title="Pe
 <span id="gsf-zfit-module"></span><h2>gsf.zfit module<a class="headerlink" href="#module-gsf.zfit" title="Permalink to this headline"></a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="gsf.zfit.check_redshift">
-<span class="sig-prename descclassname"><span class="pre">gsf.zfit.</span></span><span class="sig-name descname"><span class="pre">check_redshift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fobs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eobs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xobs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zbest</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zprior</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">NR</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zliml</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zlimu</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nmc_cz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nwalk_cz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nthin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_line_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_vary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.zfit.check_redshift" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">gsf.zfit.</span></span><span class="sig-name descname"><span class="pre">check_redshift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fobs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eobs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xobs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zbest</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zprior</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">NR</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zliml</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zlimu</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nmc_cz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nwalk_cz</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nthin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_line_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_vary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">NRbb_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_photometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.zfit.check_redshift" title="Permalink to this definition"></a></dt>
 <dd><p>Fit observed flux with a template to get redshift probability.</p>
 <dl class="simple">
 <dt>zbest<span class="classifier"></span></dt><dd><p>Initial value for redshift.</p>
@@ -2276,6 +2331,31 @@ <h2>gsf.version module<a class="headerlink" href="#gsf-version-module" title="Pe
 <p>fitc_cz :</p>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="gsf.zfit.get_chi2">
+<span class="sig-prename descclassname"><span class="pre">gsf.zfit.</span></span><span class="sig-name descname"><span class="pre">get_chi2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">zz_prob</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fy_cz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ey_cz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_cz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_zprob</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rms_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.zfit.get_chi2" title="Permalink to this definition"></a></dt>
+<dd><dl class="simple">
+<dt>zz_prob<span class="classifier">float array</span></dt><dd><p>redshift array for fit.</p>
+</dd>
+<dt>fy_cz, ey_cz, x_cz<span class="classifier"></span></dt><dd><p>observed values.</p>
+</dd>
+<dt>fm_tmp, xm_tmp :</dt><dd><p>template.</p>
+</dd>
+<dt>file_zprob<span class="classifier">str</span></dt><dd><p>output file</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="gsf.zfit.lnprob_cz">
+<span class="sig-prename descclassname"><span class="pre">gsf.zfit.</span></span><span class="sig-name descname"><span class="pre">lnprob_cz</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pars</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zprior</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zliml</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zlimu</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.zfit.lnprob_cz" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="gsf.zfit.residual_z">
+<span class="sig-prename descclassname"><span class="pre">gsf.zfit.</span></span><span class="sig-name descname"><span class="pre">residual_z</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pars</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fm_tmp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xobs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fobs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eobs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">NR</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">NRbb_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_photometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_line_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.zfit.residual_z" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
 </section>
 <section id="module-gsf">
 <span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-gsf" title="Permalink to this headline"></a></h2>
diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html
index bf75c3e..81acd0d 100644
--- a/docs/_build/html/genindex.html
+++ b/docs/_build/html/genindex.html
@@ -88,7 +88,6 @@ <h1 id="index">Index</h1>
  | <a href="#M"><strong>M</strong></a>
  | <a href="#O"><strong>O</strong></a>
  | <a href="#P"><strong>P</strong></a>
- | <a href="#Q"><strong>Q</strong></a>
  | <a href="#R"><strong>R</strong></a>
  | <a href="#S"><strong>S</strong></a>
  | <a href="#T"><strong>T</strong></a>
@@ -108,6 +107,8 @@ <h2 id="A">A</h2>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="auto/gsf.html#gsf.minimizer.Minimizer.ampgo">ampgo() (gsf.minimizer.Minimizer method)</a>
+</li>
+      <li><a href="auto/gsf.html#gsf.function.apply_dust">apply_dust() (in module gsf.function)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.minimizer.asteval_with_uncertainties">asteval_with_uncertainties() (in module gsf.minimizer)</a>
 </li>
@@ -160,15 +161,19 @@ <h2 id="D">D</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="auto/gsf.html#gsf.function.data_int">data_int() (in module gsf.function)</a>
+</li>
+      <li><a href="auto/gsf.html#gsf.function.delta">delta() (in module gsf.function)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.function.detect_line">detect_line() (in module gsf.function)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.function.detect_line_man">detect_line_man() (in module gsf.function)</a>
 </li>
-      <li><a href="auto/gsf.html#gsf.minimizer.Minimizer.dual_annealing">dual_annealing() (gsf.minimizer.Minimizer method)</a>
+      <li><a href="auto/gsf.html#gsf.function_igm.dijkstra_igm_abs">dijkstra_igm_abs() (in module gsf.function_igm)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="auto/gsf.html#gsf.minimizer.Minimizer.dual_annealing">dual_annealing() (gsf.minimizer.Minimizer method)</a>
+</li>
       <li><a href="auto/gsf.html#gsf.function.dust_calz">dust_calz() (in module gsf.function)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.function.dust_gen">dust_gen() (in module gsf.function)</a>
@@ -242,12 +247,16 @@ <h2 id="G">G</h2>
       <li><a href="auto/gsf.html#gsf.function.gauss">gauss() (in module gsf.function)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.function.get_chi2">get_chi2() (in module gsf.function)</a>
+
+      <ul>
+        <li><a href="auto/gsf.html#gsf.zfit.get_chi2">(in module gsf.zfit)</a>
 </li>
+      </ul></li>
       <li><a href="auto/gsf.html#gsf.function_igm.get_column">get_column() (in module gsf.function_igm)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.posterior_nested.Post_nested.get_dict">get_dict() (gsf.posterior_nested.Post_nested method)</a>
 </li>
-      <li><a href="auto/gsf.html#gsf.function.get_filt">get_filt() (in module gsf.function)</a>
+      <li><a href="auto/gsf.html#gsf.function_igm.get_dtdz">get_dtdz() (in module gsf.function_igm)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.function.get_Fint">get_Fint() (in module gsf.function)</a>
 </li>
@@ -274,6 +283,8 @@ <h2 id="G">G</h2>
       <li><a href="auto/gsf.html#gsf.function_igm.get_nH">get_nH() (in module gsf.function_igm)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.writing.get_param">get_param() (in module gsf.writing)</a>
+</li>
+      <li><a href="auto/gsf.html#gsf.posterior_flexible.Post.get_sfh_prior">get_sfh_prior() (gsf.posterior_flexible.Post method)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.function.get_SFMS">get_SFMS() (in module gsf.function)</a>
 </li>
@@ -284,6 +295,24 @@ <h2 id="G">G</h2>
       <li><a href="auto/gsf.html#gsf.maketmp_filt.get_spectrum_draine">get_spectrum_draine() (in module gsf.maketmp_filt)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.function_class.Func.get_template">get_template() (gsf.function_class.Func method)</a>
+
+      <ul>
+        <li><a href="auto/gsf.html#gsf.function_class.Func_tau.get_template">(gsf.function_class.Func_tau method)</a>
+</li>
+      </ul></li>
+      <li><a href="auto/gsf.html#gsf.function_class.Func_tau.get_template_neb">get_template_neb() (gsf.function_class.Func_tau method)</a>
+</li>
+      <li><a href="auto/gsf.html#gsf.function_class.Func.get_template_single">get_template_single() (gsf.function_class.Func method)</a>
+</li>
+      <li><a href="auto/gsf.html#gsf.function_class.Func.get_total_flux">get_total_flux() (gsf.function_class.Func method)</a>
+</li>
+      <li><a href="auto/gsf.html#gsf.function_class.Func.get_total_flux_neb">get_total_flux_neb() (gsf.function_class.Func method)</a>
+</li>
+      <li><a href="auto/gsf.html#gsf.function.get_uvbeta">get_uvbeta() (in module gsf.function)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="auto/gsf.html#gsf.function_igm.get_XI">get_XI() (in module gsf.function_igm)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.fitting.Mainbody.get_zdist">get_zdist() (gsf.fitting.Mainbody method)</a>
 </li>
@@ -294,8 +323,6 @@ <h2 id="G">G</h2>
         <li><a href="auto/gsf.html#module-gsf">module</a>
 </li>
       </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li>
     gsf.basic_func
 
@@ -409,6 +436,8 @@ <h2 id="L">L</h2>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="auto/gsf.html#gsf.posterior_nested.Post_nested.lnprob">lnprob() (gsf.posterior_nested.Post_nested method)</a>
+</li>
+      <li><a href="auto/gsf.html#gsf.zfit.lnprob_cz">lnprob_cz() (in module gsf.zfit)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.posterior_flexible.Post.lnprob_emcee">lnprob_emcee() (gsf.posterior_flexible.Post method)</a>
 </li>
@@ -437,6 +466,8 @@ <h2 id="M">M</h2>
       <li><a href="auto/gsf.html#gsf.maketmp_filt.maketemp">maketemp() (in module gsf.maketmp_filt)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.maketmp_filt.maketemp_tau">maketemp_tau() (in module gsf.maketmp_filt)</a>
+</li>
+      <li><a href="auto/gsf.html#gsf.function_igm.masongronke_igm_abs">masongronke_igm_abs() (in module gsf.function_igm)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.minimizer.Minimizer.minimize">minimize() (gsf.minimizer.Minimizer method)</a>
 
@@ -509,18 +540,10 @@ <h2 id="O">O</h2>
       </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="auto/gsf.html#gsf.function_class.Func.open_spec_fits_dir">open_spec_fits_dir() (gsf.function_class.Func method)</a>
-
-      <ul>
-        <li><a href="auto/gsf.html#gsf.function_class.Func_tau.open_spec_fits_dir">(gsf.function_class.Func_tau method)</a>
+      <li><a href="auto/gsf.html#gsf.function_class.Func_tau.open_spec_fits_dir">open_spec_fits_dir() (gsf.function_class.Func_tau method)</a>
 </li>
-      </ul></li>
-      <li><a href="auto/gsf.html#gsf.function_class.Func.open_spec_neb_fits">open_spec_neb_fits() (gsf.function_class.Func method)</a>
-
-      <ul>
-        <li><a href="auto/gsf.html#gsf.function_class.Func_tau.open_spec_neb_fits">(gsf.function_class.Func_tau method)</a>
+      <li><a href="auto/gsf.html#gsf.function_class.Func_tau.open_spec_neb_fits">open_spec_neb_fits() (gsf.function_class.Func_tau method)</a>
 </li>
-      </ul></li>
   </ul></td>
 </tr></table>
 
@@ -548,14 +571,6 @@ <h2 id="P">P</h2>
   </ul></td>
 </tr></table>
 
-<h2 id="Q">Q</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="auto/gsf.html#gsf.fitting.Mainbody.quick_fit">quick_fit() (gsf.fitting.Mainbody method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
 <h2 id="R">R</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -578,6 +593,8 @@ <h2 id="R">R</h2>
 </li>
       </ul></li>
       <li><a href="auto/gsf.html#gsf.posterior_nested.Post_nested.residual_nest">residual_nest() (gsf.posterior_nested.Post_nested method)</a>
+</li>
+      <li><a href="auto/gsf.html#gsf.zfit.residual_z">residual_z() (in module gsf.zfit)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.gsf.run_gsf_all">run_gsf_all() (in module gsf.gsf)</a>
 </li>
@@ -601,10 +618,10 @@ <h2 id="S">S</h2>
 </li>
       <li><a href="auto/gsf.html#gsf.function.SFH_cons">SFH_cons() (in module gsf.function)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="auto/gsf.html#gsf.function.SFH_dec">SFH_dec() (in module gsf.function)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="auto/gsf.html#gsf.function.SFH_del">SFH_del() (in module gsf.function)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.minimizer.Minimizer.shgo">shgo() (gsf.minimizer.Minimizer method)</a>
@@ -612,6 +629,8 @@ <h2 id="S">S</h2>
       <li><a href="auto/gsf.html#gsf.minimizer.MinimizerResult.show_candidates">show_candidates() (gsf.minimizer.MinimizerResult method)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.maketmp_filt.sim_spec">sim_spec() (in module gsf.maketmp_filt)</a>
+</li>
+      <li><a href="auto/gsf.html#gsf.function.str2bool">str2bool() (in module gsf.function)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.posterior_flexible.Post.swap_pars">swap_pars() (gsf.posterior_flexible.Post method)</a>
 </li>
@@ -622,29 +641,11 @@ <h2 id="S">S</h2>
 
 <h2 id="T">T</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="auto/gsf.html#gsf.function_class.Func.tmp03">tmp03() (gsf.function_class.Func method)</a>
-</li>
-      <li><a href="auto/gsf.html#gsf.function_class.Func.tmp03_neb">tmp03_neb() (gsf.function_class.Func method)</a>
-</li>
-      <li><a href="auto/gsf.html#gsf.function_class.Func.tmp04">tmp04() (gsf.function_class.Func method)</a>
-
-      <ul>
-        <li><a href="auto/gsf.html#gsf.function_class.Func_tau.tmp04">(gsf.function_class.Func_tau method)</a>
-</li>
-      </ul></li>
-  </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="auto/gsf.html#gsf.function_class.Func.tmp04_dust">tmp04_dust() (gsf.function_class.Func method)</a>
 
       <ul>
         <li><a href="auto/gsf.html#gsf.function_class.Func_tau.tmp04_dust">(gsf.function_class.Func_tau method)</a>
-</li>
-      </ul></li>
-      <li><a href="auto/gsf.html#gsf.function_class.Func.tmp04_neb">tmp04_neb() (gsf.function_class.Func method)</a>
-
-      <ul>
-        <li><a href="auto/gsf.html#gsf.function_class.Func_tau.tmp04_neb">(gsf.function_class.Func_tau method)</a>
 </li>
       </ul></li>
   </ul></td>
diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv
index c0aa42b50b0d53b6b2949a9f1ff9984dc77446ae..fc5bdad3cb2a88ab89ace958fe721247b821f6cc 100644
GIT binary patch
delta 1669
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diff --git a/docs/_build/html/parameters.html b/docs/_build/html/parameters.html
index 09b83f1..ccc5150 100644
--- a/docs/_build/html/parameters.html
+++ b/docs/_build/html/parameters.html
@@ -180,7 +180,7 @@ <h1>Paramter Description<a class="headerlink" href="#paramter-description" title
 </tr>
 <tr class="row-odd"><td><p>MORP</p></td>
 <td><p>str</p></td>
-<td><p>Profile shape, if grism spectra are provided. Will be used to convolve templates. “moffat”, “gauss”, or none.</p></td>
+<td><p>“moffat”, “gauss”, or none. Profile shape, if grism spectra are provided. Will be used to convolve templates.</p></td>
 </tr>
 <tr class="row-even"><td><p>MORP_FILE</p></td>
 <td><p>str</p></td>
@@ -254,53 +254,57 @@ <h1>Paramter Description<a class="headerlink" href="#paramter-description" title
 <td><p>str</p></td>
 <td><p>(Optional) Minimum value for amplitude, in normal logarithmic scale.</p></td>
 </tr>
-<tr class="row-odd"><td><p>AVMAX</p></td>
+<tr class="row-odd"><td><p>DUST_MODEL</p></td>
+<td><p>int</p></td>
+<td><p>Dust attenuation model (default 0). 0 for Calzetti. 1 for MW. 2 for LCM. 3 for SMC. 4 for Kriek &amp; Conroy (2013).</p></td>
+</tr>
+<tr class="row-even"><td><p>AVMAX</p></td>
 <td><p>float</p></td>
 <td><p>(Optional) Maximum value for Av (dust attenuation in V-band), in mag.</p></td>
 </tr>
-<tr class="row-even"><td><p>AVMIN</p></td>
+<tr class="row-odd"><td><p>AVMIN</p></td>
 <td><p>float</p></td>
 <td><p>(Optional) Minimum value for Av (dust attenuation in V-band), in mag.</p></td>
 </tr>
-<tr class="row-odd"><td><p>F_ZMC</p></td>
+<tr class="row-even"><td><p>F_ZMC</p></td>
 <td><p>int</p></td>
 <td><p>If 1, redshift is set as a free parameter in the primary MCMC step. Otherwise, redshift is fixed to the input parameter.</p></td>
 </tr>
-<tr class="row-even"><td><p>F_MDYN</p></td>
+<tr class="row-odd"><td><p>F_MDYN</p></td>
 <td><p>int</p></td>
 <td><p>If 1, gsf uses dynamical mass (M_dyn column in CAT_BB) as a prior. Currently not supported.</p></td>
 </tr>
-<tr class="row-odd"><td><p>ADD_NEBULAE</p></td>
+<tr class="row-even"><td><p>ADD_NEBULAE</p></td>
 <td><p>int</p></td>
 <td><p>If 1, gsf adds nebular emission lines calculated within fsps.</p></td>
 </tr>
-<tr class="row-even"><td><p>logUMAX</p></td>
+<tr class="row-odd"><td><p>logUMAX</p></td>
 <td><p>float</p></td>
 <td><p>Maximum value for ionization parameter, in log, for emission lines if ADD_NEBULAE is True.</p></td>
 </tr>
-<tr class="row-odd"><td><p>logUMIN</p></td>
+<tr class="row-even"><td><p>logUMIN</p></td>
 <td><p>float</p></td>
 <td><p>Minimum value for ionization parameter, in log, for emission lines if ADD_NEBULAE is True.</p></td>
 </tr>
-<tr class="row-even"><td><p>DELlogU</p></td>
+<tr class="row-odd"><td><p>DELlogU</p></td>
 <td><p>float</p></td>
 <td><p>Resolution for logU.</p></td>
 </tr>
-<tr class="row-odd"><td><p>logUFIX</p></td>
+<tr class="row-even"><td><p>logUFIX</p></td>
 <td><p>float</p></td>
 <td><p>(Optional) logU will be fixed to this value if specified.</p></td>
 </tr>
-<tr class="row-even"><td><p>BPASS</p></td>
+<tr class="row-odd"><td><p>BPASS</p></td>
 <td><p>int</p></td>
 <td><p>If 1, BPASS templates will be used. Currently not supported.</p></td>
 </tr>
-<tr class="row-odd"><td></td>
+<tr class="row-even"><td></td>
 <td></td>
 <td></td>
 </tr>
 </tbody>
 </table>
-<p><strong>Parameters for a specific target:</strong></p>
+<p><strong>Parameters specific for the target:</strong></p>
 <table class="colwidths-given tight-table docutils align-default">
 <colgroup>
 <col style="width: 29%" />
diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js
index d6dbb0a..90e9cfb 100644
--- a/docs/_build/html/searchindex.js
+++ b/docs/_build/html/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file

From e8a923592cbaa2a0596e341fd0be84ab4ffc008c Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Sat, 10 Dec 2022 15:58:53 -0800
Subject: [PATCH 07/14] docs

---
 build/lib/gsf/__init__.py                     |   31 +
 build/lib/gsf/basic_func.py                   |   39 +
 build/lib/gsf/fitting.py                      | 2061 +++++++++
 build/lib/gsf/function.py                     | 1487 ++++++
 build/lib/gsf/function_class.py               | 1179 +++++
 build/lib/gsf/function_igm.py                 |  264 ++
 build/lib/gsf/gsf.py                          |  298 ++
 build/lib/gsf/maketmp_filt.py                 | 1517 ++++++
 build/lib/gsf/maketmp_z0.py                   |  503 ++
 build/lib/gsf/maketmp_z0_tau.py               |  187 +
 build/lib/gsf/minimizer.py                    | 2428 ++++++++++
 build/lib/gsf/plot_sed.py                     | 4122 +++++++++++++++++
 build/lib/gsf/plot_sfh.py                     | 2004 ++++++++
 build/lib/gsf/posterior_flexible.py           |  303 ++
 build/lib/gsf/posterior_nested.py             |  239 +
 build/lib/gsf/writing.py                      |  420 ++
 build/lib/gsf/zfit.py                         |  190 +
 ...f-1.7.6.dev20+g1e4de8a.d20221210-py3.9.egg |  Bin 0 -> 316421 bytes
 docs/_build/doctrees/auto/gsf.doctree         |  Bin 916799 -> 918402 bytes
 docs/_build/doctrees/environment.pickle       |  Bin 68880 -> 70118 bytes
 docs/_build/doctrees/index.doctree            |  Bin 5057 -> 5081 bytes
 docs/_build/doctrees/parameters.doctree       |  Bin 56349 -> 57095 bytes
 docs/_build/doctrees/redshift_fitting.doctree |  Bin 0 -> 9184 bytes
 docs/_build/html/_sources/index.rst.txt       |    1 +
 docs/_build/html/_sources/parameters.rst.txt  |    2 +
 .../html/_sources/redshift_fitting.rst.txt    |   22 +
 docs/_build/html/auto/gsf.html                |    9 +-
 docs/_build/html/auto/modules.html            |    5 +-
 docs/_build/html/genindex.html                |    1 +
 docs/_build/html/index.html                   |    2 +
 docs/_build/html/objects.inv                  |  Bin 1765 -> 1794 bytes
 docs/_build/html/parameters.html              |    2 +
 docs/_build/html/py-modindex.html             |    1 +
 docs/_build/html/redshift_fitting.html        |  131 +
 docs/_build/html/search.html                  |    1 +
 docs/_build/html/searchindex.js               |    2 +-
 docs/index.rst                                |    1 +
 docs/parameters.rst                           |    2 +
 docs/redshift_fitting.rst                     |   22 +
 gsf.egg-info/PKG-INFO                         |   21 +
 gsf.egg-info/SOURCES.txt                      |  633 +++
 gsf.egg-info/dependency_links.txt             |    1 +
 gsf.egg-info/not-zip-safe                     |    1 +
 gsf.egg-info/top_level.txt                    |    1 +
 gsf/fitting.py                                |   61 +-
 gsf/function_class.py                         |    5 +-
 gsf/maketmp_filt.py                           |   36 +-
 gsf/zfit.py                                   |    4 +-
 48 files changed, 18149 insertions(+), 90 deletions(-)
 create mode 100644 build/lib/gsf/__init__.py
 create mode 100644 build/lib/gsf/basic_func.py
 create mode 100644 build/lib/gsf/fitting.py
 create mode 100644 build/lib/gsf/function.py
 create mode 100644 build/lib/gsf/function_class.py
 create mode 100644 build/lib/gsf/function_igm.py
 create mode 100644 build/lib/gsf/gsf.py
 create mode 100644 build/lib/gsf/maketmp_filt.py
 create mode 100644 build/lib/gsf/maketmp_z0.py
 create mode 100644 build/lib/gsf/maketmp_z0_tau.py
 create mode 100644 build/lib/gsf/minimizer.py
 create mode 100644 build/lib/gsf/plot_sed.py
 create mode 100644 build/lib/gsf/plot_sfh.py
 create mode 100644 build/lib/gsf/posterior_flexible.py
 create mode 100644 build/lib/gsf/posterior_nested.py
 create mode 100644 build/lib/gsf/writing.py
 create mode 100644 build/lib/gsf/zfit.py
 create mode 100644 dist/gsf-1.7.6.dev20+g1e4de8a.d20221210-py3.9.egg
 create mode 100644 docs/_build/doctrees/redshift_fitting.doctree
 create mode 100644 docs/_build/html/_sources/redshift_fitting.rst.txt
 create mode 100644 docs/_build/html/redshift_fitting.html
 create mode 100644 docs/redshift_fitting.rst
 create mode 100644 gsf.egg-info/PKG-INFO
 create mode 100644 gsf.egg-info/SOURCES.txt
 create mode 100644 gsf.egg-info/dependency_links.txt
 create mode 100644 gsf.egg-info/not-zip-safe
 create mode 100644 gsf.egg-info/top_level.txt

diff --git a/build/lib/gsf/__init__.py b/build/lib/gsf/__init__.py
new file mode 100644
index 0000000..32449c4
--- /dev/null
+++ b/build/lib/gsf/__init__.py
@@ -0,0 +1,31 @@
+import re
+import sys
+import os
+from pkg_resources import get_distribution, DistributionNotFound
+
+__version_commit__ = ''
+_regex_git_hash = re.compile(r'.*\+g(\w+)')
+
+try:
+    __version__ = get_distribution(__name__).version
+except DistributionNotFound:
+    __version__ = 'dev'
+
+if '+' in __version__:
+    commit = _regex_git_hash.match(__version__).groups()
+    if commit:
+        __version_commit__ = commit[0]
+
+__author__ = 'Takahiro Morishita'
+__email__ = 'takahiro@ipac.caltech.edu'
+__credits__ = 'IPAC'
+
+package = 'gsf'
+
+print('Welcome to %s version %s'%(package,__version__))
+
+# Add path;
+#sys.path.append('%stmphot/'%borgpipe)
+
+import matplotlib as mat
+mat.rcParams['font.family'] = 'StixGeneral'
diff --git a/build/lib/gsf/basic_func.py b/build/lib/gsf/basic_func.py
new file mode 100644
index 0000000..f5bff9d
--- /dev/null
+++ b/build/lib/gsf/basic_func.py
@@ -0,0 +1,39 @@
+import numpy as np
+
+class Basic(object):
+    '''
+    '''
+    def __init__(self, MB):
+        self.ZZ = MB.Zall
+        self.NZ = len(self.ZZ)
+        self.delZ = MB.delZ
+
+    def Z2NZ(self, Z):
+        '''
+        Critical function to infer NZ from Z.
+        '''
+        NZ = np.argmin(np.abs(self.ZZ-Z))
+        return NZ
+
+
+class Basic_tau(object):
+    '''
+    '''
+    def __init__(self, MB):
+        self.ZZ = MB.Zall
+        self.NZ = len(self.ZZ)
+        self.delZ = MB.delZ
+        self.TT = MB.tau
+        self.delT = MB.deltau            
+        self.LA = MB.ageparam
+        self.delLA = MB.delage
+        
+    def Z2NZ(self, Z, tau, age):
+        '''
+        Critical function to infer NZ from Z.
+        '''
+        NZ = np.argmin(np.abs(Z-self.ZZ))
+        NT = np.argmin(np.abs(tau-self.TT))    
+        NA = np.argmin(np.abs(age-self.LA))
+
+        return NZ, NT, NA
diff --git a/build/lib/gsf/fitting.py b/build/lib/gsf/fitting.py
new file mode 100644
index 0000000..e8478e5
--- /dev/null
+++ b/build/lib/gsf/fitting.py
@@ -0,0 +1,2061 @@
+"""
+This includes the main part of gsf.
+
+Original copyright:
+   Copyright (c) 2021 Takahiro Morishita, STScI
+"""
+import numpy as np
+import sys
+import matplotlib.pyplot as plt
+from lmfit import Model, Parameters, minimize, fit_report#, Minimizer
+from numpy import log10
+import pickle as cPickle
+import os.path
+import random
+import string
+import timeit
+from scipy import stats
+from scipy.stats import norm
+import scipy.interpolate as interpolate
+from astropy.io import fits,ascii
+import corner
+
+# import from custom codes
+from .function import check_line_man, check_line_cz_man, calc_Dn4, savecpkl, get_leastsq
+from .zfit import check_redshift,get_chi2
+from .writing import get_param
+from .function_class import Func
+from .minimizer import Minimizer
+
+############################
+py_v = (sys.version_info[0])
+if py_v > 2:
+    try:
+        raw_input = input
+    except NameError:
+        pass
+
+################
+# Line library
+################
+LN = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3L', 'O3H', 'Mgb', 'Halpha', 'S2L', 'S2H']
+LW = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4960, 5008, 5175, 6563, 6717, 6731]
+fLW = np.zeros(len(LW), dtype='int')
+
+NRbb_lim = 10000 # BB data is associated with ids greater than this number.
+
+class Mainbody():
+    '''
+    The list of (possible) `Mainbody` attributes is given below:
+
+    Attributes
+    ----------
+    nimf : int 
+        0:Salpeter, 1:Chabrier, 2:Kroupa, 3:vanDokkum08.
+    Z : float array
+        Stellar phase metallicity in logZsun.
+    age : float array
+        Age, in Gyr.
+    fneb : int
+        flag for adding nebular emission. 0: No, 1: Yes.
+    tau0 : float array
+        Width of age bin. If you want to fix it to a specific value, set it to >0.01, in Gyr.
+        Otherwise, it would be either minimum value (=0.01; if one age bin), 
+        or the width to the next age bin.
+    '''
+
+    def __init__(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:float=25.0, pixelscale:float=0.06, Lsun:float=3.839*1e33, cosmo=None, idman=None, zman=None):
+        self.update_input(inputs, idman=idman, zman=zman)
+
+
+    def update_input(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:float=25.0, pixelscale:float=0.06, Lsun:float=3.839*1e33, cosmo=None, \
+                    idman=None, zman=None, sigz:float=5.0):
+        '''
+        The purpose of this module is to register/update the parameter attributes in `Mainbody`
+        by visiting the configuration file.
+
+        Parameters
+        ----------
+        inputs : str
+            Configuration file that lists parameters.
+
+        Mpc_cm : float, optional
+            Conversion factor from Mpc-to-cm
+
+        pixelscale : float, optional
+            Conversion factor from pixel-to-arcsec
+
+        '''
+        # Then register;
+        self.inputs = inputs
+        self.c = c
+        self.Mpc_cm = Mpc_cm
+        self.pixelscale = pixelscale
+        self.Lsun = Lsun
+        self.sigz = sigz
+        self.fitc_cz_prev = None
+
+        # Magzp;
+        try:
+            self.m0set = float(inputs['MAGZP'])
+        except:
+            print('MAGZP is not found. Set to %.2f'%(m0set))
+            self.m0set = m0set
+        self.d = 10**((48.6+self.m0set)/2.5) # Conversion factor from [ergs/s/cm2/A] to Fnu.
+
+        if cosmo == None:
+            from astropy.cosmology import WMAP9 as cosmo
+            self.cosmo = cosmo
+        else:
+            self.cosmo = cosmo
+
+        if idman != None:
+            self.ID = idman
+        else:
+            self.ID = inputs['ID']
+        print('\nFitting : %s\n'%self.ID)
+
+        # Read catalog;
+        try:
+            self.CAT_BB = inputs['CAT_BB']
+            self.fd_cat = ascii.read(self.CAT_BB)
+        except:
+            self.CAT_BB = None
+            self.fd_cat = None
+
+        if zman != None:
+            self.zgal = zman
+            self.zmcmin = None
+            self.zmcmax = None
+        else:
+            try:
+                self.zgal = float(inputs['ZMC'])
+            except:
+                iix = np.where(self.fd_cat['id'] == self.ID)
+                self.zgal = float(self.fd_cat['redshift'][iix])
+
+            try:
+                self.zmcmin = float(inputs['ZMCMIN'])
+                self.zmcmax = float(inputs['ZMCMAX'])
+            except:
+                try:
+                    self.zmcmin = self.zgal - float(self.fd_cat['ez_l'][iix])
+                    self.zmcmax = self.zgal + float(self.fd_cat['ez_u'][iix])
+                except:
+                    print('ZMCMIN and ZMCMAX cannot be found. z range is set to z pm 1.0')
+                    self.zmcmin = None
+                    self.zmcmax = None
+
+        # Data directory;
+        self.DIR_TMP = inputs['DIR_TEMP']
+        if not os.path.exists(self.DIR_TMP):
+            os.mkdir(self.DIR_TMP)
+
+        # Minimization;
+        self.fneld = self.inputs['FNELD']
+
+        # Mdyn;
+        try:
+            #self.Mdyn = float(inputs['MDYN'])
+            if int(inputs['F_MDYN']) == 1:
+                self.f_Mdyn = True
+            else:
+                self.f_Mdyn = False
+        except:
+            self.f_Mdyn = False
+
+        if self.f_Mdyn:
+            iix = np.where(self.fd_cat['id'] == int(self.ID))
+            try:
+                self.logMdyn = float(self.fd_cat['logMdyn'][iix])
+                self.elogMdyn = float(self.fd_cat['elogMdyn'][iix])
+                self.f_Mdyn = True
+            except:
+                self.f_Mdyn = False
+
+        print('f_Mdyn is set to %s\n'%self.f_Mdyn)
+        
+        #self.f_Mdyn = True
+        #self.logMdyn = 11.1
+        #self.elogMdyn = 0.1
+
+        #if self.f_Mdyn:
+        #    # If Mdyn is included.
+        #    self.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '_PA' + self.PA + '.asdf')
+        try:
+            self.DIR_EXTR = inputs['DIR_EXTR']
+        except:
+            self.DIR_EXTR = False
+
+        try:
+            # Scaling for grism; 
+            self.Cz0 = float(inputs['CZ0'])
+            self.Cz1 = float(inputs['CZ1'])
+            self.Cz2 = float(inputs['CZ2'])
+        except:
+            self.Cz0 = 1
+            self.Cz1 = 1
+            self.Cz2 = 1
+
+        # BPASS Binary template
+        try:
+            self.f_bpass = int(inputs['BPASS'])
+            try:
+                self.f_bin = int(inputs['BINARY'])
+            except:
+                self.f_bin = 1
+        except:
+            self.f_bpass = 0
+
+        # Nebular emission;
+        # Becuase of force_no_neb, add logUs regardless of `ADD_NEBULAE` flag.
+        self.fneb = False
+        self.nlogU = 0
+        try:
+            if int(inputs['ADD_NEBULAE']) == 1:
+                self.fneb = True
+                try:
+                    self.logUMIN = float(inputs['logUMIN'])
+                    self.logUMAX = float(inputs['logUMAX'])
+                    self.DELlogU = float(inputs['DELlogU'])
+                except:
+                    self.logUMIN = -2.5
+                    self.logUMAX = -2.0
+                    self.DELlogU = 0.5
+                self.logUs = np.arange(self.logUMIN, self.logUMAX, self.DELlogU)
+                self.nlogU = len(self.logUs)
+
+                try:
+                    self.logUFIX = float(inputs['logUFIX'])
+                    self.nlogU = 1
+                    self.logUMIN = self.logUFIX
+                    self.logUMAX = self.logUFIX
+                    self.DELlogU = 0
+                    self.logUs = np.asarray([self.logUMAX])
+                except:
+                    self.logUFIX = None
+            else:
+                self.fneb = False                
+                self.logUMIN = -2.5
+                self.logUMAX = -2.0
+                self.DELlogU = 0.5
+                self.logUs = np.arange(self.logUMIN, self.logUMAX, self.DELlogU)
+        except:
+            print('No nebular added.')
+            self.fneb = False
+            self.logUMIN = -2.5
+            self.logUMAX = -2.0
+            self.DELlogU = 0.5
+            self.logUs = np.arange(self.logUMIN, self.logUMAX, self.DELlogU)
+            pass
+        
+        # Outpu directory;
+        try:
+            self.DIR_OUT = inputs['DIR_OUT']
+            if not os.path.exists(self.DIR_OUT):
+                os.mkdir(self.DIR_OUT)
+        except:
+            self.DIR_OUT = './'
+
+        # Filter response curve directory, if bb catalog is provided.
+        self.DIR_FILT = inputs['DIR_FILT']
+        try:
+            self.filts = inputs['FILTER']
+            self.filts = [x.strip() for x in self.filts.split(',')]
+        except:
+            self.filts = []
+            for column in self.fd_cat.columns:
+                if column[0] == 'F':
+                    self.filts.append(column[1:])
+            pass
+
+        self.band = {}
+        for ii in range(len(self.filts)):
+            fd = np.loadtxt(self.DIR_FILT + self.filts[ii] + '.fil', comments='#')
+            self.band['%s_lam'%(self.filts[ii])] = fd[:,1]
+            self.band['%s_res'%(self.filts[ii])] = fd[:,2] / np.max(fd[:,2])
+            ffil_cum = np.cumsum(fd[:,2])
+            ffil_cum /= ffil_cum.max()
+            con = (ffil_cum>0.05) & (ffil_cum<0.95)
+            self.band['%s_fwhm'%(self.filts[ii])] = np.max(fd[:,1][con]) - np.min(fd[:,1][con])       
+
+        # Filter response curve directory, for RF colors.
+        self.filts_rf = '93,141,95,220,160'.split(',')
+        self.band_rf = {}
+        for ii in range(len(self.filts_rf)):
+            fd = np.loadtxt(self.DIR_FILT + self.filts_rf[ii] + '.fil', comments='#')
+            self.band_rf['%s_lam'%(self.filts_rf[ii])] = fd[:,1]
+            self.band_rf['%s_res'%(self.filts_rf[ii])] = fd[:,2] / np.max(fd[:,2])
+
+        # Tau comparison?
+        # -> Deprecated;
+        # self.ftaucomp = 0
+
+        # Check if func model for SFH;
+        try:
+            self.SFH_FORM = int(inputs['SFH_FORM'])
+        except:
+            self.SFH_FORM = -99
+
+        # This is for non-functional form for SFH;
+        if self.SFH_FORM == -99:
+            # Age
+            try:
+                self.age = np.asarray([float(x.strip()) for x in inputs['AGE'].split(',')])
+            except:
+                try:
+                    self.delage = float(inputs['DELAGE'])
+                except:
+                    self.delage = 0.1
+                try:
+                    self.agemax = float(inputs['AGEMAX'])
+                    self.agemin = float(inputs['AGEMIN'])
+                except:
+                    self.agemax = 14.0
+                    self.agemin = 0.003
+                logage = np.arange(np.log10(self.agemin), np.log10(self.agemax), self.delage)
+                self.age = 10**logage
+
+            try:
+                self.age_fix = [float(x.strip()) for x in inputs['AGEFIX'].split(',')]
+                aamin = []
+                print('\n')
+                print('##########################')
+                print('AGEFIX is found.\nAge will be fixed to:')
+                for age_tmp in self.age_fix:
+                    ageind = np.argmin(np.abs(age_tmp-np.asarray(self.age[:])))
+                    aamin.append(ageind)
+                    print('%6s Gyr'%(self.age[ageind]))
+                print('##########################')
+                self.aamin = aamin
+            except:
+                aamin = []
+                for nn,age_tmp in enumerate(self.age):
+                    aamin.append(nn)
+                self.aamin = aamin
+
+            self.npeak = len(self.age)
+            self.nage = np.arange(0,len(self.age),1)
+            
+        else: # This is for functional form for SFH;
+            self.agemax = float(inputs['AGEMAX'])
+            self.agemin = float(inputs['AGEMIN'])
+            self.delage = float(inputs['DELAGE'])
+
+            if self.agemax-self.agemin<self.delage:
+                self.delage = 0.0001
+                self.agemax = self.agemin + self.delage
+
+            self.ageparam = np.arange(self.agemin, self.agemax, self.delage)
+            self.nage = len(self.ageparam)
+
+            self.taumax = float(inputs['TAUMAX'])
+            self.taumin = float(inputs['TAUMIN'])
+            self.deltau = float(inputs['DELTAU'])
+
+            if self.taumax-self.taumin<self.deltau:
+                self.deltau = 0.0001
+                self.taumax = self.taumin + self.deltau
+
+            self.tau = np.arange(self.taumin, self.taumax, self.deltau)
+            self.ntau = len(self.tau)
+
+            self.npeak = int(inputs['NPEAK'])
+            self.age = np.arange(0,self.npeak,1) # This is meaningless.
+            aamin = []
+            for nn,age_tmp in enumerate(self.age):
+                aamin.append(nn)
+            self.aamin = aamin
+
+        # SNlimit;
+        try:
+            self.SNlim = float(inputs['SNLIM']) # SN for non-detection in BB catalog.
+        except:
+            self.SNlim = 1.0
+
+        # Redshift as a param;
+        try:
+            self.fzmc = int(inputs['F_ZMC'])
+        except:
+            self.fzmc = 0
+            print('Cannot find ZMC. Set to %d.'%(self.fzmc))
+
+        # Metallicity
+        self.has_ZFIX = False
+        try:
+            self.ZFIX = float(inputs['ZFIX'])
+            try:
+                self.delZ = float(inputs['DELZ'])
+                self.Zmax, self.Zmin = float(inputs['ZMAX']), float(inputs['ZMIN'])
+            except:
+                self.delZ = 0.0001
+                self.Zmin, self.Zmax = self.ZFIX, self.ZFIX + self.delZ
+            self.Zall = np.arange(self.Zmin, self.Zmax, self.delZ)
+            print('\n##########################')
+            print('ZFIX is found.\nZ will be fixed to: %.2f'%(self.ZFIX))
+            self.has_ZFIX = True
+        except:
+            self.Zmax, self.Zmin = float(inputs['ZMAX']), float(inputs['ZMIN'])
+            self.delZ = float(inputs['DELZ'])
+            if self.Zmax == self.Zmin or self.delZ == 0:
+                self.delZ = 0.0
+                self.ZFIX = self.Zmin
+                self.Zall = np.asarray([self.ZFIX])
+                self.has_ZFIX = True
+            elif np.abs(self.Zmax - self.Zmin) <= self.delZ:
+                self.ZFIX = self.Zmin
+                self.Zall = np.asarray([self.ZFIX])
+                self.has_ZFIX = True
+            else:
+                self.Zall = np.arange(self.Zmin, self.Zmax, self.delZ)
+
+        # If BPASS;
+        if self.f_bpass == 1:
+            try:
+                self.BPASS_DIR = inputs['BPASS_DIR']
+            except:
+                print('BPASS_DIR is not found. Using default.')
+                self.BPASS_DIR = '/astro/udfcen3/Takahiro/BPASS/'
+
+            self.BPASS_ver = 'v2.2.1'
+            self.Zsun = 0.020
+            Zbpass = [1e-5, 1e-4, 0.001, 0.002, 0.003, 0.004, 0.006, 0.008, 0.010, 0.020, 0.030, 0.040]
+            Zbpass = np.log10(np.asarray(Zbpass)/self.Zsun)
+
+            try: # If ZFIX is found;
+                iiz = np.argmin(np.abs(Zbpass[:] - float(inputs['ZFIX']) ) )
+                if Zbpass[iiz] - float(inputs['ZFIX']) != 0:
+                    print('%.2f is not found in BPASS Z list. %.2f is used instead.'%(float(inputs['ZFIX']),Zbpass[iiz]))
+                self.ZFIX = Zbpass[iiz]
+                self.delZ = 0.0001
+                self.Zmin, self.Zmax = self.ZFIX, self.ZFIX + self.delZ
+                self.Zall = np.arange(self.Zmin, self.Zmax, self.delZ) # in logZsun
+                print('\n##########################')
+                print('ZFIX is found.\nZ will be fixed to: %.2f'%(self.ZFIX))
+                self.has_ZFIX = True
+            except:
+                print('ZFIX is not found.')
+                print('Metallicities available in BPASS are limited and discrete. ZFIX is recommended.',self.Zall)
+                self.Zmax, self.Zmin = float(inputs['ZMAX']), float(inputs['ZMIN'])
+                con_z = np.where((Zbpass >= self.Zmin) & (Zbpass <= self.Zmax))
+                self.Zall = Zbpass[con_z]
+                self.delZ = 0.0001
+                self.Zmax,self.Zmin = np.max(self.Zall), np.min(self.Zall)
+                print('Final list for log(Z_BPASS/Zsun) is:',self.Zall)
+                if len(self.Zall)>1:
+                    self.has_ZFIX = False
+                    self.ZFIX = None
+
+        # N of param:
+        self.has_AVFIX = False
+        try:
+            Avfix = float(inputs['AVFIX'])
+            self.AVFIX = Avfix
+            self.nAV = 0
+            print('\n##########################')
+            print('AVFIX is found.\nAv will be fixed to:\n %.2f'%(Avfix))
+            self.has_AVFIX = True
+        except:
+            try:
+                self.Avmin = float(inputs['AVMIN'])
+                self.Avmax = float(inputs['AVMAX'])
+                if Avmin == Avmax:
+                    self.nAV = 0
+                    self.AVFIX = Avmin
+                    self.has_AVFIX = True
+                else:
+                    self.nAV = 1
+            except:
+                self.nAV = 1
+                self.Avmin = 0
+                self.Avmax = 4.0
+
+        # Z evolution;
+        print('\n#############################')
+        if self.SFH_FORM == -99:
+            if int(inputs['ZEVOL']) == 1:
+                self.ZEVOL = 1
+                self.ndim = int(self.npeak * 2 + self.nAV) # age, Z, and Av.
+                print('Metallicity evolution is on')
+            else:
+                self.ZEVOL = 0
+                print('Metallicity evolution is off')
+                try:
+                    ZFIX = float(inputs['ZFIX'])
+                    self.nZ = 0
+                except:
+                    if np.max(self.Zall) == np.min(self.Zall):
+                        self.nZ = 0
+                    else:
+                        self.nZ = 1
+                self.ndim = int(self.npeak + self.nZ + self.nAV) # age, Z, and Av.
+        else:
+            if int(inputs['ZEVOL']) == 1:
+                self.ZEVOL = 1
+                self.nZ = self.npeak
+                print('Metallicity evolution is on')
+            else:
+                self.ZEVOL = 0
+                print('Metallicity evolution is off')
+                try:
+                    ZFIX = float(inputs['ZFIX'])
+                    self.nZ = 0
+                except:
+                    if np.max(self.Zall) == np.min(self.Zall):
+                        self.nZ = 0
+                    else:
+                        self.nZ = 1
+                    
+            self.ndim = int(self.npeak*3 + self.nZ + self.nAV) # age, Z, and Av.
+        print('#############################\n')
+
+        # Redshift
+        self.ndim += self.fzmc
+        print('\n##########################')
+        print('No. of params are : %d'%(self.ndim))
+
+        # Line
+        try:
+            self.LW0 = [float(x.strip()) for x in inputs['LINE'].split(',')]
+        except:
+            self.LW0 = []
+
+        # Dust model specification;
+        try:
+            self.dust_model = int(inputs['DUST_MODEL'])
+            if self.dust_model == 0:
+                self.dust_model_name = 'Calz'
+            elif self.dust_model == 1:
+                self.dust_model_name = 'MW'
+            elif self.dust_model == 2:
+                self.dust_model_name = 'LMC'
+            elif self.dust_model == 3:
+                self.dust_model_name = 'SMC'
+            elif self.dust_model == 4:
+                self.dust_model_name = 'KriekConroy'
+            else:
+                print('Unknown number for dust attenuation. Calzetti.')
+                self.dust_model = 0
+                self.dust_model_name = 'Calz'
+        except:
+            self.dust_model = 0
+            self.dust_model_name = 'Calz'
+        print('Dust attenuation is set to %s\n'%self.dust_model_name)
+
+        # If FIR data;
+        try:
+            DT0 = float(inputs['TDUST_LOW'])
+            DT1 = float(inputs['TDUST_HIG'])
+            dDT = float(inputs['TDUST_DEL'])
+            try:
+                self.TDUSTFIX = float(inputs['TDUSTFIX'])
+                if self.TDUSTFIX < DT0 or self.TDUSTFIX > DT1:
+                    print('TDUSTFIX is set out of the range. Exiting.')
+                    sys.exit()
+            except:
+                self.TDUSTFIX = None
+
+            if DT0 == DT1:
+                self.Temp = [DT0]
+            else:
+                self.Temp = np.arange(DT0,DT1,dDT)
+
+            if not self.TDUSTFIX == None:
+                self.NTDUST = np.argmin(np.abs(self.Temp-self.TDUSTFIX))
+            else:
+                self.NTDUST = None
+
+            self.f_dust = True
+
+            self.DT0 = DT0
+            self.DT1 = DT1
+            self.dDT = dDT
+            try:
+                self.dust_numax = int(inputs['DUST_NUMAX'])
+                self.dust_nmodel = int(inputs['DUST_NMODEL'])
+            except:
+                self.dust_numax = 3
+                self.dust_nmodel = 9
+            print('FIR fit is on.')
+        except:
+            self.Temp = []
+            self.f_dust = False
+            pass
+
+        try:
+            self.DIR_DUST = inputs['DIR_DUST']
+        except:
+            self.DIR_DUST = './'
+
+        try:
+            # Length of each ssp templates.
+            self.tau0 = np.asarray([float(x.strip()) for x in inputs['TAU0'].split(',')])
+            self.ntau0 = len(self.tau0)
+        except:
+            self.tau0 = np.asarray([-1.0])
+            self.ntau0 = 1
+
+        # IMF
+        try:
+            self.nimf = int(inputs['NIMF'])
+        except:
+            self.nimf = 0
+            print('Cannot find NIMF. Set to %d.'%(self.nimf))
+
+        # Error parameter
+        try:
+            self.ferr = int(self.inputs['F_ERR'])
+        except:
+            self.ferr = 0
+            pass
+
+        # Nested or ensemble slice sampler?
+        self.f_mcmc = False
+        self.f_nested = False
+        self.f_zeus = False
+        try:
+            nnested = inputs['MC_SAMP']
+            if nnested == 'NEST' or nnested == '1':
+                self.f_nested = True
+            elif nnested == 'ZEUS' or  nnested == 'SLICE' or nnested == '2':
+                self.f_zeus = True
+            else:
+                self.f_mcmc = True
+        except:
+            print('Missing MC_SAMP keyword. Setting f_mcmc True.')
+            self.f_mcmc = True
+            pass
+
+        # Force Age to Age fix?:
+        try:
+            if int(inputs['FORCE_AGE'])==1:
+                self.force_agefix = True
+            else:
+                self.force_agefix = False
+        except:
+            self.force_agefix = False
+
+        # SFH prior;
+        # try:
+        #     self.norder_sfh_prior = int(inputs['norder_sfh_prior'])
+        #     self.f_prior_sfh = True
+        # except:
+        #     self.norder_sfh_prior = None
+        #     self.f_prior_sfh = False
+
+        print('\n')
+
+
+    def get_lines(self, LW0):
+        '''
+        '''
+        fLW = np.zeros(len(LW0), dtype='int')
+        LW = LW0
+        return LW, fLW
+
+
+    def read_data(self, Cz0:float, Cz1:float, Cz2:float, zgal:float, add_fir:bool=False, idman=None):
+        '''
+        Read in observed data. Not model fluxes.
+
+        Parameters
+        ----------
+        Cz0, Cz1 : float
+            Normalization coefficients for grism spectra. Cz0 for G102, Cz1 for G141.
+        zgal : float
+            Current redshift estimate.
+
+        Returns
+        -------
+        Dictionary.
+
+        Notes
+        -----
+        This modeule can be used for any SFHs.
+        '''
+        print('Reading data with Cz0=%.2f, Cz1=%.2f, Cz2=%.2f, zgal=%.2f'%(Cz0, Cz1, Cz2, zgal))
+
+        ##############
+        # Spectrum
+        ##############
+        NR, x, fy00, ey00 = self.data['spec_obs']['NR'], self.data['spec_obs']['x'], self.data['spec_obs']['fy'], self.data['spec_obs']['ey']
+        con0 = (NR<1000)
+        xx0 = x[con0]
+        fy0 = fy00[con0] * Cz0
+        ey0 = ey00[con0] * Cz0
+        con1 = (NR>=1000) & (NR<2000)
+        xx1 = x[con1]
+        fy1 = fy00[con1] * Cz1
+        ey1 = ey00[con1] * Cz1
+        con2 = (NR>=2000) & (NR<NRbb_lim)
+        xx2 = x[con2]
+        fy2 = fy00[con2] * Cz2
+        ey2 = ey00[con2] * Cz2
+
+        ##############
+        # Broadband
+        ##############
+        try:
+            NRbb, xbb, fybb, eybb, exbb = self.data['bb_obs']['NR'], self.data['bb_obs']['x'], self.data['bb_obs']['fy'], self.data['bb_obs']['ey'], self.data['bb_obs']['ex']
+        except: # if no BB;
+            print('No BB data.')
+            NRbb = np.asarray([])
+            xbb  = np.asarray([])
+            fybb = np.asarray([])
+            eybb = np.asarray([])
+            exbb = np.asarray([])
+
+        con_bb = ()
+        xx_bb = xbb[con_bb]
+        ex_bb = exbb[con_bb]
+        fy_bb = fybb[con_bb]
+        ey_bb = eybb[con_bb]
+
+        xx01 = np.append(xx0,xx1)
+        fy01 = np.append(fy0,fy1)
+        ey01 = np.append(ey0,ey1)
+
+        xx02 = np.append(xx01,xx2)
+        fy02 = np.append(fy01,fy2)
+        ey02 = np.append(ey01,ey2)
+
+        xx = np.append(xx02,xx_bb)
+        fy = np.append(fy02,fy_bb)
+        ey = np.append(ey02,ey_bb)
+
+        wht = 1./np.square(ey)
+        con_wht = (ey<0)
+        wht[con_wht] = 0
+
+        # For now...
+        #wht2 = check_line_man(fy, x, wht, fy, zgal, self.LW0)
+        wht2 = wht[:]
+
+        # Append data;
+        if add_fir:
+            nr_d, x_d, fy_d, ey_d = self.data['spec_dust_obs']['NR'], self.data['spec_dust_obs']['x'], self.data['spec_dust_obs']['fy'], self.data['spec_dust_obs']['ey']
+            NR = np.append(NR,nr_d)
+            fy = np.append(fy,fy_d)
+            ey = np.append(ey,ey_d)
+            x = np.append(x,x_d)
+            wht = np.append(wht,1./np.square(ey_d))
+            # For now...
+            #wht2= check_line_man(fy, x, wht, fy, zgal, self.LW0)
+            wht2 = wht[:]
+
+        # Sort data along wave;
+        f_sort = False
+
+        if f_sort:
+            nrd_yyd = np.zeros((len(NR),6), dtype='float')
+            nrd_yyd[:,0] = NR
+            nrd_yyd[:,1] = x
+            nrd_yyd[:,2] = fy
+            nrd_yyd[:,3] = ey
+            nrd_yyd[:,4] = wht
+            nrd_yyd[:,5] = wht2
+
+            b = nrd_yyd
+            nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
+            NR = nrd_yyd_sort[:,0]
+            x = nrd_yyd_sort[:,1]
+            fy = nrd_yyd_sort[:,2]
+            ey = nrd_yyd_sort[:,3]
+            wht = nrd_yyd_sort[:,4]
+            wht2= nrd_yyd_sort[:,5]
+
+        sn = fy/ey
+        self.n_optir = len(sn)
+
+        dict = {}
+        dict = {'NR':NR, 'x':x, 'fy':fy, 'ey':ey, 'NRbb':NRbb, 'xbb':xx_bb, 'exbb':ex_bb, 'fybb':fy_bb, 'eybb':ey_bb, 'wht':wht, 'wht2': wht2, 'sn':sn}
+
+        return dict
+
+
+    def search_redshift(self, dict, xm_tmp, fm_tmp, zliml:float=0.01, zlimu:float=6.0, delzz:float=0.01, lines:bool=False, prior=None, method:str='powell'):
+        '''
+        This module explores the redshift space to find the best redshift and probability distribution.
+
+        Parameters
+        ----------
+        dict : dictionary
+            Dictionary that includes input data.
+
+        xm_tmp : numpy.array
+            Wavelength array, common for fm_tmp below, at z=0. Should be in [len(wavelength)].
+        fm_tmp : numpy.array
+            Fluxes for various templates. Should be in a shape of [ n * len(wavelength)], 
+            where n is the number templates.
+        zliml : float
+            Lowest redshift for fitting range.
+        zlimu : float
+            Highest redshift for fitting range.
+        prior : numpy.array
+            Prior used for the redshift determination. E.g., Eazy z-probability.
+        method : str
+            Method for minimization. The option must be taken from lmfit. Powell is more accurate. Nelder is faster.
+
+        Returns
+        -------
+        zspace : numpy.array 
+            Array for redshift grid.
+        chi2s : numpy.array
+            Array of chi2 values corresponding to zspace.
+        '''
+
+        zspace = np.arange(zliml,zlimu,delzz)
+        chi2s  = np.zeros((len(zspace),2), 'float')
+        if prior == None:
+            prior = zspace[:] * 0 + 1.0
+
+        # Observed data points;
+        NR = dict['NR']
+        con0 = (NR<1000)
+        fy0 = dict['fy'][con0] #* Cz0s
+        ey0 = dict['ey'][con0] #* Cz0s
+        x0  = dict['x'][con0]
+        con1 = (NR>=1000) & (NR<10000)
+        fy1 = dict['fy'][con1] #* Cz1s
+        ey1 = dict['ey'][con1] #* Cz1s
+        x1  = dict['x'][con1]
+        con2 = (NR>=10000) # BB
+        fy2 = dict['fy'][con2]
+        ey2 = dict['ey'][con2]
+        x2 = dict['x'][con2]
+
+        fy01 = np.append(fy0,fy1)
+        fcon = np.append(fy01,fy2)
+        ey01 = np.append(ey0,ey1)
+        eycon = np.append(ey01,ey2)
+        x01 = np.append(x0,x1)
+        xobs = np.append(x01,x2)
+
+        wht = 1./np.square(eycon)
+        wht2 = wht
+
+        # Set parameters;
+        fit_par_cz = Parameters()
+        for nn in range(len(fm_tmp[:,0])):
+            fit_par_cz.add('C%d'%nn, value=1., min=0., max=1e5)
+
+        def residual_z(pars,z):
+            '''
+            '''
+            vals = pars.valuesdict()
+            xm_s = xm_tmp * (1+z)
+            fm_s = np.zeros(len(xm_tmp),'float')
+
+            for nn in range(len(fm_tmp[:,0])):
+                fm_s += fm_tmp[nn,:] * pars['C%d'%nn]
+
+            fint = interpolate.interp1d(xm_s, fm_s, kind='nearest', fill_value="extrapolate")
+            #fm_int = np.interp(xobs, xm_s, fm_s)
+            fm_int = fint(xobs)
+
+            if fcon is None:
+                print('Data is none')
+                return fm_int
+            else:
+                return (fm_int - fcon) * np.sqrt(wht2) # i.e. residual/sigma
+
+        # Start redshift search;
+        for zz in range(len(zspace)):
+            # Best fit
+            out_cz = minimize(residual_z, fit_par_cz, args=([zspace[zz]]), method=method)
+            keys = fit_report(out_cz).split('\n')
+
+            csq = out_cz.chisqr
+            rcsq = out_cz.redchi
+            fitc_cz = [csq, rcsq]
+
+            #return fitc_cz
+            chi2s[zz,0] = csq
+            chi2s[zz,1] = rcsq
+
+        self.zspace = zspace
+        self.chi2s = chi2s
+
+        return zspace, chi2s
+
+
+    def fit_redshift(self, xm_tmp, fm_tmp, delzz=0.01, ezmin=0.01, zliml=0.01, 
+        zlimu=None, snlim=0, priors=None, f_bb_zfit=True, f_line_check=False, 
+        f_norm=True, f_lambda=False, zmax=20, include_bb=False, f_exclude_negative=False):
+        '''
+        Find the best-fit redshift, before going into a big fit, through an interactive inspection.
+        This module is effective only when spec data is provided.
+        When spectrun is provided, this does redshift fit, but **by using the SED model guessed from the BB photometry**.
+        Thus, providing good BB photometry is critical in this step.
+
+        Parameters
+        ----------
+        xm_tmp, fm_tmp : float array
+            SED model.
+        delzz : float
+            Delta z in redshift search space
+        zliml : float
+            Lower limit range for redshift
+        zlimu : float
+            Upper limit range for redshift
+        ezmin : float
+            Minimum redshift uncertainty.
+        snlim : float
+            SN limit for data points. Those below the number will be cut from the fit.
+        include_bb : bool
+            Turn this True if Redshift fitting is requred for only BB data.
+        f_line_check : bool
+            If True, line masking.
+        priors : dict, optional
+            Dictionary that contains z (redshift grid) and chi2 (chi-square).
+        f_exclude_negative : bool
+            Exclude negative fluxes in spectrum.
+
+        Notes
+        -----
+        Spectral data must be provided to make this work.
+
+        '''
+        self.file_zprob = self.DIR_OUT + 'zprob_' + self.ID + '.txt'
+
+        # NMC for zfit
+        self.nmc_cz = int(self.inputs['NMCZ'])
+
+        # For z prior.
+        if self.zmcmin != None:
+            zliml = self.zmcmin
+        else:
+            zliml = 0
+        if self.zmcmax != None:
+            zlimu = self.zmcmax
+        else:
+            zlimu = 20
+
+        # Observed data;
+        sn = self.dict['fy'] / self.dict['ey']
+
+        # Only spec data?
+        if include_bb:
+            con_cz = ()#(sn>snlim)
+        else:
+            con_cz = (self.dict['NR']<NRbb_lim) #& (sn>snlim)
+
+        if f_exclude_negative:
+            con_cz &= (sn>snlim)
+            
+        fy_cz = self.dict['fy'][con_cz] # Already scaled by self.Cz0
+        ey_cz = self.dict['ey'][con_cz]
+        x_cz = self.dict['x'][con_cz] # Observed range
+        NR_cz = self.dict['NR'][con_cz]
+
+        # kind='cubic' causes an error if len(xm_tmp)<=3;
+        fint = interpolate.interp1d(xm_tmp, fm_tmp, kind='nearest', fill_value="extrapolate")
+        fm_s = fint(x_cz)
+
+        #
+        # If Eazy result exists;
+        #
+        try:
+            eaz_pz = self.inputs['EAZY_PZ']
+            f_eazy = True
+        except:
+            f_eazy = False
+
+        if f_eazy:
+            dprob = np.loadtxt(eaz_pz, comments='#')
+            zprob = dprob[:,0]
+            cprob = dprob[:,1]
+            # Then interpolate to a common z grid;
+            zz_prob = np.arange(0,zmax,delzz)
+            cprob_s = np.interp(zz_prob, zprob, cprob)
+            prior_s = np.exp(-0.5 * cprob_s)
+            prior_s /= np.sum(prior_s)
+        else:
+            zz_prob = np.arange(zliml,zlimu,delzz)
+            if priors != None:
+                zprob = priors['z']
+                cprob = priors['chi2']
+
+                cprob_s = np.interp(zz_prob, zprob, cprob)
+                prior_s = np.exp(-0.5 * cprob_s) / np.sum(cprob_s)
+                con_pri = (zz_prob<np.min(zprob)) | (zz_prob>np.max(zprob))
+                prior_s[con_pri] = 0
+                if f_norm:
+                    prior_s /= np.sum(prior_s)
+            else:
+                prior_s = zz_prob * 0 + 1.
+                prior_s /= np.sum(prior_s)
+
+        # Attach prior:
+        self.z_prior = zz_prob
+        self.p_prior = prior_s
+
+        # Plot;
+        if len(self.dict['fy'][con_cz])==0:
+            return 'y'
+
+        if self.fzvis==1 and (include_bb | len(fy_cz)>0):
+            import matplotlib as mpl
+            mpl.use('TkAgg')
+            data_model = np.zeros((len(x_cz),4),'float')
+            data_model[:,0] = x_cz
+            data_model[:,1] = fm_s
+            data_model[:,2] = fy_cz
+            data_model[:,3] = ey_cz
+
+            data_model_sort = data_model[data_model[:, 0].argsort()]            
+            #data_model_sort = np.sort(data_model, axis=0) # This does not work!!
+
+            plt.plot(data_model_sort[:,0], data_model_sort[:,1], 'gray', linestyle='--', linewidth=0.5, label='') # Model based on input z.
+            plt.plot(data_model_sort[:,0], data_model_sort[:,2],'.b', linestyle='-', linewidth=0.5, label='Obs.') # Observation
+            plt.errorbar(data_model_sort[:,0], data_model_sort[:,2], yerr=data_model_sort[:,3], color='b', capsize=0, linewidth=0.5) # Observation
+
+            # Write prob distribution;
+            if True:
+                get_chi2(zz_prob, fy_cz, ey_cz, x_cz, fm_tmp, xm_tmp/(1+self.zgal), self.file_zprob)
+
+            print('############################')
+            print('Start MCMC for redshift fit')
+            print('############################')
+            res_cz, fitc_cz = check_redshift(
+                fy_cz, ey_cz, x_cz, fm_tmp, xm_tmp/(1+self.zgal), 
+                self.zgal, self.z_prior, self.p_prior,
+                NR_cz, zliml, zlimu, self.nmc_cz, self.nwalk_cz, 
+                include_photometry=include_bb
+                )
+
+            z_cz = np.percentile(res_cz.flatchain['z'], [16,50,84])
+            scl_cz0 = np.percentile(res_cz.flatchain['Cz0'], [16,50,84])
+            scl_cz1 = np.percentile(res_cz.flatchain['Cz1'], [16,50,84])
+            scl_cz2 = np.percentile(res_cz.flatchain['Cz2'], [16,50,84])
+
+            zrecom = z_cz[1]
+            Czrec0 = scl_cz0[1]
+            Czrec1 = scl_cz1[1]
+            Czrec2 = scl_cz2[1]
+
+            # Switch to peak redshift:
+            # find minimum and maximum of xticks, so we know
+            # where we should compute theoretical distribution
+            ser = res_cz.flatchain['z']
+            xmin, xmax = self.zgal-0.2, self.zgal+0.2
+            lnspc = np.linspace(xmin, xmax, len(ser))
+            print('\n\n')
+            print('Recommended redshift, Cz0, Cz1, and Cz2, %.5f %.5f %.5f %.5f, with chi2/nu=%.3f'%(zrecom, Czrec0, Czrec1, Czrec2, fitc_cz[1]))
+            print('\n\n')
+            fit_label = 'Proposed model'
+            #self.fitc_cz = fitc_cz[1]
+
+        else:
+            print('fzvis is set to False. z fit not happening.')
+            try:
+                zmcmin = float(self.inputs['ZMCMIN'])
+                zmcmax = float(self.inputs['ZMCMAX'])
+                print('Redshift error is taken from input file.')
+            except:
+                zmcmin = self.zprev-ezmin
+                zmcmax = self.zprev+ezmin
+                print('Redshift error is assumed to %.1f.'%(ezmin))
+
+            z_cz = [zmcmin, self.zprev, zmcmax]
+            zrecom = z_cz[1]
+            scl_cz0 = [1.,1.,1.]
+            scl_cz1 = [1.,1.,1.]
+            scl_cz2 = [1.,1.,1.]
+            Czrec0 = scl_cz0[1]
+            Czrec1 = scl_cz1[1]
+            Czrec2 = scl_cz2[1]
+            res_cz = None
+            fitc_cz = [99,99]
+
+            # If this label is being used, it means that the fit is failed.
+            fit_label = 'Current model'
+
+        # New template at zrecom;
+        xm_s = xm_tmp / (1+self.zgal) * (1+zrecom)
+        fint = interpolate.interp1d(xm_s, fm_tmp, kind='nearest', fill_value="extrapolate")
+        fm_s = fint(x_cz)
+        whtl = 1/np.square(ey_cz)
+
+        if f_line_check:
+            try:
+                wht3, ypoly = check_line_cz_man(fy_cz, x_cz, whtl, fm_s, zrecom, LW=self.LW0)
+            except:
+                wht3, ypoly = whtl, fy_cz
+        else:
+            wht3 = whtl
+        con_line = (wht3==0)
+
+        # Visual inspection;
+        if self.fzvis==1:
+            #
+            # Ask interactively;
+            #
+            data_model_new = np.zeros((len(x_cz),4),'float')
+            data_model_new[:,0] = x_cz
+            data_model_new[:,1] = fm_s
+            # data_model_new_sort = np.sort(data_model_new, axis=0)
+            data_model_new_sort = data_model_new[data_model_new[:, 0].argsort()]
+
+            plt.plot(data_model_new_sort[:,0], data_model_new_sort[:,1], 'r', linestyle='-', linewidth=0.5, label='%s ($z=%.5f$)'%(fit_label,zrecom)) # Model based on recomended z.
+            plt.plot(x_cz[con_line], fm_s[con_line], color='orange', marker='o', linestyle='', linewidth=3.)
+
+            # Plot lines for reference
+            for ll in range(len(LW)):
+                try:
+                    conpoly = (x_cz/(1.+zrecom)>3000) & (x_cz/(1.+zrecom)<8000)
+                    yline = np.max(ypoly[conpoly])
+                    yy = np.arange(yline/1.02, yline*1.1)
+                    xxpre = yy * 0 + LW[ll] * (1.+self.zgal)
+                    xx = yy * 0 + LW[ll] * (1.+zrecom)
+                    plt.plot(xxpre, yy/1.02, linewidth=0.5, linestyle='--', color='gray')
+                    plt.text(LW[ll] * (1.+self.zgal), yline/1.05, '%s'%(LN[ll]), fontsize=8, color='gray')
+                    plt.plot(xx, yy, linewidth=0.5, linestyle='-', color='orangered')
+                    plt.text(LW[ll] * (1.+zrecom), yline, '%s'%(LN[ll]), fontsize=8, color='orangered')
+                except:
+                    pass
+
+            # Plot data
+            data_obsbb = np.zeros((len(self.dict['xbb']),3), 'float')
+            data_obsbb[:,0],data_obsbb[:,1] = self.dict['xbb'],self.dict['fybb']
+            if len(fm_tmp) == len(self.dict['xbb']): # BB only;
+                data_obsbb[:,2] = fm_tmp
+            #data_obsbb_sort = np.sort(data_obsbb, axis=0)
+            data_obsbb_sort = data_obsbb[data_obsbb[:, 0].argsort()]            
+            
+            if len(fm_tmp) == len(self.dict['xbb']): # BB only;
+                plt.scatter(data_obsbb_sort[:,0], data_obsbb_sort[:,2], color='none', marker='d', s=50, edgecolor='gray', zorder=4, label='Current model ($z=%.5f$)'%(self.zgal))
+            else:
+                model_spec = np.zeros((len(fm_tmp),2), 'float')
+                model_spec[:,0],model_spec[:,1] = xm_tmp,fm_tmp
+                #model_spec_sort = np.sort(model_spec, axis=0)
+                model_spec_sort = model_spec[model_spec[:, 0].argsort()]
+                plt.plot(model_spec_sort[:,0], model_spec_sort[:,1], marker='.', color='gray', ms=1, linestyle='-', linewidth=0.5, zorder=4, label='Current model ($z=%.5f$)'%(self.zgal))
+
+            try:
+                xmin, xmax = np.min(x_cz)/1.1,np.max(x_cz)*1.1
+            except:
+                xmin, xmax = 2000,10000
+
+            try:
+                plt.ylim(0,yline*1.1)
+            except:
+                pass
+
+            plt.xlim(xmin,xmax)
+            plt.xlabel('Wavelength ($\mathrm{\AA}$)')
+            plt.ylabel('$F_\\nu$ (arb.)')
+            plt.legend(loc=0)
+
+            zzsigma = ((z_cz[2] - z_cz[0])/2.)/self.zgal
+            zsigma = np.abs(self.zgal-zrecom) / (self.zgal)
+            C0sigma = np.abs(Czrec0-self.Cz0)/self.Cz0
+            eC0sigma = ((scl_cz0[2]-scl_cz0[0])/2.)/self.Cz0
+            C1sigma = np.abs(Czrec1-self.Cz1)/self.Cz1
+            eC1sigma = ((scl_cz1[2]-scl_cz1[0])/2.)/self.Cz1
+            C2sigma = np.abs(Czrec2-self.Cz2)/self.Cz2
+            eC2sigma = ((scl_cz2[2]-scl_cz2[0])/2.)/self.Cz2
+
+            print('\n##############################################################')
+            print('Input redshift is %.3f per cent agreement.'%((1.-zsigma)*100))
+            print('Error is %.3f per cent.'%(zzsigma*100))
+            print('Input Cz0 is %.3f per cent agreement.'%((1.-C0sigma)*100))
+            print('Error is %.3f per cent.'%(eC0sigma*100))
+            print('Input Cz1 is %.3f per cent agreement.'%((1.-C1sigma)*100))
+            print('Error is %.3f per cent.'%(eC1sigma*100))
+            print('Input Cz2 is %.3f per cent agreement.'%((1.-C2sigma)*100))
+            print('Error is %.3f per cent.'%(eC2sigma*100))
+            print('##############################################################\n')
+            plt.show()
+
+            flag_z = raw_input('Do you want to continue with the input redshift, Cz0, Cz1, Cz2, and chi2/nu, %.5f %.5f %.5f %.5f %.5f? ([y]/n/m) '%\
+                (self.zgal, self.Cz0, self.Cz1, self.Cz2, self.fitc_cz_prev))
+        else:
+            flag_z = 'y'
+
+        try:
+            self.z_cz_prev = self.z_cz
+        except:
+            self.z_cz_prev = [self.zgal,self.zgal,self.zgal]
+
+        # Write it to self;
+        self.zrecom = zrecom
+        self.Czrec0 = Czrec0 * self.Cz0
+        self.Czrec1 = Czrec1 * self.Cz1
+        self.Czrec2 = Czrec2 * self.Cz2
+        self.z_cz = z_cz
+        self.scl_cz0 = scl_cz0
+        self.scl_cz1 = scl_cz1
+        self.scl_cz2 = scl_cz2
+        self.res_cz = res_cz
+        self.fitc_cz_prev = fitc_cz[1]
+
+        return flag_z
+
+
+    def get_zdist(self, f_interact=False, f_ascii=True):
+        '''
+        Saves a plot of z-distribution.
+
+        Parameters
+        ----------
+        f_interact : bool
+            If true, this module returns figure ax.
+        '''
+
+        try:
+            fig = plt.figure(figsize=(6.5,2.5))
+            fig.subplots_adjust(top=0.96, bottom=0.16, left=0.09, right=0.99, hspace=0.15, wspace=0.25)
+            ax1 = fig.add_subplot(111)
+            n, nbins, patches = ax1.hist(self.res_cz.flatchain['z'], bins=200, density=True, color='gray', label='')
+
+            yy = np.arange(0,np.max(n),1)
+            xx = yy * 0 + self.z_cz[1]
+            ax1.plot(xx,yy,linestyle='-',linewidth=1,color='orangered',\
+                label='$z=%.5f_{-%.5f}^{+%.5f}$\n$C_{z0}=%.3f$\n$C_{z1}=%.3f$\n$C_{z2}=%.3f$'%\
+                (self.z_cz[1],self.z_cz[1]-self.z_cz[0],self.z_cz[2]-self.z_cz[1], self.Cz0, self.Cz1, self.Cz2))
+
+            if f_ascii:
+                file_ascii_out = self.DIR_OUT + 'zmc_' + self.ID + '.txt'
+                fw_ascii = open(file_ascii_out,'w')
+                fw_ascii.write('# z pz\n')
+                for ii in range(len(n)):
+                    fw_ascii.write('%.3f %.3f\n'%(nbins[ii]+(nbins[ii+1]-nbins[ii])/2.,n[ii]))
+                fw_ascii.close()
+
+            xx = yy * 0 + self.z_cz[0]
+            ax1.plot(xx,yy,linestyle='--',linewidth=1,color='orangered')
+            xx = yy * 0 + self.z_cz[2]
+            ax1.plot(xx,yy,linestyle='--',linewidth=1,color='orangered')
+            xx = yy * 0 + self.zgal
+            ax1.plot(xx,yy,linestyle='-',linewidth=1,color='royalblue', label='Input redshift')
+
+            # Prior:
+            ax1.plot(self.z_prior, self.p_prior * np.max(yy)/np.max(self.p_prior), linestyle='--', linewidth=1, color='cyan', label='Prior')
+
+            # Label:
+            ax1.set_xlabel('Redshift')
+            ax1.set_ylabel('$dn/dz$')
+            zp_min,zp_max = self.z_cz[0] - (self.z_cz[1]-self.z_cz[0])*3, self.z_cz[2] + (self.z_cz[2]-self.z_cz[1])*3
+            ax1.set_xlim(zp_min,zp_max)
+            ax1.legend(loc=0)
+            
+            # Save:
+            file_out = self.DIR_OUT + 'zmc_' + self.ID + '.png'
+            print('Figure is saved in %s'%file_out)
+
+            if f_interact:
+                fig.savefig(file_out, dpi=300)
+                # return fig, ax1
+            else:
+                plt.savefig(file_out, dpi=300)
+                plt.close()
+                # return True
+        except:
+            print('z-distribution figure is not generated.')
+            pass
+
+
+        if os.path.exists(self.file_zprob):
+            # Also, make zprob;
+            fig = plt.figure(figsize=(6.5,2.5))
+            fig.subplots_adjust(top=0.96, bottom=0.16, left=0.09, right=0.99, hspace=0.15, wspace=0.25)
+            ax1 = fig.add_subplot(111)
+
+            fd = ascii.read(self.file_zprob)
+            z = fd['z']
+            pz = fd['p(z)']
+            pz /= pz.max()
+
+            # prob:
+            ax1.plot(z, pz, linestyle='-', linewidth=1, color='r', label='')
+
+            # Label:
+            ax1.set_xlabel('Redshift')
+            ax1.set_ylabel('$p(z)$')
+            file_out = self.DIR_OUT + 'zprob_' + self.ID + '.png'
+            if f_interact:
+                fig.savefig(file_out, dpi=300)
+            else:
+                plt.savefig(file_out, dpi=300)
+                plt.close()
+
+
+    def add_param(self, fit_params, sigz=1.0, zmin=None, zmax=None):
+        '''
+        Add new parameters.
+
+        '''
+        f_add = False
+
+        # Redshift
+        if self.fzmc == 1:
+            if zmin == None:
+                self.zmcmin = 0
+            if zmax == None:
+                self.zmcmax = 15
+            fit_params.add('zmc', value=self.zgal, min=self.zmcmin, max=self.zmcmax)
+            print('Redshift is set as a free parameter (z in [%.2f:%.2f])'%(self.zmcmin, self.zmcmax))
+            f_add = True
+
+        # Dust;
+        if self.f_dust:
+            Tdust = self.Temp
+            if not self.TDUSTFIX == None:
+                fit_params.add('TDUST', value=self.NTDUST, vary=False)
+            elif len(Tdust)-1>0:
+                fit_params.add('TDUST', value=len(Tdust)/2., min=0, max=len(Tdust)-1)
+                self.ndim += 1
+            else:
+                fit_params.add('TDUST', value=0, vary=False)
+
+            fit_params.add('MDUST', value=9, min=0, max=15)
+            self.ndim += 1
+            self.dict = self.read_data(self.Cz0, self.Cz1, self.Cz2, self.zgal, add_fir=self.f_dust)
+            f_add = True
+
+        # Nebular; ver1.6
+        if self.fneb:
+            fit_params.add('Aneb', value=self.Aini, min=self.Amin, max=self.Amax)
+            self.ndim += 1
+            if not self.logUFIX == None:
+                fit_params.add('logU', value=self.logUFIX, vary=False)
+            else:
+                fit_params.add('logU', value=np.median(self.logUs), min=self.logUMIN, max=self.logUMAX)
+                self.ndim += 1
+            f_add = True
+
+        self.fit_params = fit_params
+
+        return f_add
+
+
+    def set_param(self):
+        '''
+        Set parameters.
+        '''
+        print('##################')
+        print('Setting parameters')
+        print('##################\n')
+        agemax = self.cosmo.age(self.zgal).value 
+        fit_params = Parameters()
+        f_Alog = True
+        if f_Alog:
+            try:
+                self.Amin = float(self.inputs['AMIN'])
+                self.Amax = float(self.inputs['AMAX'])
+            except:
+                self.Amin = -5
+                self.Amax = 10
+            self.Aini = -1
+        else:
+            self.Amin = 0
+            self.Amax = 1e3
+            self.Aini = 1
+
+        if self.SFH_FORM==-99:
+            self.age_vary = []
+            if len(self.age) != len(self.aamin):
+                for aa in range(len(self.age)):
+                    if aa not in self.aamin:
+                        fit_params.add('A'+str(aa), value=self.Amin, vary=False)
+                        self.ndim -= 1                    
+                        self.age_vary.append(False)
+                    else:
+                        fit_params.add('A'+str(aa), value=self.Aini, min=self.Amin, max=self.Amax)
+                        self.age_vary.append(True)
+            else:
+                for aa in range(len(self.age)):
+                    if self.age[aa] == 99:
+                        fit_params.add('A'+str(aa), value=self.Amin, vary=False)
+                        self.ndim -= 1
+                        self.age_vary.append(False)
+                    elif self.age[aa]>agemax and not self.force_agefix:
+                        print('At this redshift, A%d is beyond the age of universe and not being used.'%(aa))
+                        fit_params.add('A'+str(aa), value=self.Amin, vary=False)
+                        self.ndim -= 1
+                        self.age_vary.append(False)
+                    else:
+                        fit_params.add('A'+str(aa), value=self.Aini, min=self.Amin, max=self.Amax)
+                        self.age_vary.append(True)
+
+        else:
+            for aa in range(self.npeak):
+                tauini = (self.taumin+self.taumax)/2.
+                ageini = (self.agemin + self.agemax)/2.
+                fit_params.add('A%d'%aa, value=self.Aini, min=self.Amin, max=self.Amax)
+
+                # Check AGE fix; TBD.
+                try:
+                    AGEFIX = float(self.inputs['AGEFIX'])
+                    fit_params.add('AGE%d'%aa, value=AGEFIX, vary=False)
+                    self.agemin = AGEFIX
+                    self.agemax = AGEFIX
+                    self.ndim -= 1
+                    print('AGEFIX is found. Set to %.2f'%(AGEFIX))
+                except:
+                    tcosmo = np.log10(self.cosmo.age(self.zgal).value) 
+                    agemax_tmp = self.agemax
+                    if agemax_tmp > tcosmo:
+                        agemax_tmp = tcosmo
+                        print('Maximum age is set to the age of the univese (%.1fGyr) at this redshift.'%(self.cosmo.age(self.zgal).value))
+                    if self.npeak>1:
+                        if aa == 0:
+                            fit_params.add('AGE%d'%aa, value=ageini, min=self.agemin, max=np.log10(1.0))
+                        else:
+                            ageini = np.log10(1.0)
+                            fit_params.add('AGE%d'%aa, value=ageini, min=np.log10(1.0), max=agemax_tmp)
+                    else:
+                        #fit_params.add('AGE%d'%aa, value=0.0, min=0.0, max=0.01)
+                        fit_params.add('AGE%d'%aa, value=ageini, min=self.agemin, max=agemax_tmp)
+
+                # Check Tau fix;
+                if self.npeak>1:
+                    if aa == 0:
+                        fit_params.add('TAU%d'%aa, value=tauini, min=self.taumin, max=self.taumax)
+                    else:
+                        tauini = np.log10(0.3)
+                        fit_params.add('TAU%d'%aa, value=tauini, min=self.taumin, max=np.log10(0.3))
+                else:
+                    #fit_params.add('TAU%d'%aa, value=-0.8, min=-0.8, max=-0.79)
+                    fit_params.add('TAU%d'%aa, value=tauini, min=self.taumin, max=self.taumax)
+
+        #
+        # Dust attenuation;
+        #
+        try:
+            Avfix = float(self.inputs['AVFIX'])
+            fit_params.add('Av', value=Avfix, vary=False)
+            self.Avmin = Avfix
+            self.Avmax = Avfix
+        except:
+            try:
+                self.Avmin = float(self.inputs['AVMIN'])
+                self.Avmax = float(self.inputs['AVMAX'])
+                self.Avini = (self.Avmax+self.Avmin)/2.
+                self.Avini = 0.
+                if self.Avmin == self.Avmax:
+                    fit_params.add('Av', value=self.Avini, vary=False)
+                    self.Avmin = self.Avini
+                    self.Avmax = self.Avini
+                else:
+                    fit_params.add('Av', value=self.Avini, min=self.Avmin, max=self.Avmax)
+            except:
+                self.Avmin = 0.
+                self.Avmax = 4.
+                self.Avini = 0.5 #(Avmax-Avmin)/2. 
+                print('Dust is set in [%.1f:%.1f]/mag. Initial value is set to %.1f'%(self.Avmin,self.Avmax,self.Avini))
+                fit_params.add('Av', value=self.Avini, min=self.Avmin, max=self.Avmax)
+
+        #
+        # Metallicity;
+        #
+        if int(self.inputs['ZEVOL']) == 1:
+            for aa in range(len(self.age)):
+                if self.age[aa] == 99 or self.age[aa]>agemax:
+                    fit_params.add('Z'+str(aa), value=0, min=0, max=1e-10)
+                else:
+                    fit_params.add('Z'+str(aa), value=0, min=self.Zmin, max=self.Zmax)
+        else:
+            if self.has_ZFIX:
+                aa = 0
+                fit_params.add('Z'+str(aa), value=self.ZFIX, vary=False)
+            else:
+                aa = 0
+                if np.min(self.Zall)==np.max(self.Zall):
+                    fit_params.add('Z'+str(aa), value=np.min(self.Zall), vary=False)
+                else:
+                    fit_params.add('Z'+str(aa), value=0, min=self.Zmin, max=self.Zmax)
+
+       # Error parameter
+        try:
+            ferr = self.ferr
+            if ferr == 1:
+                fit_params.add('logf', value=0, min=-10, max=3) # in linear
+                self.ndim += 1
+                f_add = True
+        except:
+            ferr = 0
+            pass
+
+        self.fit_params = fit_params
+        return fit_params
+
+
+    def prepare_class(self, add_fir=None):
+        '''
+        '''
+        print('#################')
+        print('Preparing library')
+        print('#################\n')
+        # Load Spectral library;
+        self.lib = self.fnc.open_spec_fits(fall=0)
+        self.lib_all = self.fnc.open_spec_fits(fall=1, orig=True)
+
+        if self.f_dust:
+            self.lib_dust = self.fnc.open_spec_dust_fits(fall=0)
+            self.lib_dust_all = self.fnc.open_spec_dust_fits(fall=1)
+        if self.fneb:
+            self.lib_neb = self.fnc.open_spec_fits(fall=0, f_neb=True)
+            self.lib_neb_all = self.fnc.open_spec_fits(fall=1, f_neb=True)
+
+        if add_fir == None:
+            add_fir = self.f_dust
+
+        # For MCMC;
+        self.nmc = int(self.inputs['NMC'])
+        self.nwalk = int(self.inputs['NWALK'])
+        self.nmc_cz = int(self.inputs['NMCZ'])
+        self.nwalk_cz = int(self.inputs['NWALKZ'])
+        self.ZEVOL = int(self.inputs['ZEVOL'])
+        self.fzvis = int(self.inputs['ZVIS'])
+        if self.f_nested:
+            print('Nested sample is on. Nelder is used for time saving analysis.')
+            self.fneld = 1 
+
+        try:
+            self.ntemp = int(self.inputs['NTEMP'])
+        except:
+            self.ntemp = 1
+
+        try:
+            if int(inputs['DISP']) == 1:
+                self.f_disp = True
+            else:
+                self.f_disp = False
+        except:
+            self.f_disp = False
+
+        #
+        # Dust model specification;
+        #
+        try:
+            dust_model = int(self.inputs['DUST_MODEL'])
+        except:
+            dust_model = 0
+
+        # Error parameter
+        try:
+            self.ferr = int(self.inputs['F_ERR'])
+        except:
+            self.ferr = 0
+            pass
+
+        #
+        # Observed Data;
+        #
+        self.dict = self.read_data(self.Cz0, self.Cz1, self.Cz2, self.zgal, add_fir=add_fir)
+
+        # Set parameters;
+        self.set_param()
+    
+        return True
+
+
+    def get_shuffle(self, out, nshuf=3.0, amp=1e-4):
+        '''
+        Shuffles initial parameters of each walker, to give it extra randomeness.
+        '''
+        pos = np.zeros((self.nwalk, self.ndim), 'float')
+        for ii in range(pos.shape[0]):
+            aa = 0
+            for aatmp,key in enumerate(out.params.valuesdict()):
+                if out.params[key].vary == True:
+                    pos[ii,aa] += out.params[key].value
+                    # This is critical to avoid parameters fall on the boundary.
+                    delpar = (out.params[key].max-out.params[key].min)/1000
+                    # or not,
+                    delpar = 0
+                    if np.random.uniform(0,1) > (1. - 1./self.ndim):        
+                        pos[ii,aa] = np.random.uniform(out.params[key].min+delpar, out.params[key].max-delpar)
+                    else:
+                        if pos[ii,aa]<out.params[key].min+delpar or pos[ii,aa]>out.params[key].max-delpar:
+                            pos[ii,aa] = np.random.uniform(out.params[key].min+delpar, out.params[key].max-delpar)
+
+                    aa += 1
+        return pos
+
+
+    def main(self, cornerplot:bool=True, specplot=1, sigz=1.0, ezmin=0.01, ferr=0,
+            f_move:bool=False, verbose:bool=False, skip_fitz:bool=False, out=None, f_plot_accept:bool=True,
+            f_shuffle:bool=True, amp_shuffle=1e-2, check_converge:bool=True, Zini=None, f_plot_chain:bool=True,
+            f_chind:bool=True, ncpu:int=0, f_prior_sfh:bool=False, norder_sfh_prior:int=3, include_bb=True):
+        '''
+        Main module of this script.
+
+        Parameters
+        ----------
+        ferr : float
+            Used for error parameter.
+        skip_fitz : bool 
+            Skip redshift fit.
+        sigz : float 
+            Confidence interval for redshift fit (i.e. n-sigma).
+        ezmin : float 
+            minimum error in redshift
+        f_plot_accept : bool
+            Output acceptance plot of mcmc chains.
+        f_shuffle : bool
+            Randomly shuffle some of initial parameters in walkers.
+        check_converge : bool
+            Check convergence at every certain number.
+        f_plot_chain : book
+            Plot MC sample chain.
+        '''
+        import emcee
+        import zeus
+        try:
+            import multiprocess
+        except:
+            import multiprocessing as multiprocess
+
+        from .posterior_flexible import Post
+
+        # Call likelihood/prior/posterior function;
+        class_post = Post(self)
+
+        # Prepare library, data, etc.
+        self.prepare_class()
+
+        print('########################')
+        print('### Fitting Function ###')
+        print('########################')
+        start = timeit.default_timer()        
+        if not os.path.exists(self.DIR_TMP):
+            os.mkdir(self.DIR_TMP)
+
+        # Initial Z:
+        if Zini == None:
+            Zini = self.Zall
+
+        ####################################
+        # Initial Metallicity Determination
+        ####################################
+        # Get initial parameters
+        if not skip_fitz or out == None:
+            
+            # Do you want to prepare a template for redshift fit by using only spectrum?;
+            f_only_spec = False
+            out, chidef, Zbest = get_leastsq(self, Zini, self.fneld, self.age, self.fit_params, class_post.residual,\
+            self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.ID, f_only_spec=f_only_spec)
+
+            # Best fit
+            csq = out.chisqr
+            rcsq = out.redchi
+            fitc = [csq, rcsq] # Chi2, Reduced-chi2
+            ZZ = Zbest # This is really important/does affect lnprob/residual.
+            if self.fitc_cz_prev == None:
+                self.fitc_cz_prev = rcsq
+
+            print('\n\n')
+            print('#####################################')
+            print('Zbest, chi are;',Zbest,chidef)
+            print('Params are;',fit_report(out))
+            print('#####################################')
+            print('\n\n')
+
+            Av_tmp = out.params['Av'].value
+            AA_tmp = np.zeros(len(self.age), dtype='float')
+            ZZ_tmp = np.zeros(len(self.age), dtype='float')
+            nrd_tmp, fm_tmp, xm_tmp = self.fnc.get_template(out, f_val=True, f_nrd=True, f_neb=False)
+        else:
+            csq = out.chisqr
+            rcsq = out.redchi
+            fitc = [csq, rcsq]
+
+        ########################
+        # Check redshift
+        ########################
+        if skip_fitz:
+            flag_z = 'y'
+        else:
+            flag_z = self.fit_redshift(xm_tmp, fm_tmp, delzz=0.001, include_bb=include_bb)
+
+        #################################################
+        # Gor for mcmc phase
+        #################################################
+        if flag_z == 'y' or flag_z == '':
+
+            #######################
+            # Add parameters;
+            #######################
+            out_keep = out
+            f_add = self.add_param(self.fit_params, sigz=self.sigz, zmin=self.zmcmin, zmax=self.zmcmax)
+
+            # Then, minimize again.
+            if f_add:
+                if self.fneld == 1:
+                    fit_name = 'nelder'
+                elif self.fneld == 0:
+                    fit_name = 'powell'
+                elif self.fneld == 2:
+                    fit_name = 'leastsq'
+                else:
+                    fit_name = self.fneld
+
+                out = minimize(class_post.residual, self.fit_params, args=(self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust), method=fit_name) 
+                # showing this is confusing.
+                # print('\nMinimizer refinement;')
+                # print(fit_report(out))
+
+                # Fix params to what we had before.
+                if self.fzmc:
+                    out.params['zmc'].value = self.zgal
+                out.params['Av'].value = out_keep.params['Av'].value
+                for aa in range(len(self.age)):
+                    out.params['A'+str(aa)].value = out_keep.params['A'+str(aa)].value
+                    try:
+                        out.params['Z'+str(aa)].value = out_keep.params['Z'+str(aa)].value
+                    except:
+                        out.params['Z0'].value = out_keep.params['Z0'].value
+
+
+            ##############################
+            print('\n\n')
+            print('###############################')
+            print('Input redshift is adopted.')
+            print('Starting long journey in MCMC.')
+            print('###############################')
+            print('\n\n')
+
+            ################################
+            print('\nMinimizer Defined\n')
+
+            print('########################')
+            print('### Starting sampling ##')
+            print('########################\n')
+            start_mc = timeit.default_timer()
+
+            # MCMC;
+            if self.f_mcmc or self.f_zeus:
+                nburn = int(self.nmc/2)
+                if f_shuffle:
+                    # ZEUS may fail to run with f_shuffle.
+                    pos = self.get_shuffle(out, amp=amp_shuffle)
+                else:
+                    pos = amp_shuffle * np.random.randn(self.nwalk, self.ndim)
+                    aa = 0
+                    for aatmp,key in enumerate(out.params.valuesdict()):
+                        if out.params[key].vary:
+                            pos[:,aa] += out.params[key].value
+                            aa += 1
+
+                if self.f_zeus:
+                    print('sampling with ZEUS')
+                    check_converge = False
+                    f_burnin = True
+                    if f_burnin:
+                        # Burn phase;
+                        moves = zeus.moves.DifferentialMove() #GlobalMove()
+                        sampler = zeus.EnsembleSampler(self.nwalk, self.ndim, class_post.lnprob_emcee, \
+                            args=[out.params, self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust], \
+                            moves=moves, maxiter=1e6,\
+                            kwargs={'f_val':True, 'out':out, 'lnpreject':-np.inf, 'f_chind':f_chind, 
+                            'f_prior_sfh':f_prior_sfh, 'norder_sfh_prior':norder_sfh_prior},\
+                            )
+                        # Run MCMC
+                        nburn = int(self.nmc/10)
+
+                        print('Running burn-in')
+                        sampler.run_mcmc(pos, nburn)
+                        print('Done burn-in')
+
+                        # Get the burnin samples
+                        burnin = sampler.get_chain()
+
+                        # Set the new starting positions of walkers based on their last positions
+                        pos = burnin[-1]
+
+                    # Switch sampler;
+                    moves = zeus.moves.GlobalMove()
+                    sampler = zeus.EnsembleSampler(self.nwalk, self.ndim, class_post.lnprob_emcee, \
+                        args=[out.params, self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust], \
+                        moves=moves, maxiter=1e4,\
+                        kwargs={'f_val':True, 'out':out, 'lnpreject':-np.inf, 
+                        'f_prior_sfh':f_prior_sfh, 'norder_sfh_prior':norder_sfh_prior},\
+                        )
+
+                else:
+                    print('sampling with EMCEE')
+                    moves=[(emcee.moves.DEMove(), 0.8), (emcee.moves.DESnookerMove(), 0.2),]
+                    sampler = emcee.EnsembleSampler(self.nwalk, self.ndim, class_post.lnprob_emcee, \
+                        args=(out.params, self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust),\
+                        #moves=moves,\
+                        kwargs={'f_val': True, 'out': out, 'lnpreject':-np.inf, 
+                        'f_prior_sfh':f_prior_sfh, 'norder_sfh_prior':norder_sfh_prior},\
+                        )
+
+                if check_converge:
+                    # Check convergence every number;
+                    nevery = int(self.nmc/10)
+                    nconverge = 10.
+                    if nevery < 1000:
+                        nevery = 1000
+                    index = 0
+                    old_tau = np.inf
+                    autocorr = np.empty(self.nmc)
+                    for sample in sampler.sample(pos, iterations=self.nmc, progress=True):
+                        # Only check convergence every "nevery" steps
+                        if sampler.iteration % nevery:
+                            continue
+
+                        # Compute the autocorrelation time so far
+                        # Using tol=0 means that we'll always get an estimate even
+                        # if it isn't trustworthy
+                        tau = sampler.get_autocorr_time(tol=0)
+                        autocorr[index] = np.mean(tau)
+                        index += 1
+
+                        # Check convergence
+                        converged = np.all(tau * 100 < sampler.iteration)
+                        converged &= np.all(np.abs(old_tau - tau) / tau < nconverge)
+                        if converged:
+                            print('Converged at %d/%d\n'%(index*nevery,self.nmc))
+                            nburn = int(index*nevery / 50) # Burn 50%
+                            self.nmc = index*nevery
+                            break
+                        old_tau = tau
+                else:
+                    sampler.run_mcmc(pos, self.nmc, progress=True)
+                
+                flat_samples = sampler.get_chain(discard=nburn, thin=10, flat=True)
+
+                # Plot for chain.
+                if f_plot_chain:
+                    fig, axes = plt.subplots(self.ndim, figsize=(10, 7), sharex=True)
+                    samples = sampler.get_chain()
+                    labels = []
+                    for key in out.params.valuesdict():
+                        if out.params[key].vary:
+                            labels.append(key)
+                    for i in range(self.ndim):
+                        if self.ndim>1:
+                            ax = axes[i]
+                        else:
+                            ax = axes
+                        ax.plot(sampler.get_chain()[:,:,i], alpha=0.3, color='k')
+                        ax.set_xlim(0, len(samples))
+                        ax.yaxis.set_label_coords(-0.1, 0.5)
+                        ax.set_ylabel(labels[i])
+                    if self.ndim>1:
+                        axes[-1].set_xlabel("step number")
+                    else:
+                        axes.set_xlabel("step number")
+                    plt.savefig('%s/chain_%s.png'%(self.DIR_OUT,self.ID))
+
+                # Similar for nested;
+                # Dummy just to get structures;
+                print('\nRunning dummy sampler. Disregard message from here;\n')
+                mini = Minimizer(class_post.lnprob_emcee, out.params, 
+                fcn_args=[out.params, self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust], 
+                f_disp=False, nan_policy='omit',
+                moves=moves,\
+                kwargs={'f_val': True},\
+                )
+                res = mini.emcee(burn=0, steps=10, thin=1, nwalkers=self.nwalk, 
+                params=out.params, is_weighted=True, ntemps=self.ntemp, workers=ncpu, float_behavior='posterior', progress=False)
+                print('\nto here.\n')
+
+                # Update;
+                var_names = []#res.var_names
+                params_value = {}
+                ii = 0
+                for key in out.params:
+                    if out.params[key].vary:
+                        var_names.append(key)
+                        params_value[key] = np.median(flat_samples[nburn:,ii])
+                        ii += 1
+
+                import pandas as pd
+                flatchain = pd.DataFrame(data=flat_samples[nburn:,:], columns=var_names)
+
+                class get_res:
+                    def __init__(self, flatchain, var_names, params_value, res):
+                        self.flatchain = flatchain
+                        self.var_names = var_names
+                        self.params_value = params_value
+                        self.params = res.params
+                        for key in var_names:
+                            self.params[key].value = params_value[key]
+
+                # Inserting result from res0 into res structure;
+                res = get_res(flatchain, var_names, params_value, res)
+                res.bic = -99
+
+            elif self.f_nested:
+                import dynesty
+                from dynesty import NestedSampler
+
+                nlive = self.nmc      # number of live point
+                maxmcmc = self.nmc    # maximum MCMC chain length
+                nthreads = ncpu       # use one CPU core
+                bound = 'multi'   # use MutliNest algorithm for bounds
+                sample = 'unif' #'rwalk' # uniform sampling
+                tol = 0.01         # the stopping criterion
+                ndim_nest = self.ndim #0
+
+                #pars, fy, ey, wht, f_fir
+                logl_kwargs = {} #{'pars':out.params, 'fy':dict['fy'], 'ey':dict['ey'], 'wht':dict['wht2'], 'f_fir':self.f_dust}
+                logl_args = [self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust]
+                ptform_kwargs = {} #{'pars': out.params}
+                ptform_args = []
+
+                from .posterior_nested import Post_nested
+                class_post_nested = Post_nested(self, params=out.params)
+
+                # Initialize;
+                sampler = NestedSampler(class_post_nested.lnlike, class_post_nested.prior_transform, ndim_nest, walks=self.nwalk,\
+                bound=bound, sample=sample, nlive=nlive, ptform_args=ptform_args, logl_args=logl_args, logl_kwargs=logl_kwargs)
+
+                # Run;
+                sampler.run_nested(dlogz=tol, maxiter=maxmcmc, print_progress=self.f_disp)                 
+                res0 = sampler.results # get results dictionary from sampler
+                
+                # Dummy just to get structures;
+                mini = Minimizer(class_post.lnprob, out.params, fcn_args=[self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust], f_disp=False, \
+                    moves=[(emcee.moves.DEMove(), 0.8), (emcee.moves.DESnookerMove(), 0.2),])
+                res = mini.emcee(burn=0, steps=10, thin=1, nwalkers=self.nwalk, 
+                params=out.params, is_weighted=True, ntemps=self.ntemp, workers=ncpu, float_behavior='posterior')
+
+                # Update;
+                nburn = int(self.nmc/2)
+                var_names = []#res.var_names
+                params_value = {}
+                ii = 0
+                for key in out.params:
+                    if out.params[key].vary:
+                        var_names.append(key)
+                        params_value[key] = np.median(res0.samples[nburn:,ii])
+                        ii += 1
+
+                import pandas as pd
+                flatchain = pd.DataFrame(data=res0.samples[nburn:,:], columns=var_names)
+
+                class get_res:
+                    def __init__(self, flatchain, var_names, params_value, res):
+                        self.flatchain = flatchain
+                        self.var_names = var_names
+                        self.params_value = params_value
+                        self.params = res.params
+                        for key in var_names:
+                            self.params[key].value = params_value[key]
+
+                # Inserting result from res0 into res structure;
+                res = get_res(flatchain, var_names, params_value, res)
+                res.bic = -99
+
+            else:
+                print('Sampling is not specified. Failed. Exiting.')
+                return False
+
+            stop_mc  = timeit.default_timer()
+            tcalc_mc = stop_mc - start_mc
+            print('###############################')
+            print('### MCMC part took %.1f sec ###'%(tcalc_mc))
+            print('###############################')
+
+            #----------- Save pckl file
+            #-------- store chain into a cpkl file
+            start_mc = timeit.default_timer()
+            burnin = int(self.nmc/2)
+            #burnin   = 0 # Since already burnt in.
+            savepath = self.DIR_OUT
+            cpklname = 'chain_' + self.ID + '_corner.cpkl'
+            savecpkl({'chain':flatchain,
+                          'burnin':burnin, 'nwalkers':self.nwalk,'niter':self.nmc,'ndim':self.ndim},
+                         savepath+cpklname) # Already burn in
+            stop_mc = timeit.default_timer()
+            tcalc_mc = stop_mc - start_mc
+            if verbose:
+                print('#################################')
+                print('### Saving chain took %.1f sec'%(tcalc_mc))
+                print('#################################')
+
+
+            ####################
+            # MCMC corner plot.
+            ####################
+            if cornerplot:
+                levels = [0.68, 0.95, 0.997]
+                quantiles = [0.01, 0.99]
+                val_truth = []
+                for par in var_names:
+                    val_truth.append(params_value[par])
+                fig1 = corner.corner(flatchain, labels=var_names, \
+                label_kwargs={'fontsize':16}, quantiles=quantiles, show_titles=False, \
+                title_kwargs={"fontsize": 14}, \
+                truths=val_truth, \
+                plot_datapoints=False, plot_contours=True, no_fill_contours=True, \
+                plot_density=False, levels=levels, truth_color='gray', color='#4682b4')
+                fig1.savefig(self.DIR_OUT + 'SPEC_' + self.ID + '_corner.png')
+                self.cornerplot_fig = fig1
+
+            # Analyze MCMC results.
+            # Write to file.
+            stop  = timeit.default_timer()
+            tcalc = stop - start
+
+            # Then writing;
+            start_mc = timeit.default_timer()
+            get_param(self, res, fitc, tcalc=tcalc, burnin=burnin)
+            stop_mc = timeit.default_timer()
+            tcalc_mc = stop_mc - start_mc
+
+            return 2 # Cannot set to 1, to distinguish from retuen True
+
+
+        elif flag_z == 'm':
+            zrecom = raw_input('What is your manual input for redshift? [%.3f] '%(self.zgal))
+            if zrecom != '':
+                zrecom = float(zrecom)
+            else:
+                zrecom = self.zgal
+
+            Czrec0 = raw_input('What is your manual input for Cz0? [%.3f] '%(self.Cz0))
+            if Czrec0 != '':
+                Czrec0 = float(Czrec0)
+            else:
+                Czrec0 = self.Cz0
+
+            Czrec1 = raw_input('What is your manual input for Cz1? [%.3f] '%(self.Cz1))
+            if Czrec1 != '':
+                Czrec1 = float(Czrec1)
+            else:
+                Czrec1 = self.Cz1
+
+            Czrec2 = raw_input('What is your manual input for Cz2? [%.3f] '%(self.Cz2))
+            if Czrec2 != '':
+                Czrec2 = float(Czrec2)
+            else:
+                Czrec2 = self.Cz2
+
+            self.zprev = self.zgal   # Input redshift for previous run
+            self.zgal = zrecom # Recommended redshift from previous run
+            self.Cz0 = Czrec0
+            self.Cz1 = Czrec1
+            self.Cz2 = Czrec2
+            print('\n\n')
+            print('Generating model templates with input redshift and Scale.')
+            print('\n\n')
+            return True
+
+        else:
+            print('\n\n')
+            
+            flag_gen = raw_input('Do you want to make templates with recommended redshift, Cz0, Cz1, Cz2, and chi2/nu , %.5f %.5f %.5f %.5f %.5f? ([y]/n) '%\
+                (self.zrecom, self.Czrec0, self.Czrec1, self.Czrec2, self.fitc_cz_prev))
+
+            self.get_zdist()
+
+            if flag_gen == 'y' or flag_gen == '':
+                self.zprev = self.zgal # Input redshift for previous run
+                self.zgal = self.zrecom # Recommended redshift from previous run
+                self.Cz0 = self.Czrec0
+                self.Cz1 = self.Czrec1
+                self.Cz2 = self.Czrec2
+                return True
+            else:
+                print('\n\n')
+                print('Terminating process.')
+                print('\n\n')
+                return False
diff --git a/build/lib/gsf/function.py b/build/lib/gsf/function.py
new file mode 100644
index 0000000..d31c168
--- /dev/null
+++ b/build/lib/gsf/function.py
@@ -0,0 +1,1487 @@
+from scipy import asarray as ar,exp
+import numpy as np
+import sys
+from scipy.integrate import simps
+import pickle as cPickle
+import os
+import scipy.interpolate as interpolate
+from scipy.interpolate import interp1d
+import logging
+
+################
+# Line library
+################
+LN0 = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3L', 'O3H', 'Mgb', 'Halpha', 'S2L', 'S2H']
+LW0 = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4960, 5008, 5175, 6563, 6717, 6731]
+fLW = np.zeros(len(LW0), dtype='int') # flag.
+c = 3.e18 # A/s
+
+
+def str2bool(v):
+    '''
+    '''
+    if isinstance(v, bool):
+       return v
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
+
+def get_uvbeta(lm, flam, zbes, lam_blue=1650, lam_red=2300):
+    '''
+    Purpose
+    -------
+    get UV beta_lambda slope.
+
+    Parameters
+    ----------
+    lm : float array
+        in lambda
+    flam : float array
+        in flambda 
+    '''
+    con_uv = (lm/(1.+zbes)>lam_blue) & (lm/(1.+zbes)<lam_red)
+    #flam = fnutolam(lm,fl)
+    try:
+        fit_results = np.polyfit(np.log10(lm/(1.+zbes))[con_uv], np.log10(flam)[con_uv], 1) #, w=flam[con_uv])
+        beta = fit_results[0]
+        if np.isnan(beta):
+            beta = -99
+    except:
+        beta = -99
+    return beta
+
+
+def func_tmp(xint,eobs,fmodel):
+    '''
+    '''
+    int_tmp = np.exp(-0.5 * ((xint-fmodel)/eobs)**2)
+    return int_tmp
+
+
+def get_chi2(fy, ey, wht3, ysump, ndim_eff, SNlim=1.0, f_chind=True, f_exclude=False, xbb=None, x_ex=None):
+    '''
+    '''
+    from scipy import special
+    if f_chind:
+        conw = (wht3>0) & (ey>0) & (fy/ey>SNlim)
+    else:
+        conw = (wht3>0) & (ey>0)
+
+    resid = fy-ysump
+    chi2 = sum((np.square(resid) * np.sqrt(wht3))[conw])
+
+    chi_nd = 0.0
+    if f_chind:
+        f_ex = np.zeros(len(fy), 'int')
+        for ii in range(len(fy)):
+            if f_exclude:
+                if xbb[ii] in x_ex:
+                    f_ex[ii] = 1
+
+        con_up = (ey>0) & (fy/ey<=SNlim) & (f_ex == 0)
+        x_erf = (ey[con_up] - ysump[con_up]) / (np.sqrt(2) * ey[con_up])
+        f_erf = special.erf(x_erf)
+        chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey[con_up] * (1 + f_erf)) )
+
+    # Number of degree;
+    con_nod = (wht3>0) & (ey>0) #& (fy/ey>SNlim)
+    nod = int(len(wht3[con_nod])-ndim_eff)
+    if nod>0:
+        fin_chi2 = (chi2 - 2 * chi_nd) / nod
+    else:
+        fin_chi2 = -99
+
+    return chi2,fin_chi2
+
+
+def get_ind(wave,flux):
+    '''
+    Gets Lick index for input
+
+    Returns
+    -------
+    equivalent width
+    '''    
+    lml     = [4268, 5143, 5233, 5305, 5862, 4828, 4628, 4985, 5669, 5742, 4895, 4895, 5818, 6068]
+    lmcl    = [4283, 5161, 5246, 5312, 5879, 4848, 4648, 5005, 5689, 5762, 5069, 5154, 5938, 6191]
+    lmcr    = [4318, 5193, 5286, 5352, 5911, 4877, 4668, 5925, 5709, 5782, 5134, 5197, 5996, 6274]
+    lmr     = [4336, 5206, 5318, 5363, 5950, 4892, 4688, 5945, 5729, 5802, 5366, 5366, 6105, 6417]
+
+    W = np.zeros(len(lml), dtype='float')
+    for ii in range(len(lml)):
+        con_cen = (wave>lmcl[ii]) & (wave<lmcr[ii])
+        con_sid = ((wave<lmcl[ii]) & (wave>lml[ii])) | ((wave<lmr[ii]) & (wave>lmcr[ii]))
+
+        Ic = np.mean(flux[con_cen])
+        Is = np.mean(flux[con_sid])
+
+        delam = lmcr[ii] - lmcl[ii]
+
+        if ii < 10:
+            W[ii] = (1. - Ic/Is) * delam
+        elif 1. - Ic/Is > 0:
+            W[ii] = -2.5 * np.log10(1. - Ic/Is)
+        else:
+            W[ii] = -99
+
+    return W
+
+
+def printProgressBar (iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', printEnd="\r", emojis=['']):
+    '''
+    Call in a loop to create terminal progress bar.
+
+    Parameters
+    ----------
+    iteration : int 
+        current iteration
+    total : int 
+        total iterations
+    prefix : str
+        prefix string
+    suffix : str
+        suffix string
+    decimals : int
+        positive number of decimals in percent complete
+    length : int
+        character length of bar
+    fill : str
+        bar fill character
+    printEnd : str 
+        end character
+    '''
+    percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
+    if fill == None:
+        if float(percent) < 33:
+            fill = emojis[0]
+        elif float(percent) < 66:
+            fill = emojis[1]
+        elif float(percent) < 99:
+            fill = emojis[2]
+        else:
+            fill = emojis[3]
+
+    filledLength = int(length * iteration // total)
+    bar = fill * filledLength + '-' * (length - filledLength)
+    string = '(%d/%d)'%(iteration,total)
+    print(f'\r{prefix} |{bar}| {percent}% {suffix} {string}', end = printEnd)
+    # Print New Line on Complete
+    if iteration == total: 
+        print()
+
+
+def get_input():
+    '''
+    Gets a default dictionary for input params.
+
+    '''
+    inputs = {'ID':'10000', 'PA':'00', 'ZMC':0.01, 'CZ0':1.0, 'CZ1':1.0, 'BPASS':0,
+    'DIR_OUT':'./output/', 'DIR_TEMP':'./templates/', 'DIR_FILT':'./filter/',
+    'NIMF':0, 'NMC':100, 'NWALK':50, 'NMCZ':30, 'NWALKZ':20,
+    'ZEVOL':0, 'ZVIS':0, 'FNELD':'differential_evolution', 'MC_SAMP':'EMCEE'}
+
+    return inputs
+
+
+def read_input(parfile):
+    '''
+    Gets info from param file.
+
+    Returns
+    -------
+    Input dictionary.
+
+    '''
+    input0 = []
+    input1 = []
+    file = open(parfile,'r')
+    while 1:
+        line = file.readline()
+        if not line:
+            break
+        else:
+            cols = str.split(line)
+            if len(cols)>0 and cols[0][0] != '#':
+                input0.append(cols[0])
+                input1.append(cols[1])
+    file.close()
+    inputs = {}
+    for i in range(len(input0)):
+        inputs[input0[i]]=input1[i]
+
+    return inputs
+
+
+def write_input(inputs, file_out='gsf.input'):
+    '''
+    Gets an ascii format param file.    
+
+    Returns
+    -------
+    file_out
+    '''
+    import gsf
+    fw = open(file_out, 'w')
+    fw.write('# gsf ver %s\n'%(gsf.__version__))
+    for key in inputs.keys():
+        fw.write('%s %s\n'%(key,inputs[key]))
+    return True
+
+
+def loadcpkl(cpklfile):
+    """
+    Load cpkl files.
+    """
+    import pickle
+    if not os.path.isfile(cpklfile): raise ValueError(' ERR: cannot find the input file')
+    f = open(cpklfile, 'rb') #, encoding='ISO-8859-1')
+
+    if sys.version_info.major == 2:
+        data = pickle.load(f)
+    elif sys.version_info.major == 3:
+        data = pickle.load(f, encoding='latin-1')
+
+    f.close()
+    return data
+
+
+def get_leastsq(MB, ZZtmp, fneld, age, fit_params, residual, fy, ey, wht, ID0, 
+    chidef=None, Zbest=0, f_keep=False, f_only_spec=False):
+    '''
+    Get initial parameters at various Z
+    '''
+    from lmfit import Model, Parameters, minimize, fit_report, Minimizer
+
+    if len(fy)<2:
+        print('Not enough data for quick fit. Exiting.')
+        return False
+
+    file = 'Z_' + ID0 + '.cat'
+    fwz = open(file, 'w')
+    fwz.write('# ID Zini chi/nu AA Av Zbest\n')
+
+    if fneld == 1:
+        fit_name = 'nelder'
+    elif fneld == 0:
+        fit_name = 'powell'
+    elif fneld == 2:
+        fit_name = 'leastsq'
+    else:
+        fit_name = fneld
+
+    fwz.write('# minimizer: %s\n' % fit_name)
+
+    if MB.has_ZFIX:
+        ZZtmp = [MB.ZFIX]
+
+    for zz in range(len(ZZtmp)):
+        ZZ = ZZtmp[zz]
+        for aa in range(MB.npeak):
+            if MB.ZEVOL == 1 or aa == 0:
+                fit_params['Z'+str(aa)].value = ZZ
+
+        f_fir = False
+
+        out_tmp = minimize(residual, fit_params, args=(fy, ey, wht, f_fir), 
+            method=fit_name, kws={'f_only_spec':f_only_spec})
+            
+        csq = out_tmp.chisqr
+        rcsq = out_tmp.redchi
+        fitc = [csq, rcsq] # Chi2, Reduced-chi2
+
+        fwz.write('%s %.2f %.5f'%(ID0, ZZ, fitc[1]))
+
+        AA_tmp = np.zeros(MB.npeak, dtype='float')
+        ZZ_tmp = np.zeros(MB.npeak, dtype='float')
+        for aa in range(MB.npeak):
+            AA_tmp[aa] = out_tmp.params['A'+str(aa)].value
+            fwz.write(' %.5f'%(AA_tmp[aa]))
+
+        Av_tmp = out_tmp.params['Av'].value
+        fwz.write(' %.5f'%(Av_tmp))
+        for aa in range(MB.npeak):
+            if MB.ZEVOL == 1 or aa == 0:
+                ZZ_tmp[aa] = out_tmp.params['Z'+str(aa)].value
+                fwz.write(' %.5f'%(ZZ_tmp[aa]))
+
+        fwz.write('\n')
+        if chidef==None:
+            chidef = fitc[1]
+            out = out_tmp
+        elif fitc[1]<chidef:
+            chidef = fitc[1]
+            out = out_tmp
+
+    fwz.close()
+
+    if not f_keep:
+        os.system('rm %s'%file)
+    
+    return out,chidef,Zbest
+
+
+def check_rejuv(age,SF,MS,SFMS_50,lm_old=10.0,delMS=0.2):
+    '''
+    A Function to check rejuvenation.
+
+    Parameters
+    ----------
+    delMS : float
+        Scatter around the Main Sequence. 0.2 dex, in default.
+    '''
+    age_set = 1.0
+    con_old = (age>=age_set)
+    con_mid = (age<age_set) & (age>np.min(age))
+
+    f_quen  = 0
+    f_rejuv = 0.
+    t_quench= 10.0
+    t_rejuv = np.min(age)
+    if np.max(MS[con_old])>lm_old:
+
+        # Set the most recent quenching;
+        #for ii in range(len(age)-1,-1,-1): # 4 to 0.01
+        for ii in range(len(age)): # 0.01 to 4
+            #if (age[ii]<age_set) and (age[ii]>np.min(age)) and SF[ii,2]<SFMS_50[ii]-delMS:
+            if (age[ii]<age_set) and (age[ii]>np.min(age)) and SF[ii,2]<SFMS_50[ii]-delMS and f_rejuv == 0:
+                if ii>0 and f_quen==0:
+                    t_rejuv = age[ii-1]
+                f_quen  = 1
+            if f_quen==1 and SF[ii,2]>SFMS_50[ii]-delMS:
+                    t_quench = age[ii-1] # Time the most recent quenching has set.
+                    f_rejuv  = 1
+                    break
+
+    #print(f_rejuv,t_quench,t_rejuv)
+    return f_rejuv,t_quench,t_rejuv
+
+
+def get_SFMS(red,age,mass,IMF=1):
+    '''
+    Gets SFMS at age ago from z=red.
+
+    Parameters
+    ----------
+    red : float
+        Observed redshift
+    age : array
+        lookback time, in Gyr.
+    mass : array
+        stellar mass (array) at each age, in Msun (not logM). 
+
+    Returns
+    -------
+    SFR, in logMsun/yr.
+
+    Notes
+    -----
+    From Speagle+14 Eq28.
+    Chabrier IMF, in default
+    '''
+    from astropy.cosmology import WMAP9
+    cosmo = WMAP9
+
+    CIMF = 0
+    if IMF == 0:#
+        CIMF = 0.23
+        print('SFMS is shifted to Salpeter IMF.')
+    elif IMF == 2:
+        CIMF = 0.04
+        print('SFMS is shifted to Kroupa IMF.')
+
+    x = np.log10(mass) - CIMF #np.arange(6,13,0.1)
+    tz = cosmo.age(z=red).value - age # in Gyr
+    y1 = (0.84 - 0.026*tz) * x - (6.51 - 0.11*tz) # in log Msun/yr
+    con = (y1<=0)
+    y1[con] = -10
+    return y1
+
+
+def fit_spec(lm, fobs, eobs, ftmp):
+    '''
+    Fitting. (Not sure)
+    '''
+    s = np.sum(fobs*ftmp/eobs**2)/np.sum(ftmp**2/eobs**2)
+    chi2 = np.sum(((fobs-s*ftmp)/eobs)**2)
+    return chi2, s
+
+
+def fit_specphot(lm, fobs, eobs, ftmp, fbb, ebb, ltmp_bb, ftmp_bb):
+    I1   = np.sum(fobs*ftmp/eobs**2) + np.sum(fbb*ftmp_bb/ebb**2)
+    I2   = np.sum(ftmp**2/eobs**2)   + np.sum(ftmp_bb**2/ebb**2)
+    s    = I1/I2
+    chi2 = np.sum(((fobs-s*ftmp)/eobs)**2) + np.sum(((fbb-s*ftmp_bb)/ebb)**2)
+    return chi2, s
+
+
+def SFH_del(t0, tau, A, tt=None, minsfr = 1e-10):
+    '''
+    SFH
+    '''
+    try:
+        if tt == None:
+            tt = np.arange(0.,10,0.1)
+    except:
+        pass
+    sfr = np.zeros(len(tt), dtype='float')+minsfr
+    sfr[:] = A * (tt[:]-t0) * np.exp(-(tt[:]-t0)/tau)
+    con = (tt[:]-t0<0)
+    sfr[:][con] = minsfr
+    return sfr
+
+
+def SFH_dec(t0, tau, A, tt=None, minsfr = 1e-10):
+    '''
+    '''
+    try:
+        if tt == None:
+            tt = np.arange(0.,10,0.1)
+    except:
+        pass
+    sfr = np.zeros(len(tt), dtype='float')+minsfr
+    sfr[:] = A * (np.exp(-(tt[:]-t0)/tau))
+    con = (tt[:]-t0<0)
+    sfr[:][con] = minsfr
+    return sfr
+
+
+def SFH_cons(t0, tau, A, tt=None, minsfr = 1e-10):
+    '''
+    '''
+    try:
+        if tt == None:
+            tt = np.arange(0.,10,0.1)
+    except:
+        pass
+    sfr = np.zeros(len(tt), dtype='float')+minsfr
+    sfr[:] = A #* (np.exp(-(tt[:]-t0)/tau))
+    con = (tt[:]<t0) | (tt[:]>tau)
+    sfr[:][con] = minsfr
+    return sfr
+
+
+def get_Fint(lmtmp, ftmp, lmin=1400, lmax=1500):
+    '''
+    Parameters
+    ----------
+    lmtmp : 
+        Rest frame wave (AA)
+    ftmp :
+        Fnu ()
+
+    Returns
+    -------
+    integrated flux.
+    '''
+    
+    con = (lmtmp>lmin) & (lmtmp<lmax) & (ftmp>0)
+    if len(lmtmp[con])>0:
+        lamS,spec = lmtmp[con], ftmp[con] # Two columns with wavelength and flux density
+        # Equivalent to the following;
+        I1 = simps(spec*lamS*1.,lamS)
+        I2 = simps(lamS*1.,lamS)
+        fnu = I1
+    return fnu
+
+
+def get_Fuv(lmtmp, ftmp, lmin=1400, lmax=1500):
+    '''
+    Purpose
+    -------
+    Get RF UV (or any wavelength) flux density.
+
+    Parameters
+    ----------
+    lmtmp : 
+        Rest-frame wavelength, in AA.
+    ftmp : 
+        Fnu
+    
+    Returns
+    -------
+    Flux density estimated over lmin:lmax. Not integrated sum.
+    '''
+    con = (lmtmp>lmin) & (lmtmp<lmax) & (ftmp>0)
+    if len(lmtmp[con])>0:
+        lamS,spec = lmtmp[con], ftmp[con] # Two columns with wavelength and flux density
+        I1 = simps(spec*lamS*1.,lamS)
+        I2 = simps(lamS*1.,lamS)
+        fnu = I1/I2
+    else:
+        fnu = None
+    return fnu
+
+
+def data_int(lmobs, lmtmp, ftmp):
+    '''
+
+    Parameters
+    ----------
+    lmobs : 
+        Observed wavelength.
+    lmtmp, ftmp: 
+        Those to be interpolated.
+    '''
+    ftmp_int  = np.interp(lmobs,lmtmp,ftmp) # Interpolate model flux to observed wavelength axis.
+    return ftmp_int
+
+
+def fnutonu(fnu, m0set=25.0, m0input=-48.6):
+    '''
+    Converts from Fnu (cgs) to Fnu (m0=m0set)
+    
+    Parameters
+    ----------
+    fnu : float array
+        flux in cgs, with magnitude zero point of m0input.
+    m0set : float
+        Target mag zero point.
+    m0input : float
+        Original value for magzp. If erg/s/cm2/Hz, -48.6.
+    '''
+    Ctmp = 10**((m0set-m0input)/2.5)
+    fnu_new = fnu * Ctmp
+    return fnu_new
+
+
+def flamtonu(lam, flam, m0set=25.0):
+    '''
+    Converts from Flam to Fnu, with mag zeropoint of m0set.
+    
+    '''
+    Ctmp = lam**2/c * 10**((48.6+m0set)/2.5) #/ delx_org
+    fnu  = flam * Ctmp
+    return fnu
+
+
+def fnutolam(lam, fnu, m0set=25.0, m0=-48.6):
+    '''
+    Converts from Fnu to Flam, from mag zeropoint of m0set (to -48.6).
+
+    Parameters
+    ----------
+    m0set : float
+        current magzp.
+    m0 : float
+        target magzp. The default, -48.6, is for flam (erg/s/cm2/lambda).
+    '''
+    Ctmp = lam**2/c * 10**((m0set-m0)/2.5)
+    flam = fnu / Ctmp
+    return flam
+
+
+def delta(x,A):
+    '''
+    '''
+    yy = np.zeros(len(x),float)
+    iix = np.argmin(np.abs(x))
+    if len(x)%2 == 0:
+        logger= logging.getLogger( __name__ )
+        print(logger)
+        print('Input array has an even number.')
+        yy[iix] = A/2.
+        yy[iix+1] = A/2.
+    else:
+        yy[iix] = A
+    return yy
+
+
+def gauss(x,A,sig):
+    return A * np.exp(-0.5*x**2/sig**2)
+
+
+def moffat(xx, A, x0, gamma, alp):
+    yy = A * (1. + (xx-x0)**2/gamma**2)**(-alp)
+    return yy
+
+
+def get_fit(x, y, xer, yer, nsfh:str = 'Del.'):
+    '''
+    '''
+    from lmfit import Model, Parameters, minimize, fit_report, Minimizer
+
+    fit_params = Parameters()
+    #fit_params.add('t0', value=1., min=0, max=np.max(tt))
+    fit_params.add('t0', value=.5, min=0, max=14)
+    fit_params.add('tau', value=.1, min=0, max=100)
+    fit_params.add('A', value=1, min=0, max=5000)
+
+    def residual_tmp(pars):
+        vals = pars.valuesdict()
+        t0_tmp, tau_tmp, A_tmp = vals['t0'],vals['tau'],vals['A']
+
+        if nsfh == 'Del.':
+            model = SFH_del(t0_tmp, tau_tmp, A_tmp, tt=x)
+        elif nsfh == 'Decl.':
+            model = SFH_dec(t0_tmp, tau_tmp, A_tmp, tt=x)
+        elif nsfh == 'Cons.':
+            model = SFH_cons(t0_tmp, tau_tmp, A_tmp, tt=x)
+
+        con = (model>0)
+        resid = (np.log10(model[con]) - y[con]) / yer[con]
+        return resid
+
+    out = minimize(residual_tmp, fit_params, method='powell')
+    print(fit_report(out))
+
+    t0 = out.params['t0'].value
+    tau = out.params['tau'].value
+    A = out.params['A'].value
+    param = [t0, tau, A]
+
+    keys = fit_report(out).split('\n')
+    for key in keys:
+        if key[4:7] == 'chi':
+            skey = key.split(' ')
+            csq  = float(skey[14])
+        if key[4:7] == 'red':
+            skey = key.split(' ')
+            rcsq = float(skey[7])
+
+    return param, rcsq
+
+
+def savecpkl(data, cpklfile, verbose=True):
+    """
+    Save data into cpklfile.
+    """
+    if verbose: print(' => Saving data to cpklfile '+cpklfile)
+    f = open(cpklfile,'wb')
+    cPickle.dump(data, f, 2)
+    f.close()
+
+
+def apply_dust(yy, xx, nr, Av, dust_model=0):
+	'''
+	xx : float array
+		RF Wavelength
+	'''
+	if dust_model == 0:
+		yyd, xxd, nrd = dust_calz(xx, yy, Av, nr)
+	elif dust_model == 1:
+		yyd, xxd, nrd = dust_mw(xx, yy, Av, nr)
+	elif dust_model == 2: # LMC
+		yyd, xxd, nrd = dust_gen(xx, yy, Av, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
+	elif dust_model == 3: # SMC
+		yyd, xxd, nrd = dust_gen(xx, yy, Av, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
+	elif dust_model == 4: # Kriek&Conroy with gamma=-0.2
+		yyd, xxd, nrd = dust_kc(xx, yy, Av, nr, Rv=4.05, gamma=-0.2)
+	else:
+		yyd, xxd, nrd = dust_calz(xx, yy, Av, nr)
+	return yyd, xxd, nrd
+
+
+def dust_gen(lm, fl, Av, nr, Rv=4.05, gamma=-0.05, Eb=3.0, lmlimu=3.115, lmv=5000/10000, f_Alam=False):
+    '''
+    For general purpose (Noll+09).
+    This function is much better than previous, but is hard to impliment for the current version.
+    A difference from dust_gen is Eb is defined as a function of gamma.
+
+    Parameters
+    ----------
+    lm : float array
+        wavelength, at RF.
+    fl : float array
+        fnu
+    Av : float
+        in mag
+    nr : int array
+        index, to be used for sorting.
+    Rv : 
+        from Calzetti+00
+    gamma :
+        gamma.
+    Eb:
+        Eb
+    '''
+    Kl = np.zeros(len(lm), dtype='float')
+
+    lmm  = lm/10000. # in micron
+    con1 = (lmm<=0.63)
+    con2 = (lmm>0.63)  & (lmm<=lmlimu)
+    con3 = (lmm>lmlimu)
+
+    Kl1 = (2.659 * (-2.156 + 1.509/lmm[con1] - 0.198/lmm[con1]**2 + 0.011/lmm[con1]**3) + Rv)
+    Kl2 = (2.659 * (-1.857 + 1.040/lmm[con2]) + Rv)
+    Kl3 = (2.659 * (-1.857 + 1.040/lmlimu + lmm[con3] * 0) + Rv)
+
+    #nr0 = nr[con0]
+    nr1 = nr[con1]
+    nr2 = nr[con2]
+    nr3 = nr[con3]
+
+    #lmm0 = lmm[con0]
+    lmm1 = lmm[con1]
+    lmm2 = lmm[con2]
+    lmm3 = lmm[con3]
+
+    #fl0 = fl[con0]
+    fl1 = fl[con1]
+    fl2 = fl[con2]
+    fl3 = fl[con3]
+
+    Kl   = np.concatenate([Kl1,Kl2,Kl3])
+    nrd  = np.concatenate([nr1,nr2,nr3])
+    lmmc = np.concatenate([lmm1,lmm2,lmm3])
+    flc  = np.concatenate([fl1,fl2,fl3])
+
+    # Bump;
+    lm0   = 2175 / 10000 # micron m by Noll et al. (2009)
+    dellm = 350 / 10000 # micron m by Noll et al. (2009)
+    D = Eb * (lmmc * dellm)**2 / ((lmmc**2-lm0**2)**2+(lmmc * dellm)**2)
+    #
+    Alam   = Av / Rv * (Kl + D) * (lmmc / lmv)**gamma
+    fl_cor = flc[:] * 10**(-0.4*Alam[:])
+
+    if f_Alam:
+        return fl_cor, lmmc*10000., nrd, Alam
+    else:
+        return fl_cor, lmmc*10000., nrd
+
+
+def dust_kc(lm, fl, Av, nr, Rv=4.05, gamma=0, lmlimu=3.115, lmv=5000/10000, f_Alam=False):
+    '''
+    Dust model by Kriek&Conroy13
+    
+    Parameters
+    ----------
+    lm : float array
+        RF Wavelength.
+    fl : float array
+        in fnu
+    Av : float
+        in mag
+    nr : int array
+        index, to be used for sorting.
+    Rv : float
+        from Calzetti+00
+    gamma : float
+        See Eq.1
+    '''
+    Kl = np.zeros(len(lm), dtype='float')
+
+    lmm  = lm/10000. # in micron
+    con1 = (lmm<=0.63)
+    con2 = (lmm>0.63)  & (lmm<=lmlimu)
+    con3 = (lmm>lmlimu)
+
+    Kl1 = (2.659 * (-2.156 + 1.509/lmm[con1] - 0.198/lmm[con1]**2 + 0.011/lmm[con1]**3) + Rv)
+    Kl2 = (2.659 * (-1.857 + 1.040/lmm[con2]) + Rv)
+    Kl3 = (2.659 * (-1.857 + 1.040/lmlimu + lmm[con3] * 0) + Rv)
+
+    #nr0 = nr[con0]
+    nr1 = nr[con1]
+    nr2 = nr[con2]
+    nr3 = nr[con3]
+
+    #lmm0 = lmm[con0]
+    lmm1 = lmm[con1]
+    lmm2 = lmm[con2]
+    lmm3 = lmm[con3]
+
+    #fl0 = fl[con0]
+    fl1 = fl[con1]
+    fl2 = fl[con2]
+    fl3 = fl[con3]
+
+    Kl   = np.concatenate([Kl1,Kl2,Kl3])
+    nrd  = np.concatenate([nr1,nr2,nr3])
+    lmmc = np.concatenate([lmm1,lmm2,lmm3])
+    flc  = np.concatenate([fl1,fl2,fl3])
+
+    Eb = 0.85 - 1.9 * gamma
+    #print('Eb is %.2f'%(Eb))
+
+    # Bump;
+    lm0   = 2175 / 10000 # micron m by Noll et al. (2009)
+    dellm = 350 / 10000 # micron m by Noll et al. (2009)
+    D = Eb * (lmmc * dellm)**2 / ((lmmc**2-lm0**2)**2+(lmmc * dellm)**2)
+    #
+    Alam   = Av / Rv * (Kl + D) * (lmmc / lmv)**gamma
+    fl_cor = flc[:] * 10**(-0.4*Alam[:])
+
+    if f_Alam:
+        return fl_cor, lmmc*10000., nrd, Alam
+    else:
+        return fl_cor, lmmc*10000., nrd
+
+
+def dust_calz(lm, fl, Av:float, nr, Rv:float = 4.05, lmlimu:float = 3.115, f_Alam:bool = False):
+    '''
+    Parameters
+    ----------
+    lm : float array
+        wavelength, at RF.
+    fl : float array
+        fnu
+    Av : float
+        in mag
+    nr : int array
+        index, to be used for sorting.
+    Rv : float
+        from Calzetti+00
+    lmlimu : float
+        Upper limit. 2.2 in Calz+00
+    '''
+    Kl = lm[:]*0 #np.zeros(len(lm), dtype='float')
+    nrd = lm[:]*0 #np.zeros(len(lm), dtype='float')
+    lmmc = lm[:]*0 #np.zeros(len(lm), dtype='float')
+    flc = lm[:]*0 #np.zeros(len(lm), dtype='float')
+
+    lmm = lm/10000. # in micron
+    con1 = (lmm<=0.63)
+    con2 = (lmm>0.63)  & (lmm<=lmlimu)
+    con3 = (lmm>lmlimu)
+
+    Kl[con1] = (2.659 * (-2.156 + 1.509/lmm[con1] - 0.198/lmm[con1]**2 + 0.011/lmm[con1]**3) + Rv)
+    Kl[con2] = (2.659 * (-1.857 + 1.040/lmm[con2]) + Rv)
+    Kl[con3] = (2.659 * (-1.857 + 1.040/lmlimu + lmm[con3] * 0) + Rv)
+
+    nrd[con1] = nr[con1]
+    nrd[con2] = nr[con2]
+    nrd[con3] = nr[con3]
+
+    lmmc[con1] = lmm[con1]
+    lmmc[con2] = lmm[con2]
+    lmmc[con3] = lmm[con3]
+
+    flc[con1] = fl[con1]
+    flc[con2] = fl[con2]
+    flc[con3] = fl[con3]
+
+    Alam = Kl * Av / Rv
+    fl_cor = flc[:] * 10**(-0.4*Alam[:])
+
+    if f_Alam:
+        return fl_cor, lmmc*10000., nrd, Alam
+    else:
+        return fl_cor, lmmc*10000., nrd
+
+
+def dust_mw(lm, fl, Av, nr, Rv=3.1, f_Alam=False):
+    '''
+    Parameters
+    ----------
+    lm : float array
+        wavelength, at RF, in AA.
+    fl : float array
+        fnu
+    Av : float
+        mag
+    nr : int array
+        index, to be used for sorting.
+    Rv : float
+        3.1 for MW.
+    '''
+    Kl = np.zeros(len(lm), dtype='float')
+
+    lmm  = lm/10000. # into micron
+    xx   = 1./lmm
+
+    con0 = (xx>=8.0)
+    con1 = (xx>=5.9) & (xx<8.0)
+    con2 = (xx>=3.3) & (xx<5.9)
+    con3 = (xx>=1.1) & (xx<3.3)
+    con4 = (xx<1.1)
+
+    nr0 = nr[con0]
+    nr1 = nr[con1]
+    nr2 = nr[con2]
+    nr3 = nr[con3]
+    nr4 = nr[con4]
+
+    lmm0 = lmm[con0]
+    lmm1 = lmm[con1]
+    lmm2 = lmm[con2]
+    lmm3 = lmm[con3]
+    lmm4 = lmm[con4]
+
+    fl0 = fl[con0]
+    fl1 = fl[con1]
+    fl2 = fl[con2]
+    fl3 = fl[con3]
+    fl4 = fl[con4]
+
+    ax4 =  0.574 * (1./lmm4)**1.61
+    bx4 = -0.527 * (1./lmm4)**1.61
+
+    yy  = ((1./lmm3) - 1.82)
+    ax3 = 1. + 0.17699 * yy - 0.50447 * yy**2 - 0.02427 * yy**3 + 0.72085 * yy**4\
+          + 0.01979 * yy**5 - 0.77530 * yy**6 + 0.32999 * yy**7
+    bx3 = 1.41338 * yy + 2.28305 * yy**2 + 1.07233 * yy**3 - 5.38434 * yy**4\
+          - 0.62251 * yy**5 + 5.30260 * yy**6 - 2.09002 * yy**7
+
+    Fax2 = Fbx2 = lmm2 * 0
+    ax2  = 1.752 - 0.316 * (1./lmm2) - 0.104/(((1./lmm2)-4.67)**2+0.341) + Fax2
+    bx2  = -3.090 + 1.825 * (1./lmm2) + 1.206/(((1./lmm2)-4.62)**2+0.263) + Fbx2
+
+    Fax1 = -0.04473 * ((1./lmm1) - 5.9)**2 - 0.009779 * ((1./lmm1) - 5.9)**3
+    Fbx1 = 0.2130 * ((1./lmm1) - 5.9)**2 + 0.1207 * ((1./lmm1) - 5.9)**3
+    ax1  = 1.752 - 0.316 * (1./lmm1) - 0.104/(((1./lmm1)-4.67)**2+0.341) + Fax1
+    bx1  = -3.090 + 1.825 * (1./lmm1) + 1.206/(((1./lmm1)-4.62)**2+0.263) + Fbx1
+
+    Fax0 = -0.04473 * ((1./lmm0) - 5.9)**2 - 0.009779 * ((1./lmm0) - 5.9)**3
+    Fbx0 = 0.2130 * ((1./lmm0) - 5.9)**2 + 0.1207 * ((1./lmm0) - 5.9)**3
+    ax0  = 1.752 - 0.316 * (1./lmm0) - 0.104/(((1./lmm0)-4.67)**2+0.341) + Fax0
+    bx0  = -3.090 + 1.825 * (1./lmm0) + 1.206/(((1./lmm0)-4.62)**2+0.263) + Fbx0
+
+    nrd  = np.concatenate([nr0,nr1,nr2,nr3,nr4])
+    lmmc = np.concatenate([lmm0,lmm1,lmm2,lmm3,lmm4])
+    flc  = np.concatenate([fl0,fl1,fl2,fl3,fl4])
+    ax   = np.concatenate([ax0,ax1,ax2,ax3,ax4])
+    bx   = np.concatenate([bx0,bx1,bx2,bx3,bx4])
+
+    Alam   = Av * (ax + bx / Rv)
+
+    if False:
+        import matplotlib.pyplot as plt
+        plt.plot(1/lmmc,Alam/Av,linestyle='-')
+        plt.xlim(0,3)
+        plt.ylim(0,2.0)
+        plt.show()
+
+    fl_cor = flc[:] * 10**(-0.4*Alam[:])
+
+    if f_Alam:
+        return fl_cor, lmmc*10000., nrd, Alam
+    else:
+        return fl_cor, lmmc*10000., nrd
+
+
+def check_line(data,wave,wht,model):
+    '''
+    '''
+    R_grs = 50
+    dw  = 10
+    ldw = 5
+    dlw = R_grs * 2
+    lsig = 1.5 # significance of lines.
+    ii0 = 0 + dw
+    ii9 = len(data) - dw
+    wd  = data * wht
+    er  = 1./np.sqrt(wht)
+    wht2 = wht
+
+    for ii in range(ii0, ii9, 1):
+        concont = (((wave>wave[ii]-dw*R_grs) & (wave<wave[ii]-(dw-ldw)*R_grs)) \
+                   | ((wave<wave[ii]+dw*R_grs) & ((wave>wave[ii]+(dw-ldw)*R_grs))))
+
+        xcont = wave[concont]
+        ycont = data[concont]
+        wycont = wht[concont]
+
+        if len(xcont)>5:
+            try:
+                z = np.polyfit(xcont, ycont, 1, w=wycont)
+                p = np.poly1d(z)
+                fconttmp  = p(wave[ii])
+                fconttmp1 = p(wave[ii-1])
+                fconttmp2 = p(wave[ii+1])
+
+                if data[ii] > er[ii]*lsig + fconttmp and data[ii-1] > er[ii-1]*lsig + fconttmp1 and data[ii+1] > er[ii+1]*lsig + fconttmp2:
+                    #print wave[ii]/(1.+zgal), dlw/(1+zgal)
+                    for jj in range(len(LW)):
+                        #print wave[ii],  LW[jj]*(1.+zgal) - dlw, LW[jj]*(1.+zgal) + dlw, LN[jj]
+                        if wave[ii] > LW[jj]*(1.+zgal) - dlw\
+                           and wave[ii] < LW[jj]*(1.+zgal) + dlw:
+                            wht2[ii-dw:ii+dw] *= 0
+                            fLW[jj] = 1
+                            print(p, LN[jj], fconttmp, data[ii], wave[ii]/(1.+zgal))
+                elif wht2[ii] != 0:
+                    wht2[ii] = wht[ii]
+
+            except Exception:
+                print('Error in Line Check.')
+                pass
+
+    return wht2
+
+
+def filconv_cen(band0, l0, f0, DIR='FILT/'):
+    '''
+    Convolution of templates with filter response curves.
+
+    Parameters
+    ----------
+    '''
+
+    fnu  = np.zeros(len(band0), dtype='float')
+    lcen = np.zeros(len(band0), dtype='float')
+    for ii in range(len(band0)):
+        fd = np.loadtxt(DIR + '%s.fil'%str(band0[ii]), comments='#')
+        lfil = fd[:,1]
+        ffil = fd[:,2]
+
+        lmin  = np.min(lfil)
+        lmax = np.max(lfil)
+        imin = 0
+        imax = 0
+
+        lcen[ii] = np.sum(lfil*ffil)/np.sum(ffil)
+
+        lamS,spec = l0, f0 #Two columns with wavelength and flux density
+        lamF,filt = lfil, ffil #Two columns with wavelength and response in the range [0,1]
+        #filt_int = np.interp(lamS,lamF,filt)  #Interpolate Filter to common(spectra) wavelength axis
+        fint = interpolate.interp1d(lamF, filt, kind='nearest', fill_value="extrapolate")
+        filt_int = fint(lamS)
+        filtSpec = filt_int * spec #Calculate throughput
+        wht = 1. #/(er1[con_rf])**2
+
+        if len(lamS)>0: #./3*len(x0[con_org]): # Can be affect results.
+            I1  = simps(spec/lamS**2*c*filt_int*lamS,lamS)   #Denominator for Fnu
+            I2  = simps(filt_int/lamS,lamS)                  #Numerator
+            fnu[ii] = I1/I2/c         #Average flux density
+        else:
+            I1  = 0
+            I2  = 0
+            fnu[ii] = 0
+
+    return lcen, fnu
+
+
+def filconv_fast(filts, band, l0, f0, fw=False):
+    '''
+    Parameters
+    ----------
+    filts, band : 
+        From MB.filts and MB.band, respectively.
+    f0: 
+        Flux for spectrum, in fnu
+    l0: 
+        Wavelength for spectrum, in AA (that matches filter response curve's.)
+
+    '''
+    fnu  = np.zeros(len(filts), dtype='float')
+    lcen = np.zeros(len(filts), dtype='float')
+    if fw:
+        fwhm = np.zeros(len(filts[:]), dtype='float')
+
+    for ii in range(len(filts[:])):
+        lfil = band['%s_lam'%(filts[ii])]
+        ffil = band['%s_res'%(filts[ii])]
+
+        if fw:
+            ffil_cum = np.cumsum(ffil)
+            ffil_cum/= ffil_cum.max()
+            con      = (ffil_cum>0.05) & (ffil_cum<0.95)
+            fwhm[ii] = np.max(lfil[con]) - np.min(lfil[con])
+
+        lmin  = np.min(lfil)
+        lmax  = np.max(lfil)
+        imin  = 0
+        imax  = 0
+
+        con = (l0>lmin) & (l0<lmax) #& (f0>0)
+        lcen[ii] = np.sum(lfil*ffil)/np.sum(ffil)
+        if len(l0[con])>1:
+            lamS,spec = l0[con], f0[con]                     # Two columns with wavelength and flux density
+            lamF,filt = lfil, ffil                 # Two columns with wavelength and response in the range [0,1]
+            #filt_int = np.interp(lamS,lamF,filt)  # Interpolate Filter to the same wavelength axis of l0, or lamS
+            fint = interpolate.interp1d(lamF, filt, kind='nearest', fill_value="extrapolate")
+            filt_int = fint(lamS)
+
+            wht = 1.
+
+
+            # This does not work sometimes;
+            delS = lamS[1]-lamS[0]
+            I1 = np.sum(spec/lamS**2*c*filt_int*lamS*delS)   #Denominator for Fnu
+            I2 = np.sum(filt_int/lamS*delS)                  #Numerator
+            if I2>0:
+                fnu[ii] = I1/I2/c #Average flux density
+            else:
+                fnu[ii] = 0
+        else:
+            fnu[ii] = 0
+
+    if fw:
+        return lcen, fnu, fwhm
+    else:
+        return lcen, fnu
+
+
+def filconv(band0, l0, f0, DIR, fw=False, f_regist=True, MB=None):
+    '''
+    Parameters
+    ----------
+    f0 : float array
+        Flux for spectrum, in fnu
+    l0 : float array
+        Wavelength for spectrum, in AA (that matches filter response curve's.)
+    f_regist : bool
+        If True, read filter response curves and register those to MB.
+    '''
+    if MB==None:
+        f_regist = True
+    if f_regist:
+        lfil_lib = {}
+        ffil_lib = {}
+
+    fnu = np.zeros_like(band0, dtype=float)
+    lcen = np.zeros_like(band0, dtype=float)
+    if fw:
+        fwhm = np.zeros_like(band0, dtype=float)
+    
+    for ii in range(len(band0)):
+        if not f_regist:
+            try:
+                lfil = MB.lfil_lib['%s'%str(band0[ii])]
+                ffil = MB.ffil_lib['%s'%str(band0[ii])]
+                lmin = MB.lfil_lib['%s_lmin'%str(band0[ii])]
+                lmax = MB.lfil_lib['%s_lmax'%str(band0[ii])]
+                lcen[ii] = MB.lfil_lib['%s_lcen'%str(band0[ii])]
+                if fw:
+                    fwhm = MB.filt_fwhm
+            except:
+                f_regist = True
+                lfil_lib = {}
+                ffil_lib = {}
+                
+        if f_regist:
+            fd = np.loadtxt(DIR + '%s.fil'%str(band0[ii]), comments='#')
+            lfil = fd[:,1]
+            ffil = fd[:,2]
+            ffil /= np.max(ffil)
+            lmin = np.min(lfil)
+            lmax = np.max(lfil)
+
+            if fw:
+                ffil_cum = np.cumsum(ffil)
+                ffil_cum/= ffil_cum.max()
+                con = (ffil_cum>0.05) & (ffil_cum<0.95)
+                fwhm[ii] = np.max(lfil[con]) - np.min(lfil[con])
+
+            con = (l0>lmin) & (l0<lmax)
+            delw = np.nanmin(np.diff(l0))
+            if delw > np.nanmin(np.diff(ffil)):
+                lfil_new = np.arange(lmin,lmax,delw)
+                fint = interpolate.interp1d(lfil, ffil, kind='nearest', fill_value="extrapolate")
+                ffil = fint(lfil_new)
+                lfil = lfil_new
+
+            lfil_lib['%s'%str(band0[ii])] = lfil
+            ffil_lib['%s'%str(band0[ii])] = ffil
+
+            lcen[ii] = np.sum(lfil*ffil)/np.sum(ffil)
+            lfil_lib['%s_lmin'%str(band0[ii])] = lmin
+            lfil_lib['%s_lmax'%str(band0[ii])] = lmax
+            lfil_lib['%s_lcen'%str(band0[ii])] = lcen[ii]
+
+        con = (l0>lmin) & (l0<lmax) #& (f0>0)
+        if len(l0[con])>1:
+            fint = interp1d(lfil, ffil, kind='nearest', fill_value="extrapolate")
+            filt_int = fint(l0[con])
+
+            # This does not work sometimes;
+            #delS = l0[con][1]-l0[con][0]
+            I1 = np.sum(f0[con]/l0[con]**2*c*filt_int*l0[con])
+            I2 = np.sum(filt_int/l0[con])
+            if I2>0:
+                fnu[ii] = I1/I2/c
+            else:
+                fnu[ii] = 0
+        else:
+            fnu[ii] = 0
+
+    if MB != None and f_regist:
+        MB.lfil_lib = lfil_lib
+        MB.ffil_lib = ffil_lib
+        if fw:
+            MB.filt_fwhm = fwhm
+        
+    if fw:
+        return lcen, fnu, fwhm
+    else:
+        return lcen, fnu
+
+
+"""
+def filconv(band0, l0, f0, DIR, fw=False, f_regist=True, MB=None):
+    '''
+    This one does not improve much.
+    
+    Parameters
+    ----------
+    f0 : float array
+        Flux for spectrum, in fnu
+    l0 : float array
+        Wavelength for spectrum, in AA (that matches filter response curve's.)
+    f_regist : bool
+        If True, read filter response curves and register those to MB.
+    '''
+    if MB==None:
+        f_regist = True
+    if f_regist:
+        lfil_lib = {}
+        ffil_lib = {}
+
+    if fw:
+        fwhm = np.zeros_like(band0, dtype=float)
+
+    if not f_regist:
+        try:
+            ffil_lib = MB.ffil_lib
+            if fw:
+                fwhm = MB.filt_fwhm
+        except:
+            f_regist = True
+            ffil_lib = {}
+            # lfils = np.zeros_like(band0, dtype=float)
+            # ffils = np.zeros_like(band0, dtype=float)
+            # lmins = np.zeros_like(band0, dtype=float)
+            # lmaxs = np.zeros_like(band0, dtype=float)
+
+    if f_regist:
+        lcen = np.zeros_like(band0, dtype=float)
+        for ii in range(len(band0)):
+            ffil_lib['%s'%band0[ii]] = {}
+            
+            fd = np.loadtxt(DIR + '%s.fil'%str(band0[ii]), comments='#')
+            lfil = fd[:,1]
+            ffil = fd[:,2]
+            ffil /= np.max(ffil)
+            lmin = np.min(lfil)
+            lmax = np.max(lfil)
+
+            if fw:
+                ffil_cum = np.cumsum(ffil)
+                ffil_cum/= ffil_cum.max()
+                con = (ffil_cum>0.05) & (ffil_cum<0.95)
+                fwhm[ii] = np.max(lfil[con]) - np.min(lfil[con])
+
+            con = (l0>lmin) & (l0<lmax)
+            delw = np.nanmin(np.diff(l0))
+            if delw > np.nanmin(np.diff(ffil)):
+                lfil_new = np.arange(lmin,lmax,delw)
+                fint = interpolate.interp1d(lfil, ffil, kind='nearest', fill_value="extrapolate")
+                ffil = fint(lfil_new)
+                lfil = lfil_new
+
+            ffil_lib['%s'%band0[ii]]['lfil'] = lfil
+            ffil_lib['%s'%band0[ii]]['ffil'] = ffil
+            ffil_lib['%s'%band0[ii]]['lmin'] = lmin
+            ffil_lib['%s'%band0[ii]]['lmax'] = lmax
+            lcen[ii] = np.sum(lfil*ffil)/np.sum(ffil)
+        
+        ffil_lib['lcen'] = lcen
+
+    fnu = np.zeros_like(band0, dtype=float)
+    for ii in range(len(band0)):
+        con = (l0>ffil_lib['%s'%band0[ii]]['lmin']) & (l0<ffil_lib['%s'%band0[ii]]['lmax']) #& (f0>0)
+
+        if len(l0[con])>1:
+            fint = interp1d(ffil_lib['%s'%band0[ii]]['lfil'], ffil_lib['%s'%band0[ii]]['ffil'], kind='nearest', fill_value="extrapolate")
+            filt_int = fint(l0[con])
+
+            # This does not work sometimes;
+            I1 = np.sum(f0[con]/l0[con]**2*c*filt_int*l0[con])
+            I2 = np.sum(filt_int/l0[con])
+            if I2>0:
+                fnu[ii] = I1/I2/c
+            else:
+                fnu[ii] = 0
+        else:
+            fnu[ii] = 0
+
+    if MB != None and f_regist:
+        MB.ffil_lib = ffil_lib
+        if fw:
+            MB.filt_fwhm = fwhm
+        
+    if fw:
+        return ffil_lib['lcen'], fnu, fwhm
+    else:
+        return ffil_lib['lcen'], fnu
+"""
+
+
+def fil_fwhm(band0, DIR):
+    '''
+    Parameters
+    ----------
+    f0 :
+        in fnu
+    '''
+    fwhm = np.zeros(len(band0), dtype='float')
+    for ii in range(len(band0)):
+        fd = np.loadtxt(DIR + band0[ii] + '.fil', comments='#')
+        lfil = fd[:,1]
+        ffil = fd[:,2]
+
+        fsum = np.sum(ffil)
+        fcum = np.zeros(len(ffil), dtype='float')
+        lam0,lam1 = 0,0
+
+        for jj in range(len(ffil)):
+            fcum[jj] = np.sum(ffil[:jj])/fsum
+            if lam0 == 0 and fcum[jj]>0.05:
+                lam0 = lfil[jj]
+            if lam1 == 0 and fcum[jj]>0.95:
+                lam1 = lfil[jj]
+
+        fwhm[ii] = lam1 - lam0
+
+    return fwhm
+
+
+def calc_Dn4(x0, y0, z0):
+    con1 = (x0/(1+z0)>3750) & (x0/(1+z0)<3950)
+    con2 = (x0/(1+z0)>4050) & (x0/(1+z0)<4250)
+    D41  = np.average(y0[con1])
+    D42  = np.average(y0[con2])
+    if D41>0 and D42>0:
+        D4 = D42/D41
+        return D4
+    else:
+        return -99
+
+
+def gaus(x,a,x0,sigma):
+    return a*exp(-(x-x0)**2/(2*sigma**2))
+
+
+def detect_line(xcont, ycont, wycont, zgal):
+    ################
+    # Line library
+    ################
+    LN = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3', 'Halpha', 'S2L', 'S2H']
+    LW = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4983, 6563, 6717, 6731]
+    fLW = np.zeros(len(LW), dtype='int') # flag.
+
+    R_grs = 50
+    dw  = 5
+    ldw = 5
+    dlw = R_grs * 2
+    lsig = 3 # significance of lines.
+    er   = 1./np.sqrt(wycont)
+
+    con = (xcont<20000)
+    z = np.polyfit(xcont[con], ycont[con], 5, w=wycont[con])
+    p = np.poly1d(z)
+    ypoly = p(xcont)
+
+    wht2   = wycont
+    flag_l = 0
+
+    for ii in range(len(xcont)):
+        if ycont[ii] > er[ii]*lsig + ypoly[ii] and ycont[ii-1] > er[ii-1]*lsig + ypoly[ii-1] and ycont[ii+1] > er[ii+1]*lsig + ypoly[ii+1] and wht2[ii]:
+            for jj in range(len(LW)):
+                if xcont[ii]/(1.+zgal) > LW[jj] -  dw and xcont[ii]/(1.+zgal) < LW[jj] +  dw:
+                    wht2[ii-dw:ii+dw] *= 0
+                    flag_l  = 1
+                    fLW[jj] = 1
+
+    return wht2,flag_l
+
+
+def check_line_cz(ycont,xcont,wycont,model,zgal):
+    er = 1./np.sqrt(wycont)
+    try:
+        wht2, flag_l = detect_line(xcont, ycont, wycont, zgal)
+        if flag_l == 1:
+            wycont = wht2
+            wht2, flag_l = detect_line(xcont, ycont, wycont,zgal)
+    except Exception:
+        wht2 = wycont
+        pass
+
+    z = np.polyfit(xcont, ycont, 5, w=wht2)
+    p = np.poly1d(z)
+    ypoly = p(xcont)
+
+    return wht2, ypoly
+
+
+def check_line_cz_man(ycont,xcont,wycont,model,zgal,LW=LW0,norder=5.):
+    '''
+    Parameters
+    ----------
+    LW : 
+        List for emission lines to be masked.
+
+    Returns
+    -------
+    wht : 
+        Processed weight, where wavelength at line exists is masked.
+    ypoly : 
+        Fitted continuum flux.
+    '''
+
+    er  = 1./np.sqrt(wycont)
+    try:
+        wht2, flag_l = detect_line_man(xcont, ycont, wycont, zgal, LW, model)
+    except Exception:
+        print('Error in Line Check.')
+        wht2 = wycont
+        pass
+
+    z     = np.polyfit(xcont, ycont, norder, w=wht2)
+    p     = np.poly1d(z)
+    ypoly = p(xcont)
+
+    return wht2, ypoly
+
+def detect_line_man(xcont, ycont, wycont, zgal, LW, model):
+    ################
+    # Line library
+    ################
+    #LN = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3', 'Halpha', 'S2L', 'S2H']
+    #LW = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4983, 6563, 6717, 6731]
+    fLW = np.zeros(len(LW), dtype='int') # flag.
+
+    #R_grs = 45
+    #R_grs = 23.0
+    R_grs = (xcont[1] - xcont[0])
+    dw   = 1
+    lsig = 1.5 # significance of lines.
+    er   = 1./np.sqrt(wycont)
+
+    con   = (xcont<20000)
+    z     = np.polyfit(xcont[con], ycont[con], 5, w=wycont[con])
+    p     = np.poly1d(z)
+    ypoly = p(xcont)
+
+    wht2   = wycont
+    flag_l = 0
+
+    for ii in range(len(xcont)):
+        if 1 > 0:
+            for jj in range(len(LW)):
+                if xcont[ii]/(1.+zgal) > LW[jj] - dw*R_grs and xcont[ii]/(1.+zgal) < LW[jj] + dw*R_grs:
+                    wht2[int(ii-dw):int(ii+dw)] *= 0
+                    flag_l  = 1
+
+    return wht2,flag_l
+
+
+def check_line_man(data,xcont,wht,model,zgal,LW=LW0,lsig=1.5):
+    '''
+    Parameters
+    ----------
+    lsig : float
+        which sigma to detect lines.
+    '''
+    fLW = np.zeros(len(LW), dtype='int') # flag.
+    R_grs = (xcont[1] - xcont[0])
+    dw    = 1.
+
+    er = wht * 0
+    con_wht = (wht>0)
+    er[con_wht] = 1./np.sqrt(wht[con_wht])
+
+    wht2 = np.zeros(len(wht),'float')
+    wht2[:]= wht[:]
+    flag_l = 0
+
+    for ii in range(len(xcont)):
+        for jj in range(len(LW)):
+            if LW[jj]>0:
+                if xcont[ii]/(1.+zgal) > LW[jj] - dw*R_grs and xcont[ii]/(1.+zgal) < LW[jj] + dw*R_grs:
+                    wht2[int(ii-dw):int(ii+dw)] *= 0
+                    flag_l  = 1
+
+    return wht2
diff --git a/build/lib/gsf/function_class.py b/build/lib/gsf/function_class.py
new file mode 100644
index 0000000..d4a20f0
--- /dev/null
+++ b/build/lib/gsf/function_class.py
@@ -0,0 +1,1179 @@
+import numpy as np
+import sys
+import scipy.interpolate as interpolate
+import asdf
+from astropy.io import fits
+
+from .function import *
+from .basic_func import Basic
+from .function_igm import dijkstra_igm_abs
+from .maketmp_filt import maketemp,maketemp_tau
+
+
+class Func:
+    '''
+    The list of (possible) `Func` attributes is given below:
+
+    Attributes
+    ----------
+    '''
+    def __init__(self, MB, dust_model:int = 0):
+        '''
+        Parameters
+        ----------
+        dust_model : int
+            0 for Calzetti.
+        '''
+        self.ID = MB.ID
+        self.ZZ = MB.Zall
+        self.age = MB.age
+        self.AA = MB.nage
+        self.tau0 = MB.tau0
+        self.MB = MB
+
+        self.dust_model = dust_model
+        self.DIR_TMP = MB.DIR_TMP
+
+        if MB.f_dust:
+            self.Temp = MB.Temp
+
+        try:
+            self.filts = MB.filts
+            self.DIR_FIL = MB.DIR_FILT
+        except:
+            pass
+
+        # Already Read or not;
+        self.f_af = False
+        self.f_af0 = False
+
+
+    def open_spec_fits(self, fall:int = 0, orig:bool = False, f_neb=False):
+        '''
+        Load template in obs range.
+        '''
+        ID0 = self.MB.ID
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+        tau0 = np.arange(0,1,1)
+        ntau0 = len(tau0)
+
+        # ASDF;
+        if fall == 0:
+            app = ''
+            hdu0 = self.MB.af['spec']
+        elif fall == 1:
+            app = 'all_'
+            hdu0 = self.MB.af['spec_full']
+
+        if not f_neb:
+            DIR_TMP = self.DIR_TMP
+            for pp in range(ntau0):
+                for zz in range(len(ZZ)):
+                    Z = ZZ[zz]
+                    NZ = bfnc.Z2NZ(Z)
+                    if zz == 0 and pp == 0:
+                        nr = hdu0['colnum']
+                        xx = hdu0['wavelength']
+                        lib = np.zeros((len(nr), 2+len(AA)*len(ZZ)*len(tau0)), dtype='float')
+                        lib[:,0] = nr[:]
+                        lib[:,1] = xx[:]
+
+                    for aa in range(self.MB.npeak):
+                        coln = int(2+aa)
+                        if orig:
+                            colname = 'fspec_orig_' + str(zz) + '_' + str(aa) + '_' + str(pp)
+                        else:
+                            colname = 'fspec_' + str(zz) + '_' + str(aa) + '_' + str(pp)
+                        colnall = int(2 + pp*len(ZZ)*len(AA) + zz*len(AA) + aa) # 2 takes account of wavelength and AV columns.
+                        lib[:,colnall] = hdu0[colname]
+        else:
+            NZ = len(ZZ)
+            NU = len(self.MB.logUs)
+            for zz,Z in enumerate(ZZ):
+                for uu,logU in enumerate(self.MB.logUs):
+                    if zz == 0 and uu == 0:
+                        nr = hdu0['colnum']
+                        xx = hdu0['wavelength']
+                        coln = int(2 + NZ * NU)
+                        lib = np.zeros((len(nr), coln), dtype=float)
+                        lib[:,0] = nr[:]
+                        lib[:,1] = xx[:]
+                    if orig:
+                        colname = 'fspec_orig_nebular_Z%d'%zz + '_logU%d'%uu
+                    else:
+                        colname = 'fspec_nebular_Z%d'%zz + '_logU%d'%uu
+                    colnall = int(2 + zz * NU + uu) # 2 takes account of wavelength and AV columns.
+                    lib[:,colnall] = hdu0[colname]
+
+        return lib
+
+
+    def open_spec_dust_fits(self, fall:int = 0):
+        '''
+        Loads dust template in obs range.
+        '''
+        ID0 = self.MB.ID
+        tau0= self.MB.tau0
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+
+        if fall == 0:
+            app = ''
+            hdu0 = self.MB.af['spec_dust']
+        elif fall == 1:
+            app = 'all_'
+            hdu0 = self.MB.af['spec_dust_full']
+
+        DIR_TMP = self.DIR_TMP
+        nr = hdu0['colnum']
+        xx = hdu0['wavelength']
+        
+        lib = np.zeros((len(nr), 2+len(self.Temp)), dtype='float')
+        lib[:,0] = nr[:]
+        lib[:,1] = xx[:]
+
+        for aa in range(len(self.Temp)):
+            coln = int(2 + aa)
+            colname = 'fspec_' + str(aa)
+            colnall = int(2 + aa)
+            lib[:,colnall] = hdu0[colname]
+            if fall==1 and False:
+                import matplotlib.pyplot as plt
+                plt.close()
+                plt.plot(lib[:,1],lib[:,coln],linestyle='-')
+                plt.show()
+        return lib
+
+
+    """
+    def open_spec_neb_fits(self, fall:int = 0, orig:bool = False):
+        '''
+        Loads template in obs range.
+        '''
+        ID0 = self.MB.ID
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+
+        # ASDF;
+        if fall == 0:
+            app = ''
+            hdu0 = self.MB.af['spec']
+        elif fall == 1:
+            app = 'all_'
+            hdu0 = self.MB.af['spec_full']
+
+        DIR_TMP = self.DIR_TMP
+
+        NZ = len(ZZ)
+        NU = len(self.MB.logUs)
+        for zz,Z in enumerate(ZZ):
+            for uu,logU in enumerate(self.MB.logUs):
+                if zz == 0 and uu == 0:
+                    nr = hdu0['colnum']
+                    xx = hdu0['wavelength']
+                    coln = int(2 + NZ * NU)
+                    lib = np.zeros((len(nr), coln), dtype=float)
+                    lib[:,0] = nr[:]
+                    lib[:,1] = xx[:]
+
+                if orig:
+                    colname = 'fspec_orig_nebular_Z%d'%zz + '_logU%d'%uu
+                else:
+                    colname = 'fspec_nebular_Z%d'%zz + '_logU%d'%uu
+                colnall = int(2 + zz * NU + uu) # 2 takes account of wavelength and AV columns.
+                lib[:,colnall] = hdu0[colname]
+
+        return lib
+    """
+
+    """
+    def open_spec_fits_dir(self, nage:int, nz:int, kk, Av:float, zgal:float, A00:float, f_IGM=True):
+        '''
+        Load template in obs range.
+        But for weird template.
+        '''
+        tau0= self.tau0
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+
+        app = 'all'
+        hdu0 = self.MB.af['spec_full']
+        DIR_TMP = self.DIR_TMP
+
+        pp = 0
+        zz = nz
+
+        # Luminosity
+        mshdu = self.MB.af0['ML']
+        Ls = mshdu['Ls_%d'%nz] 
+
+        xx = hdu0['wavelength']
+        nr = np.arange(0,len(xx),1)
+
+        lib = np.zeros((len(nr), 2+1), dtype='float')
+        lib[:,0] = nr[:]
+        lib[:,1] = xx[:]
+
+        aa = nage
+        coln = int(2 + aa)
+        colname = 'fspec_' + str(zz) + '_' + str(aa) + '_' + str(pp)
+
+        yy0 = hdu0[colname]/Ls[aa]
+        yy = flamtonu(xx, yy0, m0set=self.MB.m0set)
+        lib[:,2] = yy[:]
+
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
+
+        xxd *= (1.+zgal)
+        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
+        nrd_yyd[:,0] = nrd[:]
+        nrd_yyd[:,1] = yyd[:]
+        nrd_yyd[:,2] = xxd[:]
+
+        b = nrd_yyd
+        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
+        yyd_sort = nrd_yyd_sort[:,1]
+        xxd_sort = nrd_yyd_sort[:,2]
+
+        return A00 * yyd_sort, xxd_sort
+    """
+
+    def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_get_Mtot=False, f_check_limit=True):
+        '''
+        Purpose
+        -------
+        get total flux for a given set of parameter.
+
+        Parameters
+        ----------
+        par : library
+            contains parameters. Needs to include: ['Z','A']
+
+        Returns
+        -------
+        xx : 
+            OBSERVED wavelength range at z=MB.zgal
+        '''
+        Mtot:float = 0
+        if lib == None:
+            if lib_all:
+                lib = self.MB.lib_all
+            else:
+                lib = self.MB.lib
+
+        for aa in range(self.MB.npeak):
+            if self.MB.ZEVOL==1 or aa == 0:
+                Z = par['Z'+str(aa)]
+                NZ = self.MB.bfnc.Z2NZ(Z)
+
+            if f_check_limit:
+                # Check limit;
+                if par['A'+str(aa)] < self.MB.Amin:
+                    par['A'+str(aa)] = self.MB.Amin
+                if par['A'+str(aa)] > self.MB.Amax:
+                    par['A'+str(aa)] = self.MB.Amax
+                # Z limit:
+                if aa == 0 or self.MB.ZEVOL == 1:
+                    if par['Z%d'%aa] < self.MB.Zmin:
+                        par['Z%d'%aa] = self.MB.Zmin
+                    if par['Z%d'%aa] > self.MB.Zmax:
+                        par['Z%d'%aa] = self.MB.Zmax
+
+            # Is A in logspace?
+            if f_Alog:
+                A00 = 10**par['A'+str(aa)]
+            else:
+                A00 = par['A'+str(aa)]
+
+            coln = int(2 + pp*len(self.ZZ)*len(self.AA) + NZ*len(self.AA) + aa)
+
+            if f_get_Mtot:
+                mslist = self.MB.af['ML']['ML_'+str(NZ)][aa]
+                Mtot += 10**(par['A%d'%aa] + np.log10(mslist))
+
+            if aa == 0:
+                nr = lib[:,0]
+                xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
+                yy = A00 * lib[:,coln]
+            else:
+                yy += A00 * lib[:,coln]
+
+        if f_get_Mtot:
+            return nr, xx, yy, Mtot
+        else:
+            return nr, xx, yy
+
+
+    def get_total_flux_neb(self, par, f_Alog=True, lib_all=True, lib=None):
+        '''
+        Purpose
+        -------
+        get total flux for a given set of parameter.
+
+        Parameters
+        ----------
+        par : library
+            contains parameters
+        '''
+        if lib == None:
+            if lib_all:
+                lib = self.MB.lib_neb_all
+            else:
+                lib = self.MB.lib_neb
+
+        aa = 0
+        if self.MB.ZEVOL==1 or aa == 0:
+            Z = par['Z'+str(aa)]
+            NZ = self.MB.bfnc.Z2NZ(Z)
+
+        try:
+            Aneb = par['Aneb']
+            logU = par['logU']
+            nlogU = np.argmin(np.abs(self.MB.logUs - logU))
+        except: # This is exception for initial minimizing;
+            Aneb = -99
+            logU = self.MB.logUs[0]
+            nlogU = 0
+
+        # logU
+        NU = self.MB.nlogU
+
+        # Check limit;
+        if Aneb < self.MB.Amin:
+            Aneb = self.MB.Amin
+        if Aneb > self.MB.Amax:
+            Aneb = self.MB.Amax
+        # Z limit:
+        if aa == 0 or self.MB.ZEVOL == 1:
+            if par['Z%d'%aa] < self.MB.Zmin:
+                par['Z%d'%aa] = self.MB.Zmin
+            if par['Z%d'%aa] > self.MB.Zmax:
+                par['Z%d'%aa] = self.MB.Zmax
+
+        # Is A in logspace?
+        if f_Alog:
+            A00 = 10**Aneb
+        else:
+            A00 = Aneb
+
+        coln = int(2 + NZ*NU + nlogU)
+
+        if aa == 0:
+            nr = lib[:,0]
+            xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
+            yy = A00 * lib[:,coln]
+        else:
+            yy += A00 * lib[:,coln]
+
+        return nr, xx, yy
+
+    """
+    def get_template_single(self, A00, Av, nmodel, Z, zgal, lib):
+        '''
+        '''
+        tau0= self.tau0
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+        DIR_TMP = self.MB.DIR_TMP
+        NZ = bfnc.Z2NZ(Z)
+
+        pp0 = np.random.uniform(low=0, high=len(tau0), size=(1,))
+        pp = int(pp0[0])
+        if pp>=len(tau0):
+            pp += -1
+
+        coln = int(2 + pp*len(ZZ)*len(AA) + NZ*len(AA) + nmodel)
+        nr = lib[:,0]
+        xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
+        yy = lib[:,coln]
+
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
+
+        xxd *= (1.+zgal)
+
+        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
+        nrd_yyd[:,0] = nrd[:]
+        nrd_yyd[:,1] = yyd[:]
+        nrd_yyd[:,2] = xxd[:]
+
+        b = nrd_yyd
+        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
+        yyd_sort = nrd_yyd_sort[:,1]
+        xxd_sort = nrd_yyd_sort[:,2]
+
+        return A00 * yyd_sort, xxd_sort
+    """
+
+    def get_template_single(self, A00, Av, nmodel, Z, zgal, lib, logU=None, f_apply_dust=True, EBVratio=2.27):
+        '''
+        EBVratio : float
+            E(B-V)_neb / E(B-V)_st. 
+            Useful table in https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf
+
+        Notes
+        -----
+        This function is only used in plot_sed.py.
+        Common function for mebular and nonnebular temlates.
+        '''
+        tau0 = self.tau0
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+        DIR_TMP = self.MB.DIR_TMP
+        NZ = bfnc.Z2NZ(Z)
+
+        pp0 = np.random.uniform(low=0, high=len(tau0), size=(1,))
+        pp = int(pp0[0])
+        if pp>=len(tau0):
+            pp += -1
+
+        if logU != None:
+            NU = len(self.MB.logUs)
+
+            # Dust attenuation to nebulae
+            Av *= EBVratio
+            nlogU = np.argmin(np.abs(self.MB.logUs - logU))
+            coln = int(2 + NZ*NU + nlogU)
+        else:
+            coln = int(2 + pp*len(ZZ)*len(AA) + NZ*len(AA) + nmodel)
+
+        # 
+        nr = lib[:,0]
+        xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
+        yy = lib[:,coln]
+
+        if f_apply_dust:
+            yyd, xxd, nrd = apply_dust(yy, xx/(1+zgal), nr, Av, dust_model=self.dust_model)
+        else:
+            yyd, xxd, nrd = yy, xx, nr
+
+        xxd *= (1.+zgal)
+
+        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
+        nrd_yyd[:,0] = nrd[:]
+        nrd_yyd[:,1] = yyd[:]
+        nrd_yyd[:,2] = xxd[:]
+
+        b = nrd_yyd
+        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
+        yyd_sort = nrd_yyd_sort[:,1]
+        xxd_sort = nrd_yyd_sort[:,2]
+
+        return A00 * yyd_sort, xxd_sort
+
+
+    def get_template(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib_all:bool=False, f_nrd:bool=False, 
+        f_apply_dust:bool=True, f_IGM=True, deltaz_lim=0.1, f_neb=False, EBVratio:float=2.27):
+        '''
+        Purpose
+        -------
+        Makes model template for a given parameter set, ``par``.
+
+        Parameters
+        ----------
+        nprec : int
+            Precision when redshift is refined. 
+        f_apply_dust : bool
+            Apply dust attenuation to nebular emission?
+        EBVratio : float
+            E(B-V)_neb / E(B-V)_st. 
+            Useful table in https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf
+        f_neb : bool
+            Expect to explore nebular template or not.
+
+        Notes
+        -----
+        This function is only used in plot_sed.py.
+        Common function for mebular and nonnebular temlates.
+        '''
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+
+        if f_val:
+            par = par.params
+
+        if self.MB.fzmc == 1:
+            try:
+                zmc = par['zmc'].value
+            except:
+                zmc = self.MB.zgal
+        else:
+            zmc = self.MB.zgal
+
+        # AV limit;
+        if par['Av'] < self.MB.Avmin:
+            par['Av'] = self.MB.Avmin
+        if par['Av'] > self.MB.Avmax:
+            par['Av'] = self.MB.Avmax
+        Av = par['Av']
+
+        if f_neb:
+            # Dust attenuation to nebulae
+            Av *= EBVratio
+
+        if f_neb:
+            # Get total flux;
+            nr, xx, yy = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=lib_all)
+        else:
+            nr, xx, yy = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all)
+
+        # @@@ Filter convolution may need to happpen here
+        if round(zmc,nprec) != round(self.MB.zgal,nprec):
+            if np.abs(zmc - self.MB.zgal) > deltaz_lim and not self.MB.f_spec:
+                # print('!!! zmc (%.3f) is exploring too far from zgal (%.3f).'%(zmc, self.MB.zgal))
+
+                # @@@ This only work for BB only data set.
+                # Get total flux;
+                if lib_all:
+                    nr_full, xx_full, yy_full = nr, xx, yy
+                else:
+                    if f_neb:
+                        nr_full, xx_full, yy_full = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=True)
+                    else:
+                        nr_full, xx_full, yy_full = self.get_total_flux(par, f_Alog=f_Alog, lib_all=True)
+
+                xx_s, yy_s = filconv(self.MB.filts, xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, self.MB.DIR_FILT, MB=self.MB, f_regist=False)
+            else:
+                fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
+                xx_s = xx / (1+self.MB.zgal) * (1+zmc)
+                yy_s = fint(xx_s)
+
+        else:
+            xx_s = xx
+            yy_s = yy
+
+        xx = xx_s
+        yy = yy_s
+
+        if f_apply_dust:
+            yyd, xxd, nrd = apply_dust(yy, xx/(1+zmc), nr, Av, dust_model=self.dust_model)
+            xxd *= (1.+zmc)
+
+            if self.dust_model != 0:
+                nrd_yyd = np.zeros((len(nrd),3), dtype=float)
+                nrd_yyd[:,0] = nrd[:]
+                nrd_yyd[:,1] = yyd[:]
+                nrd_yyd[:,2] = xxd[:]
+                nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
+                nrd[:],yyd[:],xxd[:] = nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
+
+            if not f_nrd:
+                return yyd[:],xxd[:]
+            else:
+                return nrd[:],yyd[:],xxd[:]
+
+        else:
+            if not f_nrd:
+                return yy,xx
+            else:
+                return nr,yy,xx
+
+
+    def tmp04_dust(self, par, nprec=1):
+        '''
+        Makes model template with a given param setself.
+        Also dust attenuation.
+        '''
+        tau0= self.tau0
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+        DIR_TMP = self.MB.DIR_TMP
+
+        try:
+            m_dust = par['MDUST']
+            t_dust = par['TDUST']
+        except: # This is exception for initial minimizing;
+            m_dust = -99
+            t_dust = 0
+
+        nr = self.MB.lib_dust[:,0]
+        xx = self.MB.lib_dust[:,1] # This is OBSERVED wavelength range at z=zgal
+        coln = 2+int(t_dust+0.5)
+        yy = 10**m_dust * self.MB.lib_dust[:,coln]
+
+        if self.MB.fzmc == 1:
+            zmc = par.params['zmc'].value
+        else:
+            zmc = self.MB.zgal
+
+        # How much does this cost in time?
+        if round(zmc,nprec) != round(self.MB.zgal,nprec):
+            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
+            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
+            yy_s = fint(xx_s)
+        else:
+            xx_s = xx
+            yy_s = yy
+
+        return yy_s, xx_s
+
+
+"""
+## Duplicated. To be deprecated.
+    def get_template(self, lib, Amp:float = 1.0, T:float = 1.0, Av:float = 0.0, Z:float = 0.0, zgal:float = 1.0, f_bb:bool = False, fneb=False):
+        '''
+        Gets an element template given a set of parameters.
+        Not necessarily the most efficient way, but easy to use.
+
+        Parameters:
+        -----------
+        lib : dict
+            library dictionary.
+        Amp : float
+            Amplitude of the target template. Note that each template has Lbol = 1e10Lsun.
+        T : float
+            Age, in Gyr.
+        Av : float
+            Dust attenuation, in mag.
+        Z : float
+            Metallicity, in log(Z/Zsun).
+        zgal : float
+            Redshift.
+        f_bb: bool
+            If calculate bb photometry for the spectrum requested.
+
+        Returns
+            flux : float array. Flux in Fnu. 
+            wavelength : float array. Wave in AA.
+            lcen, lflux : , if f_bb==True.
+
+        '''
+        bfnc = self.MB.bfnc
+        DIR_TMP = self.MB.DIR_TMP 
+        NZ  = bfnc.Z2NZ(Z)
+
+        pp0 = np.random.uniform(low=0, high=self.MB.ntau0, size=(1,))
+        pp  = int(pp0[0])
+        if pp>=self.MB.ntau0:
+            pp += -1
+
+        nmodel = np.argmin(np.abs(T-self.age[:]))
+        if T - self.age[nmodel] != 0:
+            print('T=%.2f is not found in age library. T=%.2f is used.'%(T,self.age[nmodel]))
+
+        if fneb:
+            coln = int(2 + pp*len(self.ZZ)*1 + NZ*1 + 0)
+        else:
+            coln = int(2 + pp*len(self.ZZ)*len(self.AA) + NZ*len(self.AA) + nmodel)
+        nr = lib[:, 0]
+        xx = lib[:, 1] # This is OBSERVED wavelength range at z=zgal
+        yy = lib[:, coln]
+
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
+        xxd *= (1.+zgal)
+
+        if self.dust_model != 0:
+            # This may be needed when not calzetti model
+            nrd_yyd = np.zeros((len(nrd),3), dtype=float)
+            nrd_yyd[:,0] = nrd[:]
+            nrd_yyd[:,1] = yyd[:]
+            nrd_yyd[:,2] = xxd[:]
+            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
+            nrd[:],yyd[:],xxd[:] = nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
+
+        if f_bb:
+            fil_cen, fil_flux = filconv_fast(self.MB, xxd, Amp * yyd)
+            return Amp * yyd, xxd, fil_flux, fil_cen
+        else:
+            return Amp * yyd, xxd
+"""
+
+class Func_tau:
+    '''
+    '''
+    def __init__(self, MB, dust_model:int=0):
+        '''
+        Parameters:
+        -----------
+        dust_model : int
+            0 for Calzetti. 1 for MW. 4 for Kriek Conroy
+        '''
+        self.MB = MB
+        self.ID = MB.ID
+        self.ZZ = MB.Zall
+        self.AA = MB.nage
+        self.tau = MB.tau
+
+        self.dust_model = dust_model
+        self.DIR_TMP = MB.DIR_TMP
+
+        if MB.f_dust:
+            self.Temp = MB.Temp
+
+        try:
+            self.filts = MB.filts
+            self.DIR_FIL = MB.DIR_FILT
+        except:
+            pass
+
+        # Already Read or not;
+        self.f_af = False
+        self.f_af0 = False
+
+
+    def open_spec_fits(self, fall:int=0, orig:bool=False):
+        '''
+        Loads template in obs range.
+        '''
+        ID0 = self.MB.ID
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+        DIR_TMP = self.DIR_TMP
+
+        # ASDF;
+        if fall == 0:
+            app = ''
+            hdu0 = self.MB.af['spec']
+        elif fall == 1:
+            app = 'all_'
+            hdu0 = self.MB.af['spec_full']
+
+        NZ = len(ZZ)
+        NT = self.MB.ntau
+        NA = self.MB.nage
+        for zz,Z in enumerate(ZZ):
+            for tt,TT in enumerate(self.MB.tau):                
+                for ss,TA in enumerate(self.MB.ageparam):
+                    if zz == 0 and tt == 0 and ss == 0:
+                        nr = hdu0['colnum']
+                        xx = hdu0['wavelength']
+                        coln = int(2 + NZ * NT * NA)
+                        lib = np.zeros((len(nr), coln), dtype=float)
+                        lib[:,0] = nr[:]
+                        lib[:,1] = xx[:]
+
+                    if orig:
+                        colname = 'fspec_orig_' + str(zz) + '_' + str(tt) + '_' + str(ss)
+                    else:
+                        colname = 'fspec_' + str(zz) + '_' + str(tt) + '_' + str(ss)
+                    colnall = int(2 + zz * NT * NA + tt * NA + ss) # 2 takes account of wavelength and AV columns.
+                    lib[:,colnall] = hdu0[colname]
+
+        return lib
+
+
+    def open_spec_dust_fits(self, fall:int = 0):
+        '''
+        Load dust template in obs range.
+        '''
+        ID0 = self.MB.ID
+        tau0= self.MB.tau0
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc #Basic(ZZ)
+
+        # self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
+        # self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
+
+        if fall == 0:
+            app = ''
+            hdu0 = self.MB.af['spec_dust']
+        elif fall == 1:
+            app = 'all_'
+            hdu0 = self.MB.af['spec_dust_full']
+
+        DIR_TMP = self.DIR_TMP
+        nr = hdu0['colnum']
+        xx = hdu0['wavelength']
+        
+        lib = np.zeros((len(nr), 2+len(self.Temp)), dtype='float')
+        lib[:,0] = nr[:]
+        lib[:,1] = xx[:]
+
+        for aa in range(len(self.Temp)):
+            coln = int(2 + aa)
+            colname = 'fspec_' + str(aa)
+            colnall = int(2 + aa) # 2 takes account of wavelength and AV columns.
+            lib[:,colnall] = hdu0[colname]
+            if fall==1 and False:
+                import matplotlib.pyplot as plt
+                plt.close()
+                plt.plot(lib[:,1],lib[:,coln],linestyle='-')
+                plt.show()
+        return lib
+
+
+    def open_spec_neb_fits(self, fall=0, orig=False):
+        '''
+        Loads template in obs range.
+        '''
+        ID0 = self.MB.ID
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+
+        # ASDF;
+        if fall == 0:
+            app = ''
+            hdu0 = self.MB.af['spec']
+        elif fall == 1:
+            app = 'all_'
+            hdu0 = self.MB.af['spec_full']
+
+        DIR_TMP = self.DIR_TMP
+
+        NZ = len(ZZ)
+        NU = len(self.MB.logUs)
+        for zz,Z in enumerate(ZZ):
+            for uu,logU in enumerate(self.MB.logUs):
+                if zz == 0 and uu == 0:
+                    nr = hdu0['colnum']
+                    xx = hdu0['wavelength']
+                    coln = int(2 + NZ * NU)
+                    lib = np.zeros((len(nr), coln), dtype=float)
+                    lib[:,0] = nr[:]
+                    lib[:,1] = xx[:]
+
+                if orig:
+                    colname = 'fspec_orig_nebular_Z%d'%zz + '_logU%d'%uu
+                else:
+                    colname = 'fspec_nebular_Z%d'%zz + '_logU%d'%uu
+                colnall = int(2 + zz * NU + uu) # 2 takes account of wavelength and AV columns.
+                lib[:,colnall] = hdu0[colname]
+
+        return lib
+
+
+    def open_spec_fits_dir(self, nage, nz, kk, Av, zgal, A00):
+        '''
+        Loads template in obs range.
+        But for weird template.
+        '''
+        tau0= self.tau0
+        ZZ = self.ZZ
+        AA = self.AA
+        bfnc = self.MB.bfnc
+
+        app = 'all'
+        hdu0 = self.MB.af['spec_full']
+        DIR_TMP = self.DIR_TMP
+
+        pp = 0
+        zz = nz
+
+        # Luminosity
+        mshdu = self.MB.af0['ML']
+        Ls = mshdu['Ls_%d'%nz] 
+
+        xx = hdu0['wavelength'] # at RF;
+        nr = np.arange(0,len(xx),1) #hdu0.data['colnum']
+
+        lib = np.zeros((len(nr), 2+1), dtype='float')
+        lib[:,0] = nr[:]
+        lib[:,1] = xx[:]
+
+        aa = nage
+        coln = int(2 + aa)
+        colname = 'fspec_' + str(zz) + '_' + str(aa) + '_' + str(pp)
+
+        yy0 = hdu0[colname]/Ls[aa]
+        yy = flamtonu(xx, yy0, m0set=self.MB.m0set)
+        lib[:,2] = yy[:]
+
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
+        xxd *= (1.+zgal)
+
+        if self.dust_model == 0:
+            return A00 * yyd, xxd
+        else:
+            nrd_yyd = np.zeros((len(nrd),3), dtype='float')
+            nrd_yyd[:,0] = nrd[:]
+            nrd_yyd[:,1] = yyd[:]
+            nrd_yyd[:,2] = xxd[:]
+
+            b = nrd_yyd
+            nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
+            yyd_sort = nrd_yyd_sort[:,1]
+            xxd_sort = nrd_yyd_sort[:,2]
+            return A00 * yyd_sort, xxd_sort
+
+
+    def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, lib_all=False, f_nrd=False):
+        '''
+        Makes model template with a given param set.
+        Also dust attenuation.
+
+        Parameters:
+        -----------
+        nprec : int
+            Precision when redshift is refined. 
+        '''
+        ZZ = self.ZZ
+        AA = self.AA 
+        bfnc = self.MB.bfnc
+        Mtot = 0
+        pp = 0
+
+        if f_val:
+            par = par.params
+
+        if self.MB.fzmc == 1:
+            try:
+                zmc = par['zmc'].value
+            except:
+                zmc = self.MB.zgal
+        else:
+            zmc = self.MB.zgal
+
+        if check_bound:
+            # AV limit;
+            if par['Av'] < self.MB.Avmin:
+                par['Av'] = self.MB.Avmin
+            if par['Av'] > self.MB.Avmax:
+                par['Av'] = self.MB.Avmax
+        Av = par['Av']
+
+        for aa in range(self.MB.npeak):
+            if self.MB.ZEVOL==1 or aa == 0:
+                if check_bound:
+                    # Z limit:
+                    if par['Z%d'%aa] < self.MB.Zmin:
+                        par['Z%d'%aa] = self.MB.Zmin
+                    if par['Z%d'%aa] > self.MB.Zmax:
+                        par['Z%d'%aa] = self.MB.Zmax
+                Z = par['Z%d'%aa]
+            else:
+                pass
+
+            if check_bound:
+                # A
+                if par['A'+str(aa)] < self.MB.Amin:
+                    par['A'+str(aa)] = self.MB.Amin
+                if par['A'+str(aa)] > self.MB.Amax:
+                    par['A'+str(aa)] = self.MB.Amax
+
+                if par['TAU'+str(aa)] < self.MB.taumin:
+                    par['TAU'+str(aa)] = self.MB.taumin
+                if par['TAU'+str(aa)] > self.MB.taumax:
+                    par['TAU'+str(aa)] = self.MB.taumax
+
+                if par['AGE'+str(aa)] < self.MB.agemin:
+                    par['AGE'+str(aa)] = self.MB.agemin
+                if par['AGE'+str(aa)] > self.MB.agemax:
+                    par['AGE'+str(aa)] = self.MB.agemax
+
+            # Is A in logspace?
+            if f_Alog:
+                A00 = 10**par['A'+str(aa)]
+            else:
+                A00 = par['A'+str(aa)]
+
+            tau,age = par['TAU%d'%aa],par['AGE%d'%aa]
+
+            NZ, NT, NA = bfnc.Z2NZ(Z,tau,age)
+            coln = int(2 + NZ*self.MB.ntau*self.MB.npeak + NT*self.MB.npeak + NA)
+            mslist = self.MB.af['ML']['ML_'+str(NZ)+'_'+str(NT)][NA]
+            Mtot += 10**(par['A%d'%aa] + np.log10(mslist))
+
+            if lib_all:
+                if aa == 0:
+                    nr = self.MB.lib_all[:, 0]
+                    xx = self.MB.lib_all[:, 1] # This is OBSERVED wavelength range at z=zgal
+                    yy = A00 * self.MB.lib_all[:, coln]
+                else:
+                    yy += A00 * self.MB.lib_all[:, coln]
+            else:
+                if aa == 0:
+                    nr = self.MB.lib[:, 0]
+                    xx = self.MB.lib[:, 1] # This is OBSERVED wavelength range at z=zgal
+                    yy = A00 * self.MB.lib[:, coln]
+                else:
+                    yy += A00 * self.MB.lib[:, coln]
+
+        # Keep logM
+        self.MB.logMtmp = np.log10(Mtot)
+
+        # Redshift refinement;
+        if round(zmc,nprec) != round(self.MB.zgal,nprec): # Not sure how much this costs in time.
+            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
+            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
+            yy_s = fint(xx_s)
+        else:
+            xx_s = xx
+            yy_s = yy
+
+        xx = xx_s
+        yy = yy_s
+
+        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zmc), nr, Av, dust_model=self.dust_model)
+
+        xxd *= (1.+zmc)
+
+        if self.dust_model == 0:
+            if not f_nrd:
+                return yyd,xxd
+            else:
+                return nrd,yyd,xxd
+        else:
+            nrd_yyd = np.zeros((len(nrd),3), dtype='float')
+            nrd_yyd[:,0] = nrd[:]
+            nrd_yyd[:,1] = yyd[:]
+            nrd_yyd[:,2] = xxd[:]
+            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
+            if not f_nrd:
+                return nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
+            else:
+                return nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
+
+
+    def tmp04_dust(self, par, nprec=1):
+        '''
+        Makes model template with a given param setself.
+        Also dust attenuation.
+        '''
+        bfnc = self.MB.bfnc #Basic(ZZ)
+        DIR_TMP = self.MB.DIR_TMP #'./templates/'
+
+        try:
+            m_dust = par['MDUST']
+            t_dust = par['TDUST']
+        except: # This is exception for initial minimizing;
+            m_dust = -99
+            t_dust = 0
+
+        nr = self.MB.lib_dust[:,0]
+        xx = self.MB.lib_dust[:,1] # This is OBSERVED wavelength range at z=zgal
+        coln= 2+int(t_dust+0.5)
+        yy = 10**m_dust * self.MB.lib_dust[:,coln]
+
+        if self.MB.fzmc == 1:
+            zmc = par.params['zmc'].value
+        else:
+            zmc = self.MB.zgal
+
+        # How much does this cost in time?
+        if round(zmc,nprec) != round(self.MB.zgal,nprec):
+            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
+            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
+            yy_s = fint(xx_s)
+        else:
+            xx_s = xx
+            yy_s = yy
+
+        return yy_s, xx_s
+
+  
+    def get_template_neb(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, 
+        lib_all=False, lib=None, f_nrd=False, f_apply_dust=True):
+        '''
+        Makes model template with a given param set.
+        Also dust attenuation.
+
+        Parameters:
+        -----------
+        nprec : int
+            Precision when redshift is refined. 
+        '''
+        ZZ = self.ZZ
+        AA = self.AA 
+        bfnc = self.MB.bfnc
+        Mtot = 0
+        pp = 0
+
+        if lib == None:
+            if lib_all:
+                lib = self.MB.lib_all
+            else:
+                lib = self.MB.lib
+
+        if f_val:
+            par = par.params
+
+        if self.MB.fzmc == 1:
+            try:
+                zmc = par['zmc'].value
+            except:
+                zmc = self.MB.zgal
+        else:
+            zmc = self.MB.zgal
+
+        if check_bound:
+            # AV limit;
+            if par['Av'] < self.MB.Avmin:
+                par['Av'] = self.MB.Avmin
+            if par['Av'] > self.MB.Avmax:
+                par['Av'] = self.MB.Avmax
+        Av = par['Av']
+
+        try:
+            Aneb = par['Aneb']
+            logU = par['logU']
+            nlogU = np.argmin(np.abs(self.MB.logUs - logU))
+        except: # This is exception for initial minimizing;
+            Aneb = -99
+            logU = self.MB.logUs[0]
+            nlogU = 0
+
+        # logU
+        NU = len(self.MB.logUs)
+        # Check limit;
+        if Aneb < self.MB.Amin:
+            Aneb = self.MB.Amin
+        if Aneb > self.MB.Amax:
+            Aneb = self.MB.Amax
+
+        # Is A in logspace?
+        if f_Alog:
+            A00 = 10**Aneb
+        else:
+            A00 = Aneb
+
+        aa = 0
+        if self.MB.ZEVOL==1 or aa == 0:
+            if check_bound:
+                # Z limit:
+                if par['Z%d'%aa] < self.MB.Zmin:
+                    par['Z%d'%aa] = self.MB.Zmin
+                if par['Z%d'%aa] > self.MB.Zmax:
+                    par['Z%d'%aa] = self.MB.Zmax
+            Z = par['Z%d'%aa]
+            NZ = np.argmin(np.abs(self.MB.Zall-Z))
+
+        coln = int(2 + NZ*NU + nlogU)
+        if aa == 0:
+            nr = lib[:, 0]
+            xx = lib[:, 1] # This is OBSERVED wavelength range at z=zgal
+            yy = A00 * lib[:, coln]
+        else:
+            yy += A00 * lib[:, coln]
+        
+        if round(zmc,nprec) != round(self.MB.zgal,nprec):
+            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
+            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
+            yy_s = fint(xx_s)
+        else:
+            xx_s = xx
+            yy_s = yy
+
+        xx = xx_s
+        yy = yy_s
+
+        if f_apply_dust:
+            yyd, xxd, nrd = apply_dust(yy, xx/(1.+zmc), nr, Av, dust_model=self.dust_model)
+            xxd *= (1.+zmc)
+
+            nrd_yyd = np.zeros((len(nrd),3), dtype=float)
+            nrd_yyd[:,0] = nrd[:]
+            nrd_yyd[:,1] = yyd[:]
+            nrd_yyd[:,2] = xxd[:]
+            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
+
+            if not f_nrd:
+                return nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
+            else:
+                return nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
+
+        else:
+            if not f_nrd:
+                return yy,xx
+            else:
+                return nr, yy,xx
diff --git a/build/lib/gsf/function_igm.py b/build/lib/gsf/function_igm.py
new file mode 100644
index 0000000..5e34b3c
--- /dev/null
+++ b/build/lib/gsf/function_igm.py
@@ -0,0 +1,264 @@
+from scipy import asarray as ar,exp
+import numpy as np
+import sys
+from scipy.integrate import simps
+from scipy import integrate
+
+
+def get_XI(z, zout=6, zin=8):
+	'''
+	Very simplified model.
+	'''
+	zs = np.linspace(zout, zin, 100)
+	if z < zout:
+		XI = 0
+	elif z > zin:
+		XI = 1.
+	else:
+		XI = (z-zout) / (zin-zout) * 1.0
+
+	return XI
+
+
+def get_dtdz(z, zs, dtdzs):
+	'''
+	'''
+	iix = np.argmin(np.abs(zs[:-1]-z))
+	return dtdzs[iix]
+
+
+def masongronke_igm_abs(xtmp, ytmp, zin, cosmo=None, xLL=1216., c=3e18, ckms=3e5, zobs=6, xLLL=1400):
+	'''
+	Purpose
+	-------
+
+	Parameters
+	----------
+	xtmp : float array
+		Rest-frame wavelength, in AA.
+	ytmp : float array
+		flux, in f_lambda.
+	zin : target redshift of IGM application
+
+	Returns
+	-------
+	IGM attenuated flux.
+	'''
+	if cosmo == None:
+		from astropy.cosmology import WMAP9 as cosmo
+
+	tau = np.zeros(len(xtmp), dtype=float)
+	xobs = xtmp * (1.*zin)
+	ytmp_abs = np.zeros(len(ytmp), float)
+	zs = np.linspace(zobs,zin,1000)
+
+	xtmp_obs = xtmp * (1+zin)
+	x_HI = 0.8
+	T = 1e0 #1e4 # K
+	sigma_0 = 5.9e-14 * (T / 1e4)**(-1/2) # cm2
+	a_V = 4.7e-4  * (T / 1e4)**(-1/2) # 
+	nu_a = 2.46e15 #Hz
+	k_B = 1.380649e-23 / (1e3)**2 #m2 kg s-2 K-1 * (km/m)**2 = km2 kg/s2/K
+	m_p = 1.67262192e-27 #kilograms
+	delta_nu_d = nu_a * np.sqrt(2 * k_B * T / m_p / ckms**2)
+
+	dtdzs = (cosmo.age(zs)[0:-1].value - cosmo.age(zs)[1:].value) * 1e9 * 365.25 * 3600 * 24 / np.diff(zs) # s
+
+	# xLL = 1390
+	for ii in range(len(xtmp_obs)):
+		if xtmp[ii] < xLL:
+			tau[ii] = 100
+		elif xtmp[ii] < xLLL:
+			nu = c / xtmp_obs[ii] # Hz
+			x = (nu - nu_a) / delta_nu_d
+			phi_x = get_H(x,a_V) #
+
+			# tau[ii] = integrate.quad(lambda z: ckms * get_dtdz(z, zs, dtdzs) * x_HI * sigma_0 * phi_x, zobs, zin)[0] *  get_column(zin, cosmo)
+			tau[ii] = integrate.quad(lambda z: ckms * get_dtdz(z, zs, dtdzs) * x_HI * (1.88e-7 * (1+z)**3) * sigma_0 * phi_x, zobs, zin)[0]
+			print(tau[ii], xtmp_obs[ii])
+		else:
+			tau[ii] = 1e-9
+
+	# R_b1 = 0.0 # Mpc
+	# x_D = 0.8 # neutral fraction
+	#NH = get_column(zin, cosmo)
+	ytmp_abs = ytmp * np.exp(-tau)
+	# con = ()
+	# ytmp_abs[con] = ytmp[con] * np.exp(-tau[con])
+
+	return ytmp_abs
+
+
+def dijkstra_igm_abs(xtmp, ytmp, zin, cosmo=None, xLL=1216., ckms=3e5, 
+	R_b1=1.0, delta_v_0=600, alpha_x=1.0):
+	'''
+	Purpose
+	-------
+	Apply IMG-attenuation of Dijikstra (2014).
+	https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1323358014000332
+
+	Parameters
+	----------
+	xtmp : float array
+		Rest-frame wavelength, in AA.
+	ytmp : float array
+		flux, in f_lambda.
+	zin : 
+		target redshift of IGM application
+	R_b1 : float
+		Bubble size, in Mpc
+
+	Returns
+	-------
+	IGM attenuated flux.
+	'''
+	import scipy.interpolate as interpolate
+	if cosmo == None:
+		from astropy.cosmology import WMAP9 as cosmo
+
+	tau = np.zeros(len(xtmp), dtype=float)
+	xobs = xtmp * (1.*zin)
+	ytmp_abs = np.zeros(len(ytmp), float)
+
+	xtmp_obs = xtmp * (1+zin)
+
+	x_HI = get_XI(zin) # neutral fraction
+	x_D = alpha_x * x_HI # x_D is not clear..
+	delta_lam = (xtmp - xLL) * (zin + 1)
+	delta_lam_fine = (np.linspace(900,2000,1000) - xLL) * (zin + 1)
+
+	delta_v = ckms * delta_lam_fine / (xLL * (1.+zin))
+	delta_v_b1 = delta_v
+	if R_b1>0:
+		delta_v_b1 += cosmo.H(zin).value * R_b1 / (1.+zin) # km / (Mpc s) * Mpc
+
+	tau_fine = 2.3 * x_D * (delta_v_b1/delta_v_0)**(-1) * ((1+zin)/10)**(3/2)
+	con_tau = (tau_fine < 0) | (delta_v_b1 == 0)
+	tau_fine[con_tau] = 100
+
+	fint = interpolate.interp1d(delta_lam_fine, tau_fine, kind='nearest', fill_value="extrapolate")
+	tau = fint(delta_lam)
+
+	# import matplotlib.pyplot as plt
+	# plt.close()
+	# plt.plot(xtmp, tau[:] )
+	# plt.xlim(1200,1500)
+	# plt.show()
+	# hoge
+
+	ytmp_abs = ytmp * np.exp(-tau)
+
+	return ytmp_abs, x_HI
+
+
+def madau_igm_abs(xtmp, ytmp, zin, cosmo=None, xLL=1216.):
+	'''
+	Purpose
+	-------
+	Apply IMG-attenuation of Madau (1995) of zin to the input flux.
+
+	Parameters
+	----------
+	xtmp : float array
+		Rest-frame wavelength, in AA.
+	ytmp : float array
+		flux, in f_lambda.
+	zin : target redshift of IGM application
+
+	Returns
+	-------
+	IGM attenuated flux.
+	'''
+	if cosmo == None:
+		from astropy.cosmology import WMAP9 as cosmo
+
+	tau = np.zeros(len(xtmp), dtype=float)
+	xobs = xtmp * (1.*zin)
+	ytmp_abs = np.zeros(len(ytmp), float)
+
+	NH = get_column(zin, cosmo)
+	tau = (NH/1.6e17) * (xtmp/xLL)**(3.)
+	con = (xtmp<xLL)
+	ytmp_abs[con] = ytmp[con] * np.exp(-tau[con])
+	con = (xtmp>=xLL)
+	ytmp_abs[con] = ytmp[con]
+
+	return ytmp_abs
+
+
+def get_H(x,a):
+	'''
+	Voigt function
+	'''
+	I = integrate.quad(lambda y: np.exp(-y**2)/(a**2 + (x - y)**2),-np.inf, np.inf)[0]
+	return (a/np.pi)*I
+
+
+def get_sig_lya(lam_o, z_s, T=1e4, c=3e18):
+	'''
+	Parameters
+	----------
+	lam_o : float array
+		Observed wavelength, in AA.
+
+	'''
+	nu0 = 2.466e15 #Hz. Lya freq.
+	delnuL = 9.936e7  #Hz. Natural Line Width.
+
+	nu = c / (lam_o * 1e-8)
+
+	# Assume sigma_Lya = 100km/s.
+	#sigma_lya = 100.
+	Vth = 12.85 * (T/1e4)**(1/2) * 1e5 # cm/s
+	delnuD = (Vth/c) * nu0
+
+	x = (nu - nu0)/delnuD
+	a = delnuL / (2.*delnuD)
+	H = get_H(x,a)
+
+	sig_lya = 1.041e-13 * (T/1e4)**(-1/2) * H / np.sqrt(np.pi)
+
+	return sig_lya
+
+
+def get_nH(z):
+	'''
+	Purpose
+	-------
+	Get HI density by using Cen & Haiman 2000.
+
+	Returns
+	-------
+	HI density in IGM, in cm^-3
+	'''
+	try:
+		nH = np.zeros(len(z),dtype='float')
+	except:
+		nH = 0
+
+	nH = 8.5e-5 * ((1.+z)/8)**3
+	return nH
+
+
+def get_column(zin, cosmo, Mpc_cm=3.08568025e+24, z_r=6.0, delz=0.1):
+	'''
+	Returns
+	-------
+	HI column density of IGM at zin, in cm^-2.
+	'''
+	z = np.arange(z_r, zin, delz)
+	try:
+		nH = np.zeros(len(z),dtype='float')
+	except:
+		nH = 0
+
+	# From Cen & Haiman 2000
+	nH = 8.5e-5 * ((1.+z)/8)**3 # in cm^-3
+	NH = 0
+	for zz in range(len(z)):
+		d1 = cosmo.luminosity_distance(z[zz]-delz).value#, **cosmo)
+		d2 = cosmo.luminosity_distance(z[zz]+delz).value#, **cosmo)
+		dx = (d2 - d1) * Mpc_cm
+		NH += nH[zz] * dx/(1.+z[zz])
+
+	return NH
diff --git a/build/lib/gsf/gsf.py b/build/lib/gsf/gsf.py
new file mode 100644
index 0000000..3b727f8
--- /dev/null
+++ b/build/lib/gsf/gsf.py
@@ -0,0 +1,298 @@
+import numpy as np
+import sys
+import matplotlib.pyplot as plt
+import os.path
+import string
+from astropy.cosmology import WMAP9 as cosmo
+import asdf
+
+# From gsf
+from .fitting import Mainbody
+from .maketmp_filt import maketemp,maketemp_tau
+from .function_class import Func,Func_tau
+from .basic_func import Basic,Basic_tau
+
+import timeit
+start = timeit.default_timer()
+
+
+def run_gsf_template(inputs, fplt=0, tau_lim=0.001, idman=None, nthin=1, delwave=10):
+    '''
+    Purpose
+    -------
+    This is only for 0 and 1, to get templates.
+    Not for fitting, nor plotting.
+
+    '''
+
+    MB = Mainbody(inputs, c=3e18, Mpc_cm=3.08568025e+24, m0set=25.0, pixelscale=0.06, cosmo=cosmo, idman=idman)
+
+    if os.path.exists(MB.DIR_TMP) == False:
+        os.mkdir(MB.DIR_TMP)
+
+    #
+    # Then load Func and Basic with param range.
+    #
+    if MB.SFH_FORM == -99:
+        MB.fnc = Func(MB) # Set up the number of Age/ZZ
+        MB.bfnc = Basic(MB)
+    else:
+        MB.fnc = Func_tau(MB) # Set up the number of Age/ZZ
+        MB.bfnc = Basic_tau(MB)
+
+
+    #
+    # 0. Make basic templates
+    #
+    if fplt == 0 or fplt == 1:
+        #
+        # 0. Make basic templates
+        #
+        if fplt==0:
+            lammax = 40000 * (1.+MB.zgal) # AA
+            if MB.f_dust:
+                lammax = 2000000 * (1.+MB.zgal) # AA
+
+            if MB.SFH_FORM == -99:
+                if MB.f_bpass == 1:
+                    from .maketmp_z0 import make_tmp_z0_bpass
+                    make_tmp_z0_bpass(MB, lammax=lammax)
+                else:
+                    from .maketmp_z0 import make_tmp_z0
+                    make_tmp_z0(MB, lammax=lammax)
+            else:
+                from .maketmp_z0_tau import make_tmp_z0
+                make_tmp_z0(MB, lammax=lammax)            
+
+    #
+    # 1. Start making redshifted templates.
+    #
+    if fplt<2:
+        #
+        # 1. Start making redshifted templates.
+        #
+        if MB.SFH_FORM == -99:
+            maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+        else:
+            maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+
+    # Read temp from asdf;
+    # This has to happend after fplt==1 and before fplt>=2.
+    # MB.af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
+
+    '''
+    #
+    # 2. Load templates
+    #
+    MB.lib = MB.fnc.open_spec_fits(MB, fall=0)
+    MB.lib_all = MB.fnc.open_spec_fits(MB, fall=1)
+    if MB.f_dust:
+        MB.lib_dust = MB.fnc.open_spec_dust_fits(MB, fall=0)
+        MB.lib_dust_all = MB.fnc.open_spec_dust_fits(MB, fall=1)
+    '''
+
+    # How to get SED?
+    if False:
+        import matplotlib.pyplot as plt
+        for T in MB.age[:1]:
+            y0, x0 = MB.fnc.get_template(MB.lib_all, Amp=1.0, T=T, Av=0.0, Z=-1.0, zgal=MB.zgal)
+            plt.plot(x0/(1.+MB.zgal),y0,linestyle='-',lw=1.0, label='$T=%.2f$\n$z=%.2f$'%(T,MB.zgal))
+            plt.xlim(600,20000)
+            plt.xlabel('Rest frame wavelength')
+            plt.xscale('log')
+        plt.legend(loc=0)
+        plt.show()
+
+    return MB
+
+
+def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=None, f_label=True, f_symbol=True, 
+    f_SFMS=False, f_fill=True, save_sed=True, figpdf=False, mmax=300, skip_sfh=False, f_fancyplot=False, 
+    skip_zhist=False, tau_lim=0.001, tset_SFR_SED=0.1, f_shuffle=False, amp_shuffle=1e-2, Zini=None, 
+    nthin=1, delwave=1, f_plot_resid=False, scale=1e-19, f_plot_filter=True, f_prior_sfh=False, norder_sfh_prior=3):
+    '''
+    Purpose
+    -------
+    gsf pipeline, which runs all steps.
+
+    Parameters
+    ----------
+    delwave : float
+        If >0, the input templates get smoothing to delwave. 
+        For fsps, this seems to be critical, so it has the same delwave over the template wavelength range.
+    '''
+
+    ######################
+    # Read from Input file
+    ######################
+    from .function import read_input
+    inputs = read_input(parfile)
+
+    MB = Mainbody(inputs, c=3e18, Mpc_cm=3.08568025e+24, m0set=25.0, pixelscale=0.06, cosmo=cosmo, idman=idman, zman=zman)
+    
+    # Register some params;
+    MB.tau_lim = tau_lim
+    MB.nthin = nthin
+    MB.delwave = delwave
+
+    if os.path.exists(MB.DIR_TMP) == False:
+        os.mkdir(MB.DIR_TMP)
+
+    #
+    # Then load Func and Basic with param range.
+    #
+    if MB.SFH_FORM == -99:
+        MB.fnc = Func(MB) # Set up the number of Age/ZZ
+        MB.bfnc = Basic(MB)
+    else:
+        MB.fnc = Func_tau(MB) # Set up the number of Age/ZZ
+        MB.bfnc = Basic_tau(MB)
+
+    #
+    # Make templates based on input redsfift.
+    #
+    flag_suc = True
+    if fplt == 0 or fplt == 1:
+        #
+        # 0. Make basic templates
+        #
+        if fplt==0:
+            lammax = 40000 * (1.+MB.zgal) # AA
+            if MB.f_dust:
+                lammax = 2000000 * (1.+MB.zgal) # AA
+
+            if MB.SFH_FORM == -99:
+                if MB.f_bpass == 1:
+                    from .maketmp_z0 import make_tmp_z0_bpass
+                    make_tmp_z0_bpass(MB, lammax=lammax)
+                else:
+                    from .maketmp_z0 import make_tmp_z0
+                    make_tmp_z0(MB, lammax=lammax)
+            else:
+                from .maketmp_z0_tau import make_tmp_z0
+                make_tmp_z0(MB, lammax=lammax)            
+
+    if not flag_suc:
+        sys.exit()
+
+    # Read temp from asdf;
+    # Template must be registered before fplt>=2.
+    # try:
+    #     aftmp = MB.af
+    # except:
+    #     MB.af = asdf.open(os.path.join(MB.DIR_TMP, 'spec_all_' + MB.ID + '.asdf'))
+
+    if fplt <= 2:
+        MB.zprev = MB.zgal 
+        MB.ndim_keep = MB.ndim
+
+        #
+        # 1. Start making redshifted templates, at z=MB.zgal.
+        #
+        if MB.SFH_FORM == -99:
+            flag_suc = maketemp(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
+        else:
+            flag_suc = maketemp_tau(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
+
+        #
+        # 2. Main fitting part.
+        #
+        flag_suc = MB.main(cornerplot=cornerplot, f_shuffle=f_shuffle, amp_shuffle=amp_shuffle, Zini=Zini, 
+            f_prior_sfh=f_prior_sfh, norder_sfh_prior=norder_sfh_prior)
+
+        while (flag_suc and flag_suc!=2):
+
+            MB.ndim = MB.ndim_keep
+            print('\n\n')
+            print('Making templates...')
+            print('\n\n')
+
+            # Make temp at the new z
+            if MB.SFH_FORM == -99:
+                flag_suc = maketemp(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
+            else:
+                flag_suc = maketemp_tau(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
+
+            print('\n\n')
+            print('Going into another round with updated templates and redshift.')
+            print('\n\n')
+
+            flag_suc = MB.main(cornerplot=cornerplot, f_shuffle=f_shuffle, amp_shuffle=amp_shuffle, Zini=Zini, 
+                f_prior_sfh=f_prior_sfh, norder_sfh_prior=norder_sfh_prior)
+
+        # Total calculation time
+        stop = timeit.default_timer()
+        print('The whole process took;',stop - start)
+
+    if not flag_suc:
+        sys.exit()
+
+    if fplt <= 3 and flag_suc:
+
+        if True: #fplt >= 3: # If main fitting has not been done;
+            # make redshifted templates and register data;
+            
+            # Use the final redshift;
+            from astropy.io import fits
+            hd_sum = fits.open(os.path.join(MB.DIR_OUT, 'summary_%s.fits'%MB.ID))[0].header
+            MB.zgal = hd_sum['ZMC']
+            
+            if MB.SFH_FORM == -99:
+                flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+            else:
+                flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+
+        if MB.SFH_FORM == -99:
+            from .plot_sfh import plot_sfh
+            from .plot_sed import plot_sed            
+        else:
+            from .plot_sfh import plot_sfh_tau as plot_sfh
+            from .plot_sed import plot_sed_tau as plot_sed            
+
+        if not skip_sfh:
+            plot_sfh(MB, fil_path=MB.DIR_FILT, mmax=mmax,
+            dust_model=MB.dust_model, DIR_TMP=MB.DIR_TMP, f_silence=True, 
+            f_SFMS=f_SFMS, f_symbol=f_symbol, skip_zhist=skip_zhist, tau_lim=tau_lim, tset_SFR_SED=tset_SFR_SED)
+
+        plot_sed(MB, fil_path=MB.DIR_FILT,
+        figpdf=figpdf, save_sed=save_sed, mmax=mmax,
+        dust_model=MB.dust_model, DIR_TMP=MB.DIR_TMP, f_label=f_label, f_fill=f_fill, 
+        f_fancyplot=f_fancyplot, f_plot_resid=f_plot_resid, scale=scale, f_plot_filter=f_plot_filter)
+
+    '''
+    if fplt == 4:
+        from .plot_sfh import get_evolv
+        get_evolv(MB, MB.ID, MB.PA, MB.Zall, MB.age, f_comp=MB.ftaucomp, fil_path=MB.DIR_FILT, inputs=MB.inputs, dust_model=MB.dust_model, DIR_TMP=MB.DIR_TMP)
+
+
+    if fplt == 5:
+        from .plot_sfh import plot_evolv
+        plot_evolv(MB, MB.ID, MB.PA, MB.Zall, MB.age, f_comp=MB.ftaucomp, fil_path=MB.DIR_FILT, inputs=MB.inputs, dust_model=MB.dust_model, DIR_TMP=MB.DIR_TMP, nmc=10)
+    '''
+
+    if fplt == 6:
+        # Use the final redshift;
+        from astropy.io import fits
+        hd_sum = fits.open(os.path.join(MB.DIR_OUT, 'summary_%s.fits'%MB.ID))[0].header
+        MB.zgal = hd_sum['ZMC']
+        
+        if MB.SFH_FORM == -99:
+            flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+        else:
+            flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+
+        if MB.SFH_FORM == -99:
+            from .plot_sed import plot_corner_physparam_frame,plot_corner_physparam_summary
+            plot_corner_physparam_summary(MB)
+        else:
+            #from .plot_sed_logA import plot_corner_physparam_summary_tau as plot_corner_physparam_summary
+            print('One for Tau model is TBD...')
+
+
+if __name__ == "__main__":
+    '''
+    '''
+    parfile = sys.argv[1]
+    fplt = int(sys.argv[2])
+
+    run_gsf_all(parfile, fplt)
diff --git a/build/lib/gsf/maketmp_filt.py b/build/lib/gsf/maketmp_filt.py
new file mode 100644
index 0000000..451bc6f
--- /dev/null
+++ b/build/lib/gsf/maketmp_filt.py
@@ -0,0 +1,1517 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy
+import sys
+import os
+from scipy.integrate import simps
+import scipy.interpolate as interpolate
+import asdf
+
+from astropy.io import fits,ascii
+from astropy.modeling.models import Moffat1D
+from astropy.convolution import convolve, convolve_fft
+
+from .function import *
+from .function_igm import *
+
+
+def get_spectrum_draine(lambda_d, DL, zbest, numin, numax, ndmodel, \
+    DIR_DUST='./DL07spec/', phi=0.055, m0set=25.0):
+    '''
+    Parameters
+    ----------
+    lambda_d : array
+        Wavelength array, in AA.
+    phi : float
+        Eq.34 of Draine & Li 2007. (default: 0.055)
+        ~0.055 g / (ergs/s)
+    DL : float
+        in cm.
+
+    Returns
+    -------
+    Interpolated dust emission in Fnu of m0=25.0. In units of Fnu/Msun
+
+    Notes
+    -----
+    umins = ['0.10', '0.15', '0.20', '0.30', '0.40', '0.50', '0.70', '0.80', '1.00', '1.20',\
+            '1.50', '2.00', '2.50', '3.00', '4.00', '5.00', '7.00', '8.00', '10.0', '12.0', '15.0',\
+            '20.0', '25.0']
+    umaxs = ['1e3', '1e4', '1e5', '1e6', '1e7']
+
+    '''
+    from .function import fnutonu
+    import scipy.interpolate as interpolate
+
+    Htokg = 1.66054e-27 # kg/H atom
+    kgtomsun = 1.989e+30 # kg/Msun
+    MsunperH = Htokg / kgtomsun # Msun/H
+    HperMsun = kgtomsun / Htokg # N_Hatom per Msun 
+
+    Jytoerg = 1e-23 # erg/s/cm2/Hz / Jy
+
+    umins = ['0.10', '0.15', '0.20', '0.30', '0.40', '0.50', '0.70', '0.80', '1.00', '1.20',\
+            '1.50', '2.00', '2.50', '3.00', '4.00', '5.00', '7.00', '8.00', '12.0', '15.0',\
+            '20.0', '25.0']
+    umaxs = ['1e3', '1e4', '1e5', '1e6', '1e7']
+        
+    dust_model = DIR_DUST+'draine07_models.txt'
+    fd_model = ascii.read(dust_model)
+
+    umin = umins[numin]
+    umax = umaxs[numax]
+    dmodel = fd_model['name'][ndmodel]
+
+    if ndmodel == 6 or ndmodel == 1:
+        data_start = 55
+    else:
+        data_start = 36
+
+    file_dust = DIR_DUST + 'U%s/U%s_%s_%s.txt'%(umin, umin, umax, dmodel)
+    print(file_dust)
+    fd = ascii.read(file_dust, data_start=data_start)
+
+    wave = fd['col1'] # in mu m.
+    flux = fd['col2'] # nu*Pnu: erg/s H-1
+    flux_dens = fd['col3'] # j_nu, emissivity: Jy cm2 sr-1 H-1
+    #survey = fd['col4'] #
+
+    fobs = flux_dens * Jytoerg / (4.*np.pi*DL**2/(1.+zbest)) * HperMsun
+    # Jy cm2 sr-1 H-1 * erg/s/cm2/Hz / Jy / (cm2 * sr) * (H/Msun) = erg/s/cm2/Hz / Msun
+    # i.e. observed flux density from a dust of Msun at the distance of DL.
+
+    fnu = fnutonu(fobs, m0set=m0set, m0input=-48.6)
+    # Fnu / Msun
+
+    fint = interpolate.interp1d(wave*1e4, fnu, kind='nearest', fill_value="extrapolate")
+    yy_s = fint(lambda_d)
+    con_yys = (lambda_d<1e4) # Interpolation cause some error??
+    yy_s[con_yys] = 0
+
+    return yy_s
+
+
+def sim_spec(lmin, fin, sn):
+    '''
+    SIMULATION of SPECTRA.
+    
+    Parameters
+    ----------
+    wave_obs :
+    wave_temp : 
+    flux_temp : 
+    sn_obs
+
+    Returns
+    -------
+    frand, erand
+    '''
+
+    frand = fin * 0
+    erand = fin * 0
+    for ii in range(len(lmin)):
+        if fin[ii]>0 and sn[ii]>0:
+            erand[ii] = fin[ii]/sn[ii]
+            frand[ii] = np.random.normal(fin[ii],erand[ii],1)
+        else:
+            erand[ii] = 1e10
+            frand[ii] = np.random.normal(fin[ii],erand[ii],1)
+    return frand, erand
+
+
+def check_library(MB, af, nround=3):
+    '''
+    Check library if it has a consistency setup as input file.
+
+    Returns
+    -------
+    flag : bool
+    '''
+
+    # Z needs special care in z0 script, to avoid Zfix.
+    if False:
+        Zmax_tmp, Zmin_tmp = float(MB.inputs['ZMAX']), float(MB.inputs['ZMIN'])
+        delZ_tmp = float(MB.inputs['DELZ'])
+        if Zmax_tmp == Zmin_tmp or delZ_tmp==0:
+            delZ_tmp = 0.0001
+        Zall = np.arange(Zmin_tmp, Zmax_tmp+delZ_tmp, delZ_tmp) # in logZsun
+    else:
+        Zall = MB.Zall
+    
+    flag = True
+    print('Checking the template library...')
+    print('Speficied - Template')
+    # Matallicity:
+    for aa in range(len(Zall)):
+        if Zall[aa] != af['Z%d'%(aa)]:
+            print('Z:', Zall[aa], af['Z%d'%(aa)])
+            flag = False
+
+    if MB.SFH_FORM==-99:
+        # Age:
+        for aa in range(len(MB.age)):
+            if round(MB.age[aa],nround) != round(af['age%d'%(aa)],nround):
+                print('age:', MB.age[aa], af['age%d'%(aa)])
+                flag = False
+        # Tau (e.g. ssp/csp):
+        for aa in range(len(MB.tau0)):
+            if round(MB.tau0[aa]) != round(af['tau0%d'%(aa)]):
+                print('tau0:', MB.tau0[aa], af['tau0%d'%(aa)])
+                flag = False
+    else:
+        # Age:
+        for aa in range(len(MB.ageparam)):
+            if round(MB.ageparam[aa]) != round(af['age%d'%(aa)]):
+                print('age:', MB.ageparam[aa], af['age%d'%(aa)])
+                flag = False
+        for aa in range(len(MB.tau)):
+            if round(MB.tau[aa]) != round(af['tau%d'%(aa)]):
+                print('tau:', MB.tau[aa], af['tau%d'%(aa)])
+                flag = False
+
+    # IMF:
+    if MB.nimf != af['nimf']:
+        print('nimf:', MB.nimf, af['nimf'])
+        flag = False
+
+    return flag
+
+
+def get_LSF(inputs, DIR_EXTR, ID, lm, c=3e18):
+    '''
+    Gets Morphology params, and returns LSF
+    '''
+    Amp = 0
+    f_morp = False
+    if inputs['MORP'] == 'moffat' or inputs['MORP'] == 'gauss':
+        f_morp = True
+        try:
+            mor_file = inputs['MORP_FILE'].replace('$ID','%s'%(ID))
+            fm = ascii.read(DIR_EXTR + mor_file)
+            Amp = fm['A']
+            gamma = fm['gamma']
+            if inputs['MORP'] == 'moffat':
+                alp = fm['alp']
+            else:
+                alp = 0
+        except Exception:
+            print('Error in reading morphology params.')
+            print('No morphology convolution.')
+            pass
+    else:
+        print('MORP Keywords does not match.')
+        print('No morphology convolution.')
+
+    ############################
+    # Template convolution;
+    ############################
+    try:
+        sig_temp = float(inputs['SIG_TEMP'])
+        print('Template is set to %.1f km/s.'%(sig_temp))
+    except:
+        sig_temp = 50.
+        print('Template resolution is unknown.')
+        print('Set to %.1f km/s.'%(sig_temp))
+
+    iixlam = np.argmin(np.abs(lm-4000)) # Around 4000 AA
+    dellam = lm[iixlam+1] - lm[iixlam] # AA/pix
+    R_temp = c / (sig_temp*1e3*1e10)
+    sig_temp_pix = np.median(lm) / R_temp / dellam # delta v in pixel;
+    sig_inst = 0 #65 #km/s for Manga
+
+    # If grism;
+    if f_morp:
+        print('\nStarting templates convolution (intrinsic morphology).')
+        if gamma>sig_temp_pix:# and False:
+            sig_conv = np.sqrt(gamma**2-sig_temp_pix**2)
+        else:
+            sig_conv = 0
+            print('Template resolution is broader than Morphology.')
+            print('No convolution is applied to templates.')
+
+        xMof = np.arange(-5, 5.1, .1) # dimension must be even.
+        if inputs['MORP'] == 'moffat' and Amp>0 and alp>0:
+            LSF = moffat(xMof, Amp, 0, np.sqrt(gamma**2-sig_temp_pix**2), alp)
+            print('Template convolution with Moffat.')
+        elif inputs['MORP'] == 'gauss':
+            sigma = gamma
+            LSF = gauss(xMof, Amp, np.sqrt(sigma**2-sig_temp_pix**2))
+            print('Template convolution with Gaussian.')
+            print('params is sigma;',sigma)
+        else:
+            print('Something is wrong with the convolution file. Exiting.')
+            return False
+
+    else: # For slit spectroscopy. To be updated...
+        print('Templates convolution (intrinsic velocity).')
+        try:
+            vdisp = float(inputs['VDISP'])
+            dellam = lm[1] - lm[0] # AA/pix
+            R_disp = c/(np.sqrt(vdisp**2-sig_inst**2)*1e3*1e10)
+            vdisp_pix = np.median(lm) / R_disp / dellam # delta v in pixel;
+            print('Templates are convolved at %.2f km/s.'%(vdisp))
+            if vdisp_pix-sig_temp_pix>0:
+                sig_conv = np.sqrt(vdisp_pix**2-sig_temp_pix**2)
+            else:
+                sig_conv = 0
+        except:
+            vdisp = 0.
+            print('Templates are not convolved.')
+            sig_conv = 0 #np.sqrt(sig_temp_pix**2)
+            pass
+        xMof = np.arange(-5, 5.1, .1) # dimension must be even.
+        Amp = 1.
+        LSF = gauss(xMof, Amp, sig_conv)
+
+    return LSF, lm
+
+
+def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, 
+    tau_lim=0.001, tmp_norm=1e10, nthin=1, delwave=0, lammax=300000, f_IGM=True):
+    '''
+    Make SPECTRA at given z and filter set.
+    Also, after v1.8, through this function library and data are register to the main class object, MB.
+    
+    Parameters
+    ----------
+    inputs : str
+        Configuration file.
+    zbest : float
+        Best redshift at this iteration. Templates are generated based on this reshift.
+    Z : array
+        Stellar phase metallicity in logZsun.
+    age : array
+        Age, in Gyr.
+    fneb : int
+        flag for adding nebular emissionself.
+    tmp_norm : float
+        Normalization of the stored templated. i.e. each template is in units of tmp_norm [Lsun].
+    '''
+    inputs = MB.inputs
+    age = MB.age
+    nage = MB.nage
+    Z = MB.Zall
+    fneb = MB.fneb
+    DIR_TMP = MB.DIR_TMP
+    zbest = MB.zgal
+    tau0 = MB.tau0
+
+    delz = 1.0
+    wid_z = MB.zmcmax - MB.zmcmin
+    nz = int(wid_z/delz)
+    if nz%2 == 0:
+        nz += 1
+    MB.zbests = np.linspace(MB.zgal - wid_z/2., MB.zgal + wid_z/2., nz)
+    MB.zbests = np.arange(MB.zgal, MB.zgal + 0.01, delz)
+
+    try:
+        af = MB.af0
+    except:
+        af = asdf.open(os.path.join(DIR_TMP, 'spec_all.asdf'))
+        MB.af0 = af
+
+    mshdu = af['ML']
+    spechdu = af['spec']
+
+    # Consistency check:
+    flag = check_library(MB, af)
+    if not flag:
+        print('\n!!!\nThere is inconsistency in z0 library and input file. Exiting.\n!!!\n')
+        sys.exit()
+
+    # ASDF Big tree;
+    # Create header;
+    tree = {
+        'isochrone': af['isochrone'],
+        'library': af['library'],
+        'nimf': af['nimf'],
+        'version_gsf': af['version_gsf']
+    }
+    tree_spec = {}
+    tree_spec_full = {}
+    tree_ML = {}
+    tree_SFR = {}
+
+    try:
+        DIR_EXTR = MB.DIR_EXTR
+        if len(DIR_EXTR)==0:
+            DIR_EXTR = False
+    except:
+        DIR_EXTR = False
+    DIR_FILT = MB.DIR_FILT
+    try:
+        CAT_BB_IND = inputs['CAT_BB_IND']
+    except:
+        CAT_BB_IND = False
+    try:
+        CAT_BB = inputs['CAT_BB']
+    except:
+        CAT_BB = False
+
+    try:
+        SFILT = MB.filts
+        FWFILT = fil_fwhm(SFILT, DIR_FILT)
+        print('')
+        print('Filters in the input catalog are;')
+        print(','.join(MB.filts))
+        print('')
+    except:
+        print('########################')
+        print('Filter is not detected!!')
+        print('Make sure your \nfilter directory is correct.')
+        print('########################')
+        sys.exit()
+
+    try:
+        SKIPFILT = inputs['SKIPFILT']
+        SKIPFILT = [x.strip() for x in SKIPFILT.split(',')]
+    except:
+        SKIPFILT = []
+
+    # If FIR data;
+    if MB.f_dust:
+        DFILT = inputs['FIR_FILTER'] # filter band string.
+        DFILT = [x.strip() for x in DFILT.split(',')]
+        DFWFILT = fil_fwhm(DFILT, DIR_FILT)
+        CAT_BB_DUST = inputs['CAT_BB_DUST']
+        DT0 = float(inputs['TDUST_LOW'])
+        DT1 = float(inputs['TDUST_HIG'])
+        dDT = float(inputs['TDUST_DEL'])
+        print('FIR is implemented.\n')
+    else:
+        print('No FIR is implemented.\n')
+
+    print('############################')
+    print('Making templates at z=%.4f'%(zbest))
+    print('############################')
+    ####################################################
+    # Get extracted spectra.
+    ####################################################
+    #
+    # Get ascii data.
+    #
+    MB.f_spec = False
+    try:
+        spec_files = inputs['SPEC_FILE']
+        spec_files = [x.strip() for x in spec_files.split(',')]
+        ninp0 = np.zeros(len(spec_files), dtype='int')
+        for ff, spec_file in enumerate(spec_files):
+            try:
+                fd0 = np.loadtxt(DIR_EXTR + spec_file, comments='#')
+                lm0tmp = fd0[:,0]
+                fobs0 = fd0[:,1]
+                eobs0 = fd0[:,2]
+                ninp0[ff] = len(lm0tmp)
+            except Exception:
+                print('File, %s/%s, cannot be open.'%(DIR_EXTR,spec_file))
+                pass
+        # Constructing arrays.
+        lm = np.zeros(np.sum(ninp0[:]),dtype=float)
+        fobs = np.zeros(np.sum(ninp0[:]),dtype=float)
+        eobs = np.zeros(np.sum(ninp0[:]),dtype=float)
+        fgrs = np.zeros(np.sum(ninp0[:]),dtype='int')  # FLAG for each grism.
+        for ff, spec_file in enumerate(spec_files):
+            try:
+                fd0 = np.loadtxt(DIR_EXTR + spec_file, comments='#')
+                lm0tmp= fd0[:,0]
+                fobs0 = fd0[:,1]
+                eobs0 = fd0[:,2]
+                for ii1 in range(ninp0[ff]):
+                    if ff==0:
+                        ii = ii1
+                    else:
+                        ii = ii1 + np.sum(ninp0[:ff])
+                    fgrs[ii] = ff
+                    lm[ii]   = lm0tmp[ii1]
+                    fobs[ii] = fobs0[ii1]
+                    eobs[ii] = eobs0[ii1]
+                MB.f_spec = True
+            except Exception:
+                pass
+    except:
+        print('No spec file is provided.')
+        pass
+
+    #############################
+    # READ BB photometry from CAT_BB:
+    #############################
+    if CAT_BB:
+        key_id = 'id'
+        fd0 = ascii.read(CAT_BB)
+        try:
+            id0 = fd0[key_id].astype('str')
+        except:
+            key_id = 'ID'
+            id0 = fd0[key_id].astype('str')
+
+        ii0 = np.where(id0[:]==MB.ID)
+        try:
+            if len(ii0[0]) == 0:
+                print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(MB.ID))
+                return False
+            id = fd0[key_id][ii0]
+        except:
+            print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(MB.ID))
+            print(fd0)
+            return False
+
+        fbb = np.zeros(len(SFILT), dtype=float)
+        ebb = np.zeros(len(SFILT), dtype=float)
+
+        for ii in range(len(SFILT)):
+            try:
+                fbb[ii] = fd0['F%s'%(SFILT[ii])][ii0]
+                ebb[ii] = fd0['E%s'%(SFILT[ii])][ii0]
+            except:
+                print('Could not find flux inputs for filter %s in the input BB catalog! Exiting.'%(SFILT[ii]))
+                return False
+
+    elif CAT_BB_IND: # if individual photometric catalog; made in get_sdss.py
+        unit = 'nu'
+        fd0 = fits.open(DIR_EXTR + CAT_BB_IND)
+        hd0 = fd0[1].header
+        bunit_bb = float(hd0['bunit'][:5])
+        lmbb0 = fd0[1].data['wavelength']
+        fbb0 = fd0[1].data['flux'] * bunit_bb
+        ebb0 = 1/np.sqrt(fd0[1].data['inverse_variance']) * bunit_bb
+
+        try:
+            unit = inputs['UNIT_SPEC']
+        except:
+            print('No param for UNIT_SPEC is found.')
+            print('BB flux unit is assumed to F%s.'%unit)
+            pass
+
+        if unit == 'lambda':
+            print('#########################')
+            print('Changed BB from Flam to Fnu')
+            snbb0 = fbb0/ebb0
+            fbb = flamtonu(lmbb0, fbb0, m0set=MB.m0set)
+            ebb = fbb/snbb0
+        else:
+            snbb0 = fbb0/ebb0
+            fbb = fbb0
+            ebb = ebb0
+
+    else:
+        fbb = np.zeros(len(SFILT), dtype=float)
+        ebb = np.zeros(len(SFILT), dtype=float)
+        for ii in range(len(SFILT)):
+            fbb[ii] = 0
+            ebb[ii] = -99 #1000
+
+    # Dust flux;
+    if MB.f_dust:
+        key_id = 'id'
+        fdd = ascii.read(CAT_BB_DUST)
+        try:
+            try:
+                id0 = fdd[key_id].astype('str')
+            except:
+                key_id = 'ID'
+                id0 = fdd[key_id].astype('str')
+
+            ii0 = np.where(id0[:]==MB.ID)
+            try:
+                id = fd0[key_id][ii0]
+            except:
+                print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(MB.ID))
+                return False
+        except:
+            return False
+            
+        id = fdd[key_id]
+
+        fbb_d = np.zeros(len(DFILT), dtype=float)
+        ebb_d = np.zeros(len(DFILT), dtype=float)
+        for ii in range(len(DFILT)):
+            fbb_d[ii] = fdd['F%s'%(DFILT[ii])][ii0]
+            ebb_d[ii] = fdd['E%s'%(DFILT[ii])][ii0]
+
+    #################
+    # Get morphology;
+    #################
+    if MB.f_spec:
+        LSF, lm = get_LSF(inputs, DIR_EXTR, MB.ID, lm)
+    else:
+        lm = []
+
+    if MB.SFH_FORM == -99:
+        ####################################
+        # Start generating templates
+        ####################################
+        for zz in range(len(Z)):
+            for pp in range(len(tau0)):
+                Zbest = Z[zz]
+                Na = len(age)
+                Ntmp = 1
+
+                for nzz, zbest in enumerate(MB.zbests):
+
+                    age_univ= MB.cosmo.age(zbest).value
+
+                    if zz == 0 and pp == 0:
+                        if delwave>0:
+                            lm0_orig = spechdu['wavelength'][::nthin]
+                            lm0 = np.arange(lm0_orig.min(), lm0_orig.max(), delwave)
+                        else:
+                            lm0 = spechdu['wavelength'][::nthin]
+                        if not lammax == None and not MB.f_dust:
+                            lm0 = lm0[(lm0 * (zbest+1) < lammax)]
+
+                    lmbest = np.zeros((Ntmp, len(lm0)), dtype=float)
+                    fbest = np.zeros((Ntmp, len(lm0)), dtype=float)
+                    lmbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
+                    fbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
+
+                    spec_mul = np.zeros((Na, len(lm0)), dtype=float)
+                    spec_mul_nu = np.zeros((Na, len(lm0)), dtype=float)
+                    spec_mul_nu_conv = np.zeros((Na, len(lm0)), dtype=float)
+
+                    ftmpbb = np.zeros((Na, len(SFILT)), dtype=float)
+                    ltmpbb = np.zeros((Na, len(SFILT)), dtype=float)
+
+                    ftmp_nu_int = np.zeros((Na, len(lm)), dtype=float)
+                    spec_av_tmp = np.zeros((Na, len(lm)), dtype=float)
+
+                    ms = np.zeros(Na, dtype=float)
+                    Ls = np.zeros(Na, dtype=float)
+                    tau = np.zeros(Na, dtype=float)
+                    sfr = np.zeros(Na, dtype=float)
+                    ms[:] = mshdu['ms_'+str(zz)][:] # [:] is necessary.
+                    Ls[:] = mshdu['Ls_'+str(zz)][:]
+                    Fuv = np.zeros(Na, dtype=float)
+
+                    for ss in range(Na):
+                        wave = lm0
+                        if delwave>0:
+                            fint = interpolate.interp1d(lm0_orig, spechdu['fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin], kind='nearest', fill_value="extrapolate")
+                            spec_mul[ss] = fint(lm0)
+                        else:
+                            spec_mul[ss] = spechdu['fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin] # Lsun/A
+
+                        ###################
+                        # IGM attenuation.
+                        ###################
+                        if f_IGM:
+                            # spec_av_tmp = madau_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
+                            spec_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
+                            MB.x_HI = x_HI
+                            spec_mul[ss,:] = spec_av_tmp
+
+                        # Distance;
+                        DL = MB.cosmo.luminosity_distance(zbest).value * MB.Mpc_cm # Luminositydistance in cm
+                        wavetmp = wave*(1.+zbest)
+
+                        # Flam to Fnu
+                        spec_mul_nu[ss,:] = flamtonu(wave, spec_mul[ss,:], m0set=MB.m0set)
+                        spec_mul_nu[ss,:] *= MB.Lsun / (4.*np.pi*DL**2/(1.+zbest))
+                        # (1.+zbest) takes acount of the change in delta lam by redshifting.
+                        # Note that this is valid only when F_nu.
+                        # When Flambda, /(1.+zbest) will be *(1.+zbest).
+
+                        spec_mul_nu[ss,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
+                        ms[ss] *= (1./Ls[ss])*tmp_norm # M/L; 1 unit template has this mass/solar.
+                        try:
+                            tautmp = af['ML']['realtau_%d'%int(zz)]
+                            sfr[ss] = ms[ss] / (tautmp[ss]*1e9) # SFR per unit template, in units of Msolar/yr.
+                        except:
+                            if zz == 0 and ss == 0:
+                                print('realtau entry not found. SFR is set to 0. (Or rerun maketmp_z0.py)')
+                            sfr[ss] = 0
+
+                        if MB.f_spec:
+                            ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu[ss,:])
+
+                        ltmpbb[ss,:], ftmpbb[ss,:] = filconv(SFILT, wavetmp, spec_mul_nu[ss,:], DIR_FILT, MB=MB, f_regist=False)
+
+                        # Convolution has to come after this?
+                        if len(lm)>0:
+                            try:
+                                spec_mul_nu_conv[ss,:] = convolve(spec_mul_nu[ss], LSF, boundary='extend')
+                            except:
+                                #print('Error. No convolution is happening...')
+                                spec_mul_nu_conv[ss,:] = spec_mul_nu[ss]
+                                if zz==0 and ss==0:
+                                    print('Kernel is too small. No convolution.')
+                        else:
+                            spec_mul_nu_conv[ss,:] = spec_mul_nu[ss]
+
+                        ##########################################
+                        # Writing out the templates to fits table.
+                        ##########################################
+                        if ss == 0 and pp == 0 and zz == 0:
+                            # First file
+                            nd1 = np.arange(0,len(lm),1)
+                            nd3 = np.arange(10000,10000+len(ltmpbb[ss,:]),1)
+                            nd_ap = np.append(nd1,nd3)
+                            lm_ap = np.append(lm, ltmpbb[ss,:])
+
+                            # ASDF
+                            tree_spec.update({'wavelength':lm_ap})
+                            tree_spec.update({'colnum':nd_ap})
+
+                            # Second file
+                            # ASDF
+                            nd = np.arange(0,len(wavetmp),1)
+                            if zbest == MB.zgal:
+                                tree_spec_full.update({'wavelength':wavetmp})
+                                tree_spec_full.update({'colnum':nd})
+
+                        # ASDF
+                        spec_ap = np.append(ftmp_nu_int[ss,:], ftmpbb[ss,:])
+                        tree_spec.update({'fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp): spec_ap})
+                        if zbest == MB.zgal:
+                            tree_spec_full.update({'fspec_orig_'+str(zz)+'_'+str(ss)+'_'+str(pp): spec_mul_nu[ss,:]})
+
+                        # For nebular library;
+                        # For every Z, but not for ss and pp.
+                        if fneb == 1 and MB.f_bpass==0 and ss==0 and pp==0:
+                            if zz==0:
+                                spec_mul_neb = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                                spec_mul_neb_nu = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                                spec_mul_neb_nu_conv = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                                ftmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
+                                ltmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
+                                ftmp_neb_nu_int = np.zeros((len(Z), len(MB.logUs), len(lm)), dtype=float)
+
+                            for uu in range(len(MB.logUs)):
+                                if delwave>0:
+                                    fint = interpolate.interp1d(lm0_orig, spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin], kind='nearest', fill_value="extrapolate")
+                                    spec_mul_neb[zz,uu,:] = fint(lm0)
+                                else:
+                                    spec_mul_neb[zz,uu,:] = spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin]
+                                
+                                con_neb = (spec_mul_neb[zz,uu,:]<0)
+                                spec_mul_neb[zz,uu,:][con_neb] = 0
+                                
+                                if f_IGM:
+                                    # spec_neb_av_tmp = madau_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
+                                    spec_neb_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
+                                    # spec_neb_av_tmp = masongronke_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
+                                    spec_mul_neb[zz,uu,:] = spec_neb_av_tmp
+
+                                spec_mul_neb_nu[zz,uu,:] = flamtonu(wave, spec_mul_neb[zz,uu,:], m0set=MB.m0set)
+                                
+                                spec_mul_neb_nu[zz,uu,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
+                                
+                                spec_mul_neb_nu[zz,uu,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
+                                ltmpbb_neb[zz,uu,:], ftmpbb_neb[zz,uu,:] = filconv(SFILT, wavetmp, spec_mul_neb_nu[zz,uu,:], DIR_FILT, MB=MB, f_regist=False)
+
+                                if MB.f_spec:
+                                    ftmp_neb_nu_int[zz,uu,:] = data_int(lm, wavetmp, spec_mul_neb_nu[zz,uu,:])
+
+                                if len(lm)>0:
+                                    try:
+                                        spec_mul_neb_nu_conv[zz,uu,:] = convolve(spec_mul_neb_nu[zz,uu,:], LSF, boundary='extend')
+                                    except:
+                                        spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
+                                else:
+                                    spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
+
+                                if zbest == MB.zgal:
+                                    tree_spec_full.update({'fspec_orig_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu[zz,uu,:]})
+                                    tree_spec_full.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu_conv[zz,uu,:]})
+
+                                spec_neb_ap = np.append(ftmp_neb_nu_int[zz,uu,:], ftmpbb_neb[zz,uu,:])
+                                tree_spec.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_neb_ap})
+
+
+                    #########################
+                    # Summarize the ML
+                    #########################
+                    if pp == 0:
+                        # ML
+                        tree_ML.update({'ML_'+str(zz): ms})
+                        # SFR
+                        tree_SFR.update({'SFR_'+str(zz): sfr})
+
+    else:
+        print('Tau model comes here;')
+
+
+
+    #########################
+    # Summarize the templates
+    #########################
+    tree['id'] = MB.ID
+    tree['z'] = zbest
+    try:
+        tree['x_HI'] = x_HI
+    except:
+        pass
+
+    tree.update({'spec' : tree_spec})
+    tree.update({'spec_full' : tree_spec_full})
+    tree.update({'ML' : tree_ML})
+    tree.update({'SFR' : tree_SFR})
+
+    ######################
+    # Add dust component;
+    ######################
+    if MB.f_dust:
+        tree_spec_dust = {}
+        tree_spec_dust_full = {}
+
+        if DT0 == DT1:
+            Temp = [DT0]
+        else:
+            Temp = np.arange(DT0,DT1,dDT)
+
+        Mdust_temp = np.zeros(len(Temp),float)
+        dellam_d = 1e1
+        lambda_d = np.arange(1e3, 1e7, dellam_d)
+        
+        print('Reading dust table...')
+        for tt in range(len(Temp)):
+            if tt == 0:
+                # For full;
+                nd_d  = np.arange(0,len(lambda_d),1)
+
+                # ASDF
+                tree_spec_dust_full.update({'wavelength': lambda_d*(1.+zbest)})
+                tree_spec_dust_full.update({'colnum': nd_d})
+
+            f_drain = False #True #
+            if f_drain:
+                #numin, numax, nmodel = 8, 3, 9
+                numin, numax, nmodel = tt, MB.dust_numax, MB.dust_nmodel #3, 9
+                fnu_d = get_spectrum_draine(lambda_d, DL, zbest, numin, numax, nmodel, DIR_DUST=MB.DIR_DUST, m0set=MB.m0set)
+                # This should be in fnu w mzp=25.0
+                Mdust_temp[tt] = 1.0
+            else:
+                if tt == 0:
+                    from astropy.modeling import models
+                    from astropy import units as u
+                    print('Dust emission based on Modified Blackbody')
+                    '''
+                    # from Eq.3 of Bianchi 13
+                    kabs0 = 4.0 # in cm2/g
+                    beta_d = 2.08 #
+                    lam0 = 250.*1e4 # mu m to AA
+                    '''
+                    kb = 1.380649e-23 # Boltzmann constant, in J/K
+                    hp = 6.62607015e-34 # Planck constant, in J*s
+                    wav = lambda_d * u.AA
+                    nures = c / wav.value * 1e-9 # GHz
+
+                    Tcmb = 2.726 * (1+zbest)
+                    beta = 1.8
+                    kappa = 0.0484 * (nures/345.)**beta # m^2 / kg
+                    kappa *= (100)**2 / (1e3) # cm2/g
+                    nurest = c / wav.value # Hz
+
+                BT_nu = (2.0 * hp * (nurest)**3. * c**(-2.) / (np.exp(hp*(nurest)/(kb * Temp[tt])) - 1.0))
+                BT_nu_cmb = (2.0 * hp * (nurest)**3. * c**(-2.) / (np.exp(hp*(nurest)/(kb * Temp[tt])) - 1.0))
+                # J s * (Hz)3 * (AA/s)^-2 = 1e+7 erg * (AA)-2 /s
+                denom = BT_nu-BT_nu_cmb
+
+                fnu_d = (1+zbest) / (DL*MB.Mpc_cm)**2 * (kappa * denom)
+                # 1/cm2 * cm2/g * (1e+7 erg * (AA)-2 / s) = 1e7 erg /s / g / AA / AA
+                fnu_d *= 1e7 * 1e16 # erg /s / g / cm2
+                fnu_d *= 1.989e+33 # erg /s / Msun / cm2
+
+                # Into magzp;
+                fnu_d = fnutonu(fnu_d, m0set=MB.m0set, m0input=-48.6)
+                Mdust_temp[tt] = 1. # Msun/temp, like Mass to light ratio.
+
+            # ASDF
+            tree_spec_dust_full.update({'fspec_'+str(tt): fnu_d})
+
+            # Convolution;
+            ALLFILT = np.append(SFILT,DFILT)
+            ltmpbb_d, ftmpbb_d = filconv(ALLFILT,lambda_d*(1.+zbest),fnu_d,DIR_FILT)
+            nd_db = np.arange(0, len(ftmpbb_d), 1)
+
+            if MB.f_spec:
+                ftmp_nu_int_d = data_int(lm, lambda_d*(1.+zbest), fnu_d)
+                ltmpbb_d = np.append(lm, ltmpbb_d)
+                ftmpbb_d = np.append(ftmp_nu_int_d, ftmpbb_d)
+                nd_db = np.arange(0, len(ftmpbb_d), 1)
+
+            if tt == 0:
+                # For conv;
+                # ASDF
+                tree_spec_dust.update({'wavelength': ltmpbb_d})
+                tree_spec_dust.update({'colnum': nd_db})
+
+            tree_spec_dust.update({'fspec_'+str(tt): ftmpbb_d})
+        # Md/temp;
+        tree_spec_dust.update({'Mdust': Mdust_temp})
+        tree.update({'spec_dust' : tree_spec_dust})
+        tree.update({'spec_dust_full' : tree_spec_dust_full})
+        print('dust updated.')
+
+    # Save;
+    af = asdf.AsdfFile(tree)
+    af.write_to(DIR_TMP + 'spec_all_' + MB.ID + '.asdf', all_array_compression='zlib')
+    # Loading the redshifted template;
+    MB.af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
+
+    ##########################################
+    # For observation.
+    # Write out for the Multi-component fitting.
+    ##########################################
+    if True:
+        MB.data = {}
+        file_tmp = 'tmp_library_%s.txt'%MB.ID
+        file_tmp2 = 'tmp_library2%s.txt'%MB.ID
+        fw = open(file_tmp,'w')#
+        fw.write('# BB data (>%d) in this file are not used in fitting.\n'%(ncolbb))
+        for ii in range(len(lm)):
+            g_offset = 1000 * fgrs[ii]
+            if lm[ii]/(1.+zbest) > lamliml and lm[ii]/(1.+zbest) < lamlimu:
+                fw.write('%d %.5f %.5e %.5e\n'%(ii+g_offset, lm[ii], fobs[ii], eobs[ii]))
+            else:
+                fw.write('%d %.5f 0 1000\n'%(ii+g_offset, lm[ii]))
+
+        for ii in range(len(ltmpbb[0,:])):
+            if SFILT[ii] in SKIPFILT:# data point to be skiped;
+                fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii]))
+            elif  ebb[ii]>ebblim:
+                fw.write('%d %.5f 0 1000\n'%(ii+ncolbb, ltmpbb[0,ii]))
+            else:
+                fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii]))
+        fw.close()
+
+        # register;
+        dat = ascii.read(file_tmp, format='no_header')
+        NR = dat['col1']
+        x = dat['col2']
+        fy00 = dat['col3']
+        ey00 = dat['col4']
+        dict_spec_obs = {'NR':NR, 'x':x, 'fy':fy00, 'ey':ey00}
+        MB.data['spec_obs'] = dict_spec_obs
+
+        fw = open(file_tmp,'w')
+        if MB.f_dust:
+            nbblast = len(ltmpbb[0,:])+len(lm)
+            for ii in range(len(ebb_d[:])):
+                if ebb_d[ii]>ebblim:
+                    fw.write('%d %.5f 0 1000\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast]))
+                else:
+                    fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii]))
+        fw.close()
+        if MB.f_dust:
+            dat = ascii.read(file_tmp, format='no_header')
+            nr_d = dat['col1']
+            x_d = dat['col2']
+            fy_d = dat['col3']
+            ey_d = dat['col4']
+            dict_spec_fir_obs = {'NR':nr_d, 'x':x_d, 'fy':fy_d, 'ey':ey_d}
+            MB.data['spec_fir_obs'] = dict_spec_fir_obs
+
+        # BB phot
+        fw = open(file_tmp,'w')
+        fw_rem = open(file_tmp2, 'w')
+        for ii in range(len(ltmpbb[0,:])):
+            if SFILT[ii] in SKIPFILT:# data point to be skiped;
+                fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii], FWFILT[ii]/2., SFILT[ii]))
+                fw_rem.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
+            elif ebb[ii]>ebblim:
+                fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
+            elif ebb[ii]<=0:
+                fw.write('%d %.5f 0 -99 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
+            else:
+                fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
+        fw.close()
+        fw_rem.close()
+        # register;
+        dat = ascii.read(file_tmp, format='no_header')
+        NRbb = dat['col1']
+        xbb  = dat['col2']
+        fybb = dat['col3']
+        eybb = dat['col4']
+        exbb = dat['col5']
+        dict_bb_obs = {'NR':NRbb, 'x':xbb, 'fy':fybb, 'ey':eybb, 'ex':exbb}
+        MB.data['bb_obs'] = dict_bb_obs
+        try:
+            dat = ascii.read(file_tmp2, format='no_header')
+            NR_ex = dat['col1']
+            x_ex = dat['col2']
+            fy_ex = dat['col3']
+            ey_ex = dat['col4']
+            ex_ex = dat['col5']
+            dict_bb_obs_removed = {'NR':NR_ex, 'x':x_ex, 'fy':fy_ex, 'ey':ey_ex, 'ex':ex_ex}
+            MB.data['bb_obs_removed'] = dict_bb_obs_removed
+        except:
+            pass
+
+        # Dust; Not sure where this is being used...
+        fw = open(file_tmp,'w')
+        if MB.f_dust:
+            for ii in range(len(ebb_d[:])):
+                if ebb_d[ii]>ebblim:
+                    fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], DFWFILT[ii]/2., DFILT[ii]))
+                else:
+                    fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii], DFWFILT[ii]/2., DFILT[ii]))
+        fw.close()
+        if MB.f_dust:
+            dat = ascii.read(file_tmp, format='no_header')
+            nr_bb_d = dat['col1']
+            x_bb_d = dat['col2']
+            fy_bb_d = dat['col3']
+            ey_bb_d = dat['col4']
+            dict_bb_fir_obs = {'NR':nr_bb_d, 'x':x_bb_d, 'fy':fy_bb_d, 'ey':ey_bb_d}
+            MB.data['bb_fir_obs'] = dict_bb_fir_obs
+
+        print('Done making templates at z=%.2f.\n'%zbest)
+        os.system('rm %s %s'%(file_tmp, file_tmp2))
+
+    return True
+
+
+def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_lim=0.001,
+    f_IGM=True, nthin=1, tmp_norm=1e10, delwave=0, lammax=300000):
+    '''
+    Make SPECTRA at given z and filter set.
+    
+    Parameters
+    ----------
+    inputs : str
+        Configuration file.
+    zbest :float
+        Best redshift at this iteration. Templates are generated based on this reshift.
+    Z : array
+        Stellar phase metallicity in logZsun.
+    age : array
+        Age, in Gyr.
+    fneb : int
+        flag for adding nebular emissionself.
+    f_IGM : bool
+        IGM attenuation. Madau.
+    nthin : int
+        Thinning templates.
+    lammax : float
+        Maximum wavelength in RF.
+    '''    
+
+    inputs = MB.inputs
+    ID = MB.ID
+    age = MB.ageparam
+    nage = MB.nage
+    tau = MB.tau
+    Z = MB.Zall
+    fneb = MB.fneb
+    DIR_TMP = MB.DIR_TMP
+    zbest = MB.zgal
+
+    af = asdf.open(DIR_TMP + 'spec_all.asdf')
+    mshdu = af['ML']
+    spechdu = af['spec']
+
+    # Consistency check:
+    flag = check_library(MB, af)
+    if not flag:
+        print('\n!!!\nThere is inconsistency in z0 library and input file. Exiting.\n!!!\n')
+        sys.exit()
+
+    # ASDF Big tree;
+    # Create header;
+    tree = {
+        'isochrone': af['isochrone'],
+        'library': af['library'],
+        'nimf': af['nimf'],
+        'version_gsf': af['version_gsf']
+    }
+    tree_spec = {}
+    tree_spec_full = {}
+    tree_ML = {}
+
+    try:
+        DIR_EXTR = MB.DIR_EXTR #inputs['DIR_EXTR']
+        if len(DIR_EXTR)==0:
+            DIR_EXTR = False
+    except:
+        DIR_EXTR = False
+    DIR_FILT = MB.DIR_FILT #inputs['DIR_FILT']
+    try:
+        CAT_BB_IND = inputs['CAT_BB_IND']
+    except:
+        CAT_BB_IND = False
+    try:
+        CAT_BB = MB.CAT_BB #inputs['CAT_BB']
+    except:
+        CAT_BB = False
+
+    try:
+        SFILT = MB.filts #inputs['FILTER'] # filter band string.
+        FWFILT = fil_fwhm(SFILT, DIR_FILT)
+    except:
+        print('########################')
+        print('Filter is not detected!!')
+        print('Make sure your \nfilter directory is correct.')
+        print('########################')
+        sys.exit()
+    try:
+        SKIPFILT = inputs['SKIPFILT']
+        SKIPFILT = [x.strip() for x in SKIPFILT.split(',')]
+    except:
+        SKIPFILT = []
+
+    # If FIR data;
+    if MB.f_dust:
+        DFILT = inputs['FIR_FILTER'] # filter band string.
+        DFILT = [x.strip() for x in DFILT.split(',')]
+        DFWFILT = fil_fwhm(DFILT, DIR_FILT)
+        CAT_BB_DUST = inputs['CAT_BB_DUST']
+        DT0 = float(inputs['TDUST_LOW'])
+        DT1 = float(inputs['TDUST_HIG'])
+        dDT = float(inputs['TDUST_DEL'])
+        print('FIR is implemented.\n')
+    else:
+        print('No FIR is implemented.\n')
+
+
+    print('############################')
+    print('Making templates at z=%.4f'%(zbest))
+    print('############################')
+    ####################################################
+    # Get extracted spectra.
+    ####################################################
+    MB.f_spec = False
+    try:
+        spec_files = inputs['SPEC_FILE'] #.replace('$ID','%s'%(ID))
+        spec_files = [x.strip() for x in spec_files.split(',')]
+        ninp0 = np.zeros(len(spec_files), dtype='int')
+        for ff, spec_file in enumerate(spec_files):
+            try:
+                fd0 = np.loadtxt(DIR_EXTR + spec_file, comments='#')
+                lm0tmp = fd0[:,0]
+                fobs0 = fd0[:,1]
+                eobs0 = fd0[:,2]
+                ninp0[ff] = len(lm0tmp)#[con_tmp])
+            except Exception:
+                print('File, %s/%s, cannot be open.'%(DIR_EXTR,spec_file))
+                pass
+        # Constructing arrays.
+        lm = np.zeros(np.sum(ninp0[:]),dtype=float)
+        fobs = np.zeros(np.sum(ninp0[:]),dtype=float)
+        eobs = np.zeros(np.sum(ninp0[:]),dtype=float)
+        fgrs = np.zeros(np.sum(ninp0[:]),dtype='int')  # FLAG for each grism.
+        for ff, spec_file in enumerate(spec_files):
+            try:
+                fd0 = np.loadtxt(DIR_EXTR + spec_file, comments='#')
+                lm0tmp = fd0[:,0]
+                fobs0 = fd0[:,1]
+                eobs0 = fd0[:,2]
+                for ii1 in range(ninp0[ff]):
+                    if ff==0:
+                        ii = ii1
+                    else:
+                        ii = ii1 + np.sum(ninp0[:ff])
+                    fgrs[ii] = ff
+                    lm[ii] = lm0tmp[ii1]
+                    fobs[ii] = fobs0[ii1]
+                    eobs[ii] = eobs0[ii1]
+                MB.f_spec = True
+            except Exception:
+                pass
+    except:
+        print('No spec file is provided.')
+        pass
+
+    if MB.f_spec:
+        nthin = 1
+
+    #############################
+    # READ BB photometry from CAT_BB:
+    #############################
+    if CAT_BB:
+        fd0 = ascii.read(CAT_BB)
+        id0 = fd0['id'].astype('str')
+        ii0 = np.where(id0[:]==ID)
+        try:
+            id = fd0['id'][ii0]
+        except:
+            print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(ID))
+            return False        
+        if len(ii0) == 0:
+            print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(ID))
+            return False
+
+        fbb = np.zeros(len(SFILT), dtype=float)
+        ebb = np.zeros(len(SFILT), dtype=float)
+
+        for ii in range(len(SFILT)):
+            fbb[ii] = fd0['F%s'%(SFILT[ii])][ii0]
+            ebb[ii] = fd0['E%s'%(SFILT[ii])][ii0]
+
+    elif CAT_BB_IND: # if individual photometric catalog; made in get_sdss.py
+        fd0 = fits.open(DIR_EXTR + CAT_BB_IND)
+        hd0 = fd0[1].header
+        bunit_bb = float(hd0['bunit'][:5])
+        lmbb0= fd0[1].data['wavelength']
+        fbb0 = fd0[1].data['flux'] * bunit_bb
+        ebb0 = 1/np.sqrt(fd0[1].data['inverse_variance']) * bunit_bb
+
+        unit  = 'nu'
+        try:
+            unit = inputs['UNIT_SPEC']
+        except:
+            print('No param for UNIT_SPEC is found.')
+            print('BB flux unit is assumed to Fnu.')
+            pass
+
+        if unit == 'lambda':
+            print('#########################')
+            print('Changed BB from Flam to Fnu')
+            snbb0 = fbb0/ebb0
+            fbb = flamtonu(lmbb0, fbb0, m0set=MB.m0set)
+            ebb = fbb/snbb0
+        else:
+            snbb0 = fbb0/ebb0
+            fbb = fbb0
+            ebb = ebb0
+
+    else:
+        fbb = np.zeros(len(SFILT), dtype=float)
+        ebb = np.zeros(len(SFILT), dtype=float)
+        for ii in range(len(SFILT)):
+            fbb[ii] = 0
+            ebb[ii] = -99
+
+    # Dust flux;
+    if MB.f_dust:
+        fdd = ascii.read(CAT_BB_DUST)
+        id0 = fdd['id'].astype('str')
+        ii0 = np.where(id0[:]==ID)
+        try:
+            id = fd0['id'][ii0]
+        except:
+            print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(ID))
+            return False
+
+        fbb_d = np.zeros(len(DFILT), dtype=float)
+        ebb_d = np.zeros(len(DFILT), dtype=float)
+        for ii in range(len(DFILT)):
+            fbb_d[ii] = fdd['F%s'%(DFILT[ii])][ii0]
+            ebb_d[ii] = fdd['E%s'%(DFILT[ii])][ii0]
+
+    #############################
+    # Getting Morphology params.
+    #############################
+    Amp = 0
+    f_morp = False
+    if MB.f_spec:
+        LSF, lm = get_LSF(inputs, DIR_EXTR, ID, lm)
+    else:
+        lm = []
+
+    ####################################
+    # Start generating templates
+    ####################################
+    for zz in range(len(Z)):
+        Zbest = Z[zz]
+        Na = len(age)
+        Ntmp = 1
+        age_univ= MB.cosmo.age(zbest).value #, use_flat=True, **cosmo)
+
+        for tt in range(len(tau)): # tau
+            if zz == 0 and tt == 0:
+                if delwave>0:
+                    lm0_orig = spechdu['wavelength'][::nthin]
+                    lm0 = np.arange(lm0_orig.min(), lm0_orig.max(), delwave)
+                else:
+                    lm0 = spechdu['wavelength'][::nthin]
+                if not lammax == None and not MB.f_dust:
+                    lm0 = lm0[(lm0 * (zbest+1) < lammax)]
+                wave = lm0
+
+            lmbest = np.zeros((Ntmp, len(lm0)), dtype=float)
+            fbest = np.zeros((Ntmp, len(lm0)), dtype=float)
+            lmbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
+            fbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
+
+            spec_mul = np.zeros((Na, len(lm0)), dtype=float)
+            spec_mul_nu = np.zeros((Na, len(lm0)), dtype=float)
+            spec_mul_nu_conv = np.zeros((Na, len(lm0)), dtype=float)
+
+            ftmpbb = np.zeros((Na, len(SFILT)), dtype=float)
+            ltmpbb = np.zeros((Na, len(SFILT)), dtype=float)
+
+            ftmp_nu_int = np.zeros((Na, len(lm)), dtype=float)
+            spec_av_tmp = np.zeros((Na, len(lm)), dtype=float)
+
+            ms = np.zeros(Na, dtype=float)
+            Ls = np.zeros(Na, dtype=float)
+            ms[:] = mshdu['ms_'+str(zz)+'_'+str(tt)][:] # [:] is necessary.
+            Ls[:] = mshdu['Ls_'+str(zz)+'_'+str(tt)][:]
+            Fuv = np.zeros(Na, dtype=float)
+
+            for ss in range(Na):
+                #print(ss,tt,zz)
+                if ss == 0 and tt == 0 and zz == 0:
+                    DL = MB.cosmo.luminosity_distance(zbest).value * MB.Mpc_cm # Luminositydistance in cm
+                    wavetmp = wave*(1.+zbest)
+
+                if delwave>0:
+                    fint = interpolate.interp1d(lm0_orig, spechdu['fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss)][::nthin], kind='nearest', fill_value="extrapolate")
+                    spec_mul[ss] = fint(lm0)
+                else:
+                    spec_mul[ss] = spechdu['fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss)][::nthin]
+
+                ##################
+                # IGM attenuation.
+                ##################
+                if f_IGM:
+                    # spec_av_tmp = madau_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
+                    spec_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
+                else:
+                    spec_av_tmp = spec_mul[ss,:]
+
+                spec_mul_nu[ss,:] = flamtonu(wave, spec_av_tmp, m0set=MB.m0set)
+
+                spec_mul_nu[ss,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
+                spec_mul_nu[ss,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
+                ms[ss] *= (1./Ls[ss])*tmp_norm # M/L; 1 unit template has this mass in [Msolar].
+
+                if len(lm)>0:
+                    try:
+                        spec_mul_nu_conv[ss,:] = convolve(spec_mul_nu[ss,:], LSF, boundary='extend')
+                    except:
+                        spec_mul_nu_conv[ss,:] = spec_mul_nu[ss,:]
+                        if zz==0 and ss==0:
+                            print('Kernel is too small. No convolution.')
+                else:
+                    spec_mul_nu_conv[ss,:] = spec_mul_nu[ss,:]
+
+                if MB.f_spec:
+                    #ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu_conv[ss,:])
+                    ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu[ss,:])
+                
+                # Register filter response;
+                ltmpbb[ss,:], ftmpbb[ss,:] = filconv(SFILT, wavetmp, spec_mul_nu[ss,:], DIR_FILT, MB=MB, f_regist=False)
+
+
+                ##########################################
+                # Writing out the templates to fits table.
+                ##########################################
+                if ss == 0 and tt == 0 and zz == 0:
+                    # First file
+                    nd1    = np.arange(0,len(lm),1)
+                    nd3    = np.arange(10000,10000+len(ltmpbb[ss,:]),1)
+                    nd_ap  = np.append(nd1,nd3)
+                    lm_ap  = np.append(lm, ltmpbb[ss,:])
+
+                    # ASDF
+                    tree_spec.update({'wavelength':lm_ap})
+                    tree_spec.update({'colnum':nd_ap})
+
+                    # Second file
+                    # ASDF
+                    nd = np.arange(0,len(wavetmp),1)
+                    tree_spec_full.update({'wavelength':wavetmp})
+                    tree_spec_full.update({'colnum':nd})
+
+                # ASDF
+                # ???
+                spec_ap = np.append(ftmp_nu_int[ss,:], ftmpbb[ss,:])
+                tree_spec.update({'fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss): spec_ap})
+                tree_spec_full.update({'fspec_orig_'+str(zz)+'_'+str(tt)+'_'+str(ss): spec_mul_nu[ss,:]})                
+                #tree_spec_full.update({'fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss): spec_mul_nu_conv[ss,:]})
+
+                # Nebular library;
+                if fneb == 1 and MB.f_bpass==0 and ss==0 and tt==0:
+                    if zz==0:
+                        spec_mul_neb = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                        spec_mul_neb_nu = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                        spec_mul_neb_nu_conv = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                        ftmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
+                        ltmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
+                        ftmp_neb_nu_int = np.zeros((len(Z), len(MB.logUs), len(lm)), dtype=float)
+
+                    for uu in range(len(MB.logUs)):
+                        if delwave>0:
+                            fint = interpolate.interp1d(lm0_orig, spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin], kind='nearest', fill_value="extrapolate")
+                            spec_mul_neb[zz,uu,:] = fint(lm0)
+                        else:
+                            spec_mul_neb[zz,uu,:] = spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin]
+                        
+                        con_neb = (spec_mul_neb[zz,uu,:]<0)
+                        spec_mul_neb[zz,uu,:][con_neb] = 0
+                        
+                        if f_IGM:
+                            # spec_neb_av_tmp = madau_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
+                            spec_neb_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
+                            spec_mul_neb[zz,uu,:] = spec_neb_av_tmp
+
+                        spec_mul_neb_nu[zz,uu,:] = flamtonu(wave, spec_mul_neb[zz,uu,:], m0set=MB.m0set)
+                        spec_mul_neb_nu[zz,uu,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
+                        spec_mul_neb_nu[zz,uu,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
+                        ltmpbb_neb[zz,uu,:], ftmpbb_neb[zz,uu,:] = filconv(SFILT, wavetmp, spec_mul_neb_nu[zz,uu,:], DIR_FILT, MB=MB, f_regist=False)
+
+                        if MB.f_spec:
+                            ftmp_neb_nu_int[zz,uu,:] = data_int(lm, wavetmp, spec_mul_neb_nu[zz,uu,:])
+
+                        if len(lm)>0:
+                            try:
+                                spec_mul_neb_nu_conv[zz,uu,:] = convolve(spec_mul_neb_nu[zz,uu,:], LSF, boundary='extend')
+                            except:
+                                spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
+                        else:
+                            spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
+
+                        tree_spec_full.update({'fspec_orig_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu[zz,uu,:]})
+                        tree_spec_full.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu_conv[zz,uu,:]})
+
+                        spec_neb_ap = np.append(ftmp_neb_nu_int[zz,uu,:], ftmpbb_neb[zz,uu,:])
+                        tree_spec.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_neb_ap})
+
+            #########################
+            # Summarize the ML
+            #########################
+            # ASDF
+            tree_ML.update({'ML_'+str(zz)+'_'+str(tt): ms})
+
+    #########################
+    # Summarize the templates
+    #########################
+    tree['id'] = ID
+    tree['z'] = zbest
+    try:
+        tree['x_HI'] = x_HI
+    except:
+        pass
+    tree.update({'spec' : tree_spec})
+    tree.update({'spec_full' : tree_spec_full})
+    tree.update({'ML' : tree_ML})
+
+    ######################
+    # Add dust component;
+    ######################
+    if MB.f_dust:
+        tree_spec_dust = {}
+        tree_spec_dust_full = {}
+
+        if DT0 == DT1:
+            Temp = [DT0]
+        else:
+            Temp = np.arange(DT0,DT1,dDT)
+
+        dellam_d = 1e3
+        lambda_d = np.arange(1e3, 1e7, dellam_d) # RF wavelength, in AA. #* (1.+zbest) # 1um to 1000um; This has to be wide enough, to cut dust contribution at <1um.
+
+        print('Reading dust table...')
+        for tt in range(len(Temp)):
+            if tt == 0:
+                # For full;
+                nd_d  = np.arange(0,len(lambda_d),1)
+
+                # ASDF
+                tree_spec_dust_full.update({'wavelength': lambda_d*(1.+zbest)})
+                tree_spec_dust_full.update({'colnum': nd_d})
+
+            #numin, numax, nmodel = 8, 3, 9
+            numin, numax, nmodel = tt, 3, 9
+            fnu_d = get_spectrum_draine(lambda_d, DL, zbest, numin, numax, nmodel, DIR_DUST=MB.DIR_DUST, m0set=MB.m0set)
+
+            if False:
+                for nn in range(0,11,1):
+                    try:
+                        fnu_d_tmp = get_spectrum_draine(lambda_d, DL, zbest, numin, numax, nn, DIR_DUST=MB.DIR_DUST, m0set=MB.m0set)
+                        plt.plot(lambda_d * (1+zbest), fnu_d_tmp, label='%d'%nn)
+                        plt.xlim(2000, 5000000)
+                        plt.xscale('log')
+                        plt.yscale('log')
+                    except:
+                        print('Errir in ',nn)
+                plt.legend()
+                plt.show()
+
+            # ASDF
+            tree_spec_dust_full.update({'fspec_'+str(tt): fnu_d})
+
+            # Convolution;
+            ALLFILT = np.append(SFILT,DFILT)
+            ltmpbb_d, ftmpbb_d = filconv(ALLFILT,lambda_d*(1.+zbest),fnu_d,DIR_FILT)
+
+            if MB.f_spec:
+                ftmp_nu_int_d = data_int(lm, lambda_d*(1.+zbest), fnu_d)
+                ltmpbb_d = np.append(lm, ltmpbb_d)
+                ftmpbb_d = np.append(ftmp_nu_int_d, ftmpbb_d)
+                nd_db = np.arange(0, len(ftmpbb_d), 1)
+
+            if tt == 0:
+                # For conv;
+                # ASDF
+                tree_spec_dust.update({'wavelength': ltmpbb_d})
+                tree_spec_dust.update({'colnum': nd_db})
+
+            tree_spec_dust.update({'fspec_'+str(tt): ftmpbb_d})
+
+        tree.update({'spec_dust' : tree_spec_dust})
+        tree.update({'spec_dust_full' : tree_spec_dust_full})
+        print('dust updated.')
+
+    # Save;
+    af = asdf.AsdfFile(tree)
+    af.write_to(DIR_TMP + 'spec_all_' + ID + '.asdf', all_array_compression='zlib')
+
+    # Re-register
+    MB.af = af
+
+    ##########################################
+    # For observation.
+    # Write out for the Multi-component fitting.
+    ##########################################
+    fw = open(DIR_TMP + 'spec_obs_' + ID + '.cat', 'w')
+    fw.write('# BB data (>%d) in this file are not used in fitting.\n'%(ncolbb))
+
+    for ii in range(len(lm)):
+        g_offset = 1000 * fgrs[ii]
+        if lm[ii]/(1.+zbest) > lamliml and lm[ii]/(1.+zbest) < lamlimu:
+            fw.write('%d %.5f %.5e %.5e\n'%(ii+g_offset, lm[ii], fobs[ii], eobs[ii]))
+        else:
+            fw.write('%d %.5f 0 1000\n'%(ii+g_offset, lm[ii]))
+
+    for ii in range(len(ltmpbb[0,:])):
+        if SFILT[ii] in SKIPFILT:# data point to be skiped;
+            fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii]))
+        elif  ebb[ii]>ebblim:
+            fw.write('%d %.5f 0 1000\n'%(ii+ncolbb, ltmpbb[0,ii]))
+        else:
+            fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii]))
+
+    fw.close()    
+
+
+    fw = open(DIR_TMP + 'spec_dust_obs_' + ID + '.cat', 'w')
+    if MB.f_dust:
+        nbblast = len(ltmpbb[0,:])+len(lm)
+        for ii in range(len(ebb_d[:])):
+            if ebb_d[ii]>ebblim:
+                fw.write('%d %.5f 0 1000\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast]))
+            else:
+                fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii]))
+    fw.close()
+
+    # BB phot
+    fw = open(DIR_TMP + 'bb_obs_' + ID + '.cat', 'w')
+    fw_rem = open(DIR_TMP + 'bb_obs_' + ID + '_removed.cat', 'w')
+    for ii in range(len(ltmpbb[0,:])):
+        if SFILT[ii] in SKIPFILT:# data point to be skiped;
+            fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii], FWFILT[ii]/2., SFILT[ii]))
+            fw_rem.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
+        elif ebb[ii]>ebblim:
+            fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
+        elif ebb[ii]<=0:
+            fw.write('%d %.5f 0 -99 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
+        else:
+            fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
+    fw.close()
+    fw_rem.close()
+
+    # Dust
+    fw = open(DIR_TMP + 'bb_dust_obs_' + ID + '.cat', 'w')
+    if MB.f_dust:
+        for ii in range(len(ebb_d[:])):
+            if  ebb_d[ii]>ebblim:
+                fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], DFWFILT[ii]/2., DFILT[ii]))
+            else:
+                fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii], DFWFILT[ii]/2., DFILT[ii]))
+    fw.close()
+
+    print('Done making templates at z=%.2f.\n'%zbest)
+
+    return True
diff --git a/build/lib/gsf/maketmp_z0.py b/build/lib/gsf/maketmp_z0.py
new file mode 100644
index 0000000..249bf17
--- /dev/null
+++ b/build/lib/gsf/maketmp_z0.py
@@ -0,0 +1,503 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+from astropy.io import ascii,fits
+
+from .function import get_ind
+
+INDICES = ['G4300', 'Mgb', 'Fe5270', 'Fe5335', 'NaD', 'Hb', 'Fe4668', 'Fe5015', 'Fe5709', 'Fe5782', 'Mg1', 'Mg2', 'TiO1', 'TiO2']
+
+
+def make_tmp_z0(MB, lammin=100, lammax=160000, tau_lim=0.001, force_no_neb=False, Zforce=None, f_mp=True):
+    '''
+    This is for the preparation of default template, with FSPS, at z=0.
+    Should be run before SED fitting.
+
+    Parameters
+    ----------
+    :class:`gsf.fitting.Mainbody` : class
+        Mainbody class, that contains attributes.
+
+    lammin : float, optional
+        Minimum value of the rest-frame wavelength of the template, in AA.
+
+    lammax : float, optional
+        Maximum value of the rest-frame wavelength of the template, in AA.
+
+    tau_lim : float, optional
+        Maximum value of tau of the template, in Gyr. Tau smaller than this 
+        value would be approximated by SSP.
+
+    force_no_neb : bool
+        Turn this on that you are very much sure do not want to include emission line templates, 
+        maybe to save some time running z0 module.
+
+    f_mp : bool
+        Multiprocessing.
+    '''
+    import asdf
+    import fsps
+    import gsf
+
+    nimf = MB.nimf
+    age = MB.age
+    tau0 = MB.tau0
+    fneb = MB.fneb
+    if not force_no_neb:
+        fneb = True
+    DIR_TMP = MB.DIR_TMP
+    Na = len(age)
+
+    if not Zforce == None:
+        file_out = 'spec_all_Z%.1f.asdf'%Zforce
+    else:
+        file_out = 'spec_all.asdf'
+    Z = MB.Zall
+    NZ = len(Z)
+    
+    # Current age in Gyr;
+    age_univ = MB.cosmo.age(0).value
+
+    print('#######################################')
+    print('Making templates at z=0, IMF=%d'%(nimf))
+    print('#######################################')
+    
+    tree_spec = {}
+    tree_ML = {}
+    tree_lick = {}
+
+    print('tau is the width of each age bin.')
+    tau_age = np.zeros(Na,dtype='float')
+    age_age = np.zeros(Na,dtype='float')
+    flagz = True
+    for zz in range(len(Z)):
+        if not Zforce == None and Z[zz] != Zforce:
+            continue
+        for pp in range(len(tau0)):
+            spall = [] # For ssp model
+            ms = np.zeros(Na, dtype='float')
+            Ls = np.zeros(Na, dtype='float')
+            mlost = np.zeros(Na, dtype='float')
+            LICK = np.zeros((Na,len(INDICES)), dtype='float')
+            tau0_old = 0
+            for ss in range(Na):
+                #
+                # Determining tau for each age bin;
+                #
+
+                # 1.Continuous age bin;
+                if int(tau0[pp]) == 99:
+                    if ss==0:
+                        delTl = age[ss]
+                        delTu = (age[ss+1]-age[ss])/2.
+                        delT = delTu + delTl
+                        tautmp = delT
+                        agetmp = age[ss]+delTu
+                    elif ss == len(age)-1:
+                        delTl = (age[ss]-age[ss-1])/2.
+                        delTu = delTl
+                        delT = delTu + delTl
+                        tautmp = delT
+                        agetmp = age[ss]+delTu
+                    else:
+                        delTl = (age[ss]-age[ss-1])/2.
+                        delTu = (age[ss+1]-age[ss])/2.
+                        delT = delTu + delTl
+                        tautmp = delT
+                        agetmp = age[ss]+delTu
+                # 2.A fixed-age bin;
+                elif tau0[pp] > 0.0:
+                    tautmp = tau0[pp]
+                    agetmp = age[ss] + tautmp/2.
+                # 3.SSP;
+                else: # =Negative tau;
+                    tautmp = tau_lim
+                    agetmp = age[ss]
+
+                # Keep tau in header;
+                tau_age[ss] = tautmp
+                age_age[ss] = agetmp
+
+                #
+                # Then, make sps.
+                #
+                f_add_dust = False # Not yet. Or never...
+                if tautmp != tau0_old:
+                    if int(tau0[pp]) == 99:
+                        print('CSP is applied.')
+                        print('At t=%.3f, tau is %.3f Gyr' %(age[ss],tautmp))
+                        sptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0) # Lsun/Hz
+                        if fneb:
+                            esptmp = fsps.StellarPopulation(zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0, add_neb_emission=1)
+                        if f_add_dust:
+                            dsptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz],
+                            dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0,
+                            duste_gamma=0.01, duste_umin=1.0, duste_qpah=3.5, fagn=0.0)
+                    elif tau0[pp] > 0.0:
+                        print('At t=%.3f, fixed tau, %.3f, is applied.'%(age[ss],tautmp))
+                        sptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0) # Lsun/Hz
+                        if fneb:
+                            esptmp = fsps.StellarPopulation(zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0, add_neb_emission=1)
+                        if f_add_dust:
+                            dsptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz], 
+                            dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0,
+                            duste_gamma=0.01, duste_umin=1.0, duste_qpah=3.5, fagn=0.0)
+                    else: # =Negative tau;
+                        print('At t=%.3f, SSP (%.3f) is applied.'%(age[ss],tautmp))
+                        sptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=0, logzsol=Z[zz], dust_type=2, dust2=0.0) # Lsun/Hz
+                        if fneb:
+                            esptmp = fsps.StellarPopulation(zcontinuous=1, imf_type=nimf, sfh=0, logzsol=Z[zz], dust_type=2, dust2=0.0, add_neb_emission=1)
+                        if f_add_dust:
+                            dsptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=0, logzsol=Z[zz], dust_type=2, dust2=1.0,
+                            tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0,
+                            duste_gamma=0.01, duste_umin=1.0, duste_qpah=3.5, fagn=0.0)
+                else:
+                    print('At t=%.3f, tau is %.3f Gyr' %(age[ss],tautmp))
+                    print('Skip fsps, by using previous library.')
+
+                tau0_old = tautmp
+                sp = sptmp
+                print('Z:%d/%d, t:%d/%d, %s, %s'%(zz+1, len(Z), pp+1, len(tau0), sp.libraries[0].decode("utf-8") , sp.libraries[1].decode("utf-8")))
+
+                wave0, flux0 = sp.get_spectrum(tage=age[ss], peraa=True) # Lsun/AA
+                con = (wave0>lammin) & (wave0<lammax)
+                wave, flux = wave0[con], flux0[con]
+                mlost[ss] = sp.stellar_mass / sp.formed_mass
+
+                if fneb and pp == 0 and ss == 0:
+                    esptmp.params['gas_logz'] = Z[zz] # gas metallicity, assuming = Zstel
+                    # Loop within logU;
+                    for nlogU, logUtmp in enumerate(MB.logUs):
+                        esptmp.params['gas_logu'] = logUtmp
+                        esp = esptmp
+                        ewave0, eflux0 = esp.get_spectrum(tage=0.001, peraa=True)
+                        con = (ewave0>lammin) & (ewave0<lammax)
+                        flux_nebular = eflux0[con]-flux
+                        tree_spec.update({'flux_nebular_Z%d'%zz+'_logU%d'%nlogU: flux_nebular})
+                
+                if f_add_dust:
+                    wave0_d, flux0_d = dsptmp.get_spectrum(tage=age[ss], peraa=True)
+                    con = (wave0_d>lammin) & (wave0_d<lammax)
+                    wave_d, flux_d = wave0[con], flux0[con]
+                    plt.plot(wave0, flux0, linestyle='--')
+                    plt.plot(wave0_d, flux0_d, linestyle='--')
+                    plt.xlim(4000,100000)
+                    plt.xscale('log')
+                    plt.show()
+
+                # Mass-Luminosity
+                ms[ss] = sp.stellar_mass # Survived mass
+                Ls[ss] = 10**sp.log_lbol
+                LICK[ss,:] = get_ind(wave, flux)
+
+                if flagz and ss == 0 and pp == 0:
+                    # ASDF Big tree;
+                    # Create header;
+                    tree = {
+                        'isochrone': '%s'%(sp.libraries[0].decode("utf-8")),
+                        'library': '%s'%(sp.libraries[1].decode("utf-8")),
+                        'nimf': nimf,
+                        'version_gsf': gsf.__version__
+                    }
+                    if fneb:
+                        tree.update({'logUMIN': MB.logUMIN})
+                        tree.update({'logUMAX': MB.logUMAX})
+                        tree.update({'DELlogU': MB.DELlogU})
+
+                    tree_spec.update({'wavelength': wave})
+                    flagz = False
+
+                tree_spec.update({'fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp): flux})
+
+            if pp == 0:
+                # use tau0[0] as representative for M/L and index.
+                for ll in range(len(INDICES)):
+                    # ASDF
+                    tree_lick.update({INDICES[ll]+'_'+str(zz): LICK[:,ll]})
+
+                col1 = fits.Column(name='ms_'+str(zz), format='E', unit='Msun', array=ms)
+                tree_ML.update({'ms_'+str(zz): ms})
+                col2 = fits.Column(name='Ls_'+str(zz), format='E', unit='Lsun', array=Ls)
+                tree_ML.update({'Ls_'+str(zz): Ls})
+                col3 = fits.Column(name='fm_'+str(zz), format='E', unit='', array=mlost)
+                tree_ML.update({'frac_mass_survive_'+str(zz): mlost})
+                col4 = fits.Column(name='tau_'+str(zz), format='E', unit='Gyr', array=tau_age)
+                tree_ML.update({'realtau_'+str(zz): ms})
+
+
+    # Write;
+    for aa in range(len(age)):
+        tree.update({'realtau%d(Gyr)'%(aa): tau_age[aa]})
+    for aa in range(len(age)):
+        tree.update({'realage%d(Gyr)'%(aa): age_age[aa]})
+    for aa in range(len(age)):
+        tree.update({'age%d'%(aa): age[aa]})
+    for aa in range(len(Z)):
+        tree.update({'Z%d'%(aa): Z[aa]})
+    for aa in range(len(tau0)):
+        tree.update({'tau0%d'%(aa): tau0[aa]})
+
+    # Index, Mass-to-light;
+    tree.update({'spec' : tree_spec})
+    tree.update({'ML' : tree_ML})
+    tree.update({'lick' : tree_lick})
+
+    # Save
+    af = asdf.AsdfFile(tree)
+    af.write_to(DIR_TMP + file_out, all_array_compression='zlib')
+
+
+def make_tmp_z0_bpass(MB, lammin=100, lammax=160000, Zforce=None, Zsun=0.02):
+    '''
+    This is for the preparation of default template, with BPASS templates, at z=0.
+    Should be run before SED fitting.
+
+    Parameters
+    ----------
+    :class:`gsf.fitting.Mainbody` : class
+        Mainbody class, that contains attributes.
+
+    lammin : float, optional
+        Minimum value of the rest-frame wavelength of the template, in AA.
+
+    lammax : float, optional
+        Maximum value of the rest-frame wavelength of the template, in AA.
+
+    BPASS_DIR : str, optional
+        Path to the ditectory where BPASS templates are storesd.
+
+    BPASS_ver : str, optional
+        Version of BPASS. Used to identify template files.
+
+    Zsun : float, optional
+        Metallicity of templates, in units of absolute value (e.g. Zsun=0.02 for BPASS).
+    '''
+    import asdf
+    import gsf
+
+    nimf = MB.nimf
+    if nimf == 0: # Salpeter
+        imf_str = '135all_100'
+    elif nimf == 1:
+        imf_str = '_chab100'
+    else:
+        imf_str = ''
+
+    if not Zforce == None:
+        file_out = 'spec_all_Z%.1f.asdf'%Zforce
+    else:
+        file_out = 'spec_all.asdf'
+
+    Z = MB.Zall
+    age = MB.age
+    tau0 = MB.tau0
+    fneb = MB.fneb
+    if fneb:
+        print('Currently, BPASS does not have option of nebular emission.')
+        fneb = False
+    DIR_TMP= MB.DIR_TMP
+
+    # binary?
+    f_bin = MB.f_bin
+    #f_bin = True
+    if f_bin==1:
+        bin_str = 'bin'
+    else:
+        bin_str = 'str'
+
+    DIR_LIB = MB.BPASS_DIR + 'BPASS%s/BPASS%s_%s-imf%s/'%(MB.BPASS_ver,MB.BPASS_ver,bin_str,imf_str)
+
+    NZ = len(Z)
+    Na = len(age)
+
+    # Current age in Gyr;
+    age_univ = MB.cosmo.age(0).value
+
+    print('#######################################')
+    print('Making templates at z=0, IMF=%d'%(nimf))
+    print('#######################################')
+
+    tree_spec = {}
+    tree_ML = {}
+    tree_lick = {}
+
+    tau_age = np.zeros(Na,dtype='float')
+    age_age = np.zeros(Na,dtype='float')
+
+    flagz = True
+    for zz in range(len(Z)):
+        if not Zforce == None and Z[zz] != Zforce:
+            continue
+        #
+        # open spectral file;
+        #
+        if 10**(Z[zz])*Zsun>1e-4:
+            zstrtmp = round(10**(Z[zz])*Zsun/10,5)
+            zstrtmp2 = '%.6s'%(zstrtmp)
+            z_str = zstrtmp2[3:]
+            if len(z_str)<3:
+                z_str = z_str+'0'
+        elif 10**(Z[zz])*Zsun>1e-5:
+            z_str = 'em4'
+        else:
+            z_str = 'em5'
+
+        file_sed = '%sspectra-%s-imf%s.z%s.dat'%(DIR_LIB,bin_str,imf_str,z_str)
+        file_stm = '%sstarmass-%s-imf%s.z%s.dat'%(DIR_LIB,bin_str,imf_str,z_str)
+        fd_sed = ascii.read(file_sed)
+        fd_stm = ascii.read(file_stm)
+
+        wave0 = fd_sed['col1']
+        age_stm = fd_stm['col1']
+        mass_formed = fd_stm['col2'][0]
+
+        ncols = 52
+        nage_temp = np.arange(2,ncols+1,1)
+        lage_temp = (6+0.1*(nage_temp-2))
+        age_temp = 10**(6+0.1*(nage_temp-2)) # in yr
+
+        # 'tau is the width of each age bin.'
+        for pp in range(len(tau0)):
+            spall = [] # For sps model
+            ms = np.zeros(Na, dtype='float')
+            Ls = np.zeros(Na, dtype='float')
+            mlost = np.zeros(Na, dtype='float')
+            LICK = np.zeros((Na,len(INDICES)), dtype='float')
+            tau0_old = 0
+
+            for ss in range(Na):
+                mass_formed_tot = 0
+                flux0 = np.zeros(len(wave0),'float')
+                #
+                # Determining tau for each age bin;
+                #
+                if int(tau0[pp]) == 99:
+                    if ss==0:
+                        tautmp = age[ss]
+                        agetmp = age[ss]/2.
+                        con_tau= np.where((age_temp[:]<=age[ss]*1e9) & (age_temp[:]>0))
+                        for sstmp in con_tau[0][-1:]:
+                            flux0  += fd_sed['col%d'%(sstmp+2)][:]
+                            ms[ss] += fd_stm['col2'][sstmp]
+                            mass_formed_tot += mass_formed
+                    else:
+                        tautmp = age[ss] - age[ss-1]
+                        agetmp = age[ss] - (age[ss]-age[ss-1])/2.
+                        con_tau= np.where((age_temp[:]<=age[ss]*1e9) & (age_temp[:]>age[ss-1]*1e9))
+                        for sstmp in con_tau[0][-1:]:
+                            flux0  += fd_sed['col%d'%(sstmp+2)][:]
+                            ms[ss] += fd_stm['col2'][sstmp]
+                            mass_formed_tot += mass_formed
+
+                elif tau0[pp] > 0.0:
+                    if ss==0 and age[ss]<tau0[pp]:
+                        tautmp = age[ss]
+                        agetmp = age[ss]/2.
+                        con_tau= np.where((age_temp[:]<=age[ss]*1e9) & (age_temp[:]>0))
+                        for sstmp in con_tau[0]:
+                            flux0  += fd_sed['col%d'%(sstmp+2)][:]
+                            ms[ss] += fd_stm['col2'][sstmp]
+                            mass_formed_tot += mass_formed
+
+                    elif (age[ss]-age[ss-1]) < tau0[pp]:
+                        tautmp = age[ss] - age[ss-1]
+                        agetmp = age[ss] - (age[ss]-age[ss-1])/2.
+                        con_tau= np.where((age_temp[:]<=age[ss]*1e9) & (age_temp[:]>age[ss-1]*1e9))
+                        for sstmp in con_tau[0]:
+                            flux0  += fd_sed['col%d'%(sstmp+2)][:]
+                            ms[ss] += fd_stm['col2'][sstmp]
+                            mass_formed_tot += mass_formed
+
+                    else:
+                        tautmp = tau0[pp]
+                        agetmp = age[ss] - tau0[pp]/2.
+                        con_tau= np.where((age_temp[:]<=age[ss]*1e9) & (age_temp[:]>(age[ss] - tau0[pp])*1e9))
+                        for sstmp in con_tau[0]:
+                            flux0  += fd_sed['col%d'%(sstmp+2)][:]
+                            ms[ss] += fd_stm['col2'][sstmp]
+                            mass_formed_tot += mass_formed
+
+                else: # =Negative tau; SSP
+                    iis   = np.argmin(np.abs(age[ss] - age_temp[:]/1e9))
+                    iistm = np.argmin(np.abs(age[ss] - 10**age_stm[:]/1e9))
+                    if ss==0:
+                        tautmp = 10**6.05 / 1e9 # in Gyr
+                        agetmp = age[ss]/2.
+                    else:
+                        tautmp = ( 10**(lage_temp[iis]+0.05) - 10**(lage_temp[iis]-0.05) ) / 1e9 # Gyr
+                        agetmp = (age[ss]+age[ss-1])/2.
+
+                    flux0  = fd_sed['col%d'%(iis+2)]
+                    ms[ss] = fd_stm['col2'][iistm]
+                    mass_formed_tot += mass_formed
+
+                # Keep tau in header;
+                tau_age[ss] = tautmp
+                age_age[ss] = agetmp
+
+                # Then. add flux if tau > 0.
+                con = (wave0>lammin) & (wave0<lammax)
+                wave, flux = wave0[con], flux0[con]
+                # Temp
+                mlost[ss] =  ms[ss] / mass_formed_tot
+
+                Ls[ss] = np.sum(flux0) # BPASS sed is in Lsun.
+                LICK[ss,:] = get_ind(wave, flux)
+
+                if flagz and ss == 0 and pp == 0:
+                    # ASDF Big tree;
+                    # Create header;
+                    tree = {
+                        'isochrone': 'BPASS',
+                        'library': 'BPASS',
+                        'nimf': nimf,
+                        'version_gsf': gsf.__version__
+                    }
+
+                    # ASDF
+                    tree_spec.update({'wavelength': wave})
+                    flagz = True
+
+                # ASDF
+                tree_spec.update({'fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp): flux})
+
+                if fneb:
+                    # ASDF
+                    tree_spec.update({'efspec_'+str(zz)+'_'+str(ss)+'_'+str(pp): eflux})
+
+            if pp == 0:
+                # use tau0[0] as representative for M/L and index.
+                for ll in range(len(INDICES)):
+                    # ASDF
+                    tree_lick.update({INDICES[ll]+'_'+str(zz): LICK[:,ll]})
+
+                col1 = fits.Column(name='ms_'+str(zz), format='E', unit='Msun', array=ms)
+                tree_ML.update({'ms_'+str(zz): ms})
+                col2 = fits.Column(name='Ls_'+str(zz), format='E', unit='Lsun', array=Ls)
+                tree_ML.update({'Ls_'+str(zz): Ls})
+                col3 = fits.Column(name='fm_'+str(zz), format='E', unit='', array=mlost)
+                tree_ML.update({'frac_mass_survive_'+str(zz): mlost})
+                col4 = fits.Column(name='tau_'+str(zz), format='E', unit='Gyr', array=tau_age)
+                tree_ML.update({'realtau_'+str(zz): ms})
+
+    # Write;
+    for aa in range(len(age)):
+        tree.update({'realtau%d(Gyr)'%(aa): tau_age[aa]})
+    for aa in range(len(age)):
+        tree.update({'realage%d(Gyr)'%(aa): age_age[aa]})
+    for aa in range(len(age)):
+        tree.update({'age%d'%(aa): age[aa]})
+    for aa in range(len(Z)):
+        tree.update({'Z%d'%(aa): Z[aa]})
+    for aa in range(len(tau0)):
+        tree.update({'tau0%d'%(aa): tau0[aa]})
+
+    # Index, Mass-to-light;
+    tree.update({'spec' : tree_spec})
+    tree.update({'ML' : tree_ML})
+    tree.update({'lick' : tree_lick})
+
+    # Save
+    af = asdf.AsdfFile(tree)
+    af.write_to(DIR_TMP + file_out, all_array_compression='zlib')
+
diff --git a/build/lib/gsf/maketmp_z0_tau.py b/build/lib/gsf/maketmp_z0_tau.py
new file mode 100644
index 0000000..fa66b40
--- /dev/null
+++ b/build/lib/gsf/maketmp_z0_tau.py
@@ -0,0 +1,187 @@
+import numpy as np
+from astropy.io import fits
+import matplotlib.pyplot as plt
+import os
+import sys
+from astropy.io import ascii
+
+from .function import get_ind
+
+INDICES = ['G4300', 'Mgb', 'Fe5270', 'Fe5335', 'NaD', 'Hb', 'Fe4668', 'Fe5015', 'Fe5709', 'Fe5782', 'Mg1', 'Mg2', 'TiO1', 'TiO2']
+
+
+def get_lognorm(t, ltau0, T0=-10):
+    A   = 1
+    tau0= 10**ltau0
+    SFR = t * 0 + 1e-20
+    conlogn = (t>0)
+    SFR[conlogn] = A / np.sqrt(2*np.pi*tau0**2) * np.exp(-(np.log(t[conlogn])-T0)**2/(2*tau0**2)) / t[conlogn]
+    return SFR
+
+
+def make_tmp_z0(MB, lammin=100, lammax=160000, Zforce=None): 
+    '''
+    This is for the preparation of default template, with FSPS, at z=0.
+    Should be run before SED fitting.
+
+    Parameters
+    ----------
+    :class:`gsf.fitting.Mainbody` : class
+        Mainbody class, that contains attributes.
+
+    lammin : float, optional
+        Minimum value of the rest-frame wavelength of the template, in AA.
+
+    lammax : float, optional
+        Maximum value of the rest-frame wavelength of the template, in AA.
+
+    tau_lim : float, optional
+        Maximum value of tau of the template, in Gyr. Tau smaller than this 
+        value would be approximated by SSP.
+    '''
+    import asdf
+    import fsps
+    import gsf
+    
+    nimf = MB.nimf
+    age = MB.ageparam
+    fneb = MB.fneb
+    DIR_TMP = MB.DIR_TMP
+    Na = len(age)
+
+    tau = MB.tau
+    sfh = MB.SFH_FORM
+
+    if not Zforce == None:
+        file_out = 'spec_all_Z%.1f.asdf'%Zforce
+    else:
+        file_out = 'spec_all.asdf'
+    Z = MB.Zall
+    NZ = len(Z)
+    
+    # Current age in Gyr;
+    age_univ = MB.cosmo.age(0).value
+
+    NZ = len(Z)
+    Nt = len(tau)
+    Na = len(age)
+
+    ms = np.zeros(Na, dtype='float')
+    Ls = np.zeros(Na, dtype='float')
+
+    print('#######################################')
+    print('Making templates at z=0, IMF=%d'%(nimf))
+    print('#######################################')
+    col01 = [] # For M/L ratio.
+    col02 = [] # For templates
+    col05 = [] # For spectral indices.
+
+    tree_spec = {}
+    tree_ML = {}
+    tree_lick = {}
+
+    print('tau is the width of each age bin.')
+    flagz = True
+    for zz in range(len(Z)):
+        if not Zforce == None and Z[zz] != Zforce:
+            continue
+        for ss in range(len(tau)):
+            if 10**tau[ss]<0.01:
+                # then do SSP
+                print('!! tau is <0.01Gyr. SSP is applied. !!') # This corresponds to the min-tau of fsps.
+                sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=0, logzsol=Z[zz], dust_type=2, dust2=0.0) # Lsun/Hz
+                if fneb == 1:
+                    esptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=0, logzsol=Z[zz], dust_type=2, dust2=0.0, add_neb_emission=1) # Lsun/Hz
+            elif sfh<5:
+                sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=sfh, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=10**tau[ss], const=0, sf_start=0, sf_trunc=0, tburst=13, fburst=0) # Lsun/Hz
+                if fneb == 1:
+                    esptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=sfh, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=10**tau[ss], const=0, sf_start=0, sf_trunc=0, tburst=13, fburst=0, add_neb_emission=1) # Lsun/Hz
+            elif sfh==6: # Custom SFH
+                sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=3, imf_type=nimf, sfh=3, dust_type=2, dust2=0.0)
+                if fneb == 1:
+                    sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=3, imf_type=nimf, sfh=3, dust_type=2, dust2=0.0, add_neb_emission=1)
+                print('Log normal is used. !!')
+
+            print('Z:%d/%d, t:%d/%d, %s, %s'%(zz+1, len(Z), ss+1, len(tau), sp.libraries[0].decode("utf-8") , sp.libraries[1].decode("utf-8")))
+            ms = np.zeros(Na)
+            Ls = np.zeros(Na)
+            LICK = np.zeros((Na,len(INDICES)), dtype='float')
+            mlost = np.zeros(Na, dtype='float')
+
+            for tt in range(len(age)):
+                if sfh==6: # Tabular SFH.
+                    tuniv_hr  = np.arange(0,age_univ,0.01) # in Gyr
+                    T0 = np.log(10**age[tt])
+                    sfh_hr_in = get_lognorm(tuniv_hr, tau[ss], T0) # tau in log Gyr
+                    zh_hr_in  = tuniv_hr*0 + 10**Z[zz] # metallicity is constant
+                    sp.set_tabular_sfh(tuniv_hr, sfh_hr_in, zh_hr_in)
+                    wave0, flux0 = sp.get_spectrum(tage=age_univ, peraa=True) # if peraa=True, in unit of L/AA
+                else:
+                    wave0, flux0 = sp.get_spectrum(tage=10**age[tt], peraa=True) # if peraa=True, in unit of L/AA
+
+                con = (wave0>lammin) & (wave0<lammax)
+                wave, flux = wave0[con], flux0[con]
+                ms[tt] = sp.stellar_mass
+                if np.isnan(ms[tt]):
+                    print('Error at tau element at',tt)
+                    sys.exit()
+
+                Ls[tt] = 10**sp.log_lbol
+                LICK[tt,:] = get_ind(wave, flux)
+                mlost[tt] = sp.stellar_mass / sp.formed_mass
+
+                if fneb and tt == 0 and ss == 0:
+                    esptmp.params['gas_logz'] = Z[zz] # gas metallicity, assuming = Zstel
+                    # Loop within logU;
+                    for nlogU, logUtmp in enumerate(MB.logUs):
+                        esptmp.params['gas_logu'] = logUtmp
+                        esp = esptmp
+                        ewave0, eflux0 = esp.get_spectrum(tage=0.001, peraa=True)
+                        con = (ewave0>lammin) & (ewave0<lammax)
+                        flux_nebular = eflux0[con]-flux
+                        tree_spec.update({'flux_nebular_Z%d'%zz+'_logU%d'%nlogU: flux_nebular})
+
+                if flagz and ss == 0 and tt == 0:
+                    # ASDF Big tree;
+                    # Create header;
+                    tree = {
+                        'isochrone': '%s'%(sp.libraries[0].decode("utf-8")),
+                        'library': '%s'%(sp.libraries[1].decode("utf-8")),
+                        'nimf': nimf,
+                        'version_gsf': gsf.__version__
+                    }
+                    if fneb == 1:
+                        tree.update({'logUMIN': MB.logUMIN})
+                        tree.update({'logUMAX': MB.logUMAX})
+                        tree.update({'DELlogU': MB.DELlogU})
+
+                    tree_spec.update({'wavelength': wave})
+                    flagz = False
+
+                tree_spec.update({'fspec_'+str(zz)+'_'+str(ss)+'_'+str(tt): flux})
+
+            for ll in range(len(INDICES)):
+                # ASDF
+                tree_lick.update({INDICES[ll]+'_'+str(zz)+'_'+str(ss): LICK[:,ll]})
+
+            tree_ML.update({'ms_'+str(zz)+'_'+str(ss): ms})
+            tree_ML.update({'Ls_'+str(zz)+'_'+str(ss): Ls})
+            tree_ML.update({'frac_mass_survive_'+str(zz)+'_'+str(ss): mlost})
+
+
+    # Write;
+    for aa in range(len(tau)):
+        tree.update({'tau%d'%(aa): tau[aa]})
+    for aa in range(len(age)):
+        tree.update({'age%d'%(aa): age[aa]})
+    for aa in range(len(Z)):
+        tree.update({'Z%d'%(aa): Z[aa]})
+
+    # Index, Mass-to-light;
+    tree.update({'spec' : tree_spec})
+    tree.update({'ML' : tree_ML})
+    tree.update({'lick' : tree_lick})
+
+    # Save
+    af = asdf.AsdfFile(tree)
+    af.write_to(DIR_TMP + file_out, all_array_compression='zlib')
diff --git a/build/lib/gsf/minimizer.py b/build/lib/gsf/minimizer.py
new file mode 100644
index 0000000..35ea50a
--- /dev/null
+++ b/build/lib/gsf/minimizer.py
@@ -0,0 +1,2428 @@
+"""Simple minimizer is a wrapper around scipy.leastsq, allowing a user to build
+a fitting model as a function of general purpose Fit Parameters that can be
+fixed or varied, bounded, and written as a simple expression of other Fit
+Parameters.
+
+The user sets up a model in terms of instance of Parameters and writes a
+function-to-be-minimized (residual function) in terms of these Parameters.
+
+Original copyright:
+   Copyright (c) 2011 Matthew Newville, The University of Chicago
+
+See LICENSE for more complete authorship information and license.
+
+"""
+from collections import namedtuple
+from copy import deepcopy
+import multiprocessing
+import numbers
+import warnings
+
+import numpy as np
+from numpy import ndarray, ones_like, sqrt
+from numpy.linalg import inv
+from numpy.linalg import LinAlgError
+from scipy.optimize import basinhopping as scipy_basinhopping
+from scipy.optimize import brute as scipy_brute
+from scipy.optimize import differential_evolution
+from scipy.optimize import dual_annealing as scipy_dual_annealing
+from scipy.optimize import least_squares
+from scipy.optimize import leastsq as scipy_leastsq
+from scipy.optimize import minimize as scipy_minimize
+from scipy.optimize import shgo as scipy_shgo
+from scipy.sparse import issparse
+from scipy.sparse.linalg import LinearOperator
+from scipy.stats import cauchy as cauchy_dist
+from scipy.stats import norm as norm_dist
+from scipy.version import version as scipy_version
+import uncertainties
+
+from lmfit._ampgo import ampgo
+from lmfit.parameter import Parameter, Parameters
+from lmfit.printfuncs import fitreport_html_table
+
+# check for EMCEE
+try:
+    import emcee
+    from emcee.autocorr import AutocorrError
+    HAS_EMCEE = int(emcee.__version__[0]) >= 3
+except ImportError:
+    HAS_EMCEE = False
+
+# check for pandas
+try:
+    import pandas as pd
+    from pandas import isnull
+    HAS_PANDAS = True
+except ImportError:
+    HAS_PANDAS = False
+    isnull = np.isnan
+
+# check for numdifftools
+try:
+    import numdifftools as ndt
+    HAS_NUMDIFFTOOLS = True
+except ImportError:
+    HAS_NUMDIFFTOOLS = False
+
+# check for dill
+try:
+    import dill   # noqa: F401
+    HAS_DILL = True
+except ImportError:
+    HAS_DILL = False
+
+
+# define the namedtuple here so pickle will work with the MinimizerResult
+Candidate = namedtuple('Candidate', ['params', 'score'])
+
+
+def asteval_with_uncertainties(*vals, **kwargs):
+    """Calculate object value, given values for variables.
+
+    This is used by the uncertainties package to calculate the
+    uncertainty in an object even with a complicated expression.
+
+    """
+    _obj = kwargs.get('_obj', None)
+    _pars = kwargs.get('_pars', None)
+    _names = kwargs.get('_names', None)
+    _asteval = _pars._asteval
+    if (_obj is None or _pars is None or _names is None or
+            _asteval is None or _obj._expr_ast is None):
+        return 0
+    for val, name in zip(vals, _names):
+        _asteval.symtable[name] = val
+    return _asteval.eval(_obj._expr_ast)
+
+
+wrap_ueval = uncertainties.wrap(asteval_with_uncertainties)
+
+
+def eval_stderr(obj, uvars, _names, _pars):
+    """Evaluate uncertainty and set .stderr for a parameter `obj`.
+
+    Given the uncertain values `uvars` (a list of uncertainties.ufloats), a
+    list of parameter names that matches uvars, and a dict of param objects,
+    keyed by name.
+
+    This uses the uncertainties package wrapped function to evaluate the
+    uncertainty for an arbitrary expression (in obj._expr_ast) of parameters.
+
+    """
+    if not isinstance(obj, Parameter) or getattr(obj, '_expr_ast', None) is None:
+        return
+    uval = wrap_ueval(*uvars, _obj=obj, _names=_names, _pars=_pars)
+    try:
+        obj.stderr = uval.std_dev
+    except Exception:
+        obj.stderr = 0
+
+
+class MinimizerException(Exception):
+    """General Purpose Exception."""
+
+    def __init__(self, msg):
+        Exception.__init__(self)
+        self.msg = msg
+
+    def __str__(self):
+        return "{}".format(self.msg)
+
+
+class AbortFitException(MinimizerException):
+    """Raised when a fit is aborted by the user."""
+
+    pass
+
+
+SCALAR_METHODS = {'nelder': 'Nelder-Mead',
+                  'powell': 'Powell',
+                  'cg': 'CG',
+                  'bfgs': 'BFGS',
+                  'newton': 'Newton-CG',
+                  'lbfgsb': 'L-BFGS-B',
+                  'l-bfgsb': 'L-BFGS-B',
+                  'tnc': 'TNC',
+                  'cobyla': 'COBYLA',
+                  'slsqp': 'SLSQP',
+                  'dogleg': 'dogleg',
+                  'trust-ncg': 'trust-ncg',
+                  'differential_evolution': 'differential_evolution',
+                  'trust-constr': 'trust-constr',
+                  'trust-exact': 'trust-exact',
+                  'trust-krylov': 'trust-krylov'}
+
+
+def reduce_chisquare(r):
+    """Reduce residual array to scalar (chi-square).
+
+    Calculate the chi-square value from the residual array `r`: (r*r).sum()
+
+    Parameters
+    ----------
+    r : numpy.ndarray
+        Residual array.
+
+    Returns
+    -------
+    float
+        Chi-square calculated from the residual array
+
+    """
+    return (r*r).sum()
+
+
+def reduce_negentropy(r):
+    """Reduce residual array to scalar (negentropy).
+
+    Reduce residual array `r` to scalar using negative entropy and the normal
+    (Gaussian) probability distribution of `r` as pdf:
+
+       (norm.pdf(r)*norm.logpdf(r)).sum()
+
+    since pdf(r) = exp(-r*r/2)/sqrt(2*pi), this is
+       ((r*r/2 - log(sqrt(2*pi))) * exp(-r*r/2)).sum()
+
+    Parameters
+    ----------
+    r : numpy.ndarray
+        Residual array.
+
+    Returns
+    -------
+    float
+        Negative entropy value calculated from the residual array
+
+    """
+    return (norm_dist.pdf(r)*norm_dist.logpdf(r)).sum()
+
+
+def reduce_cauchylogpdf(r):
+    """Reduce residual array to scalar (cauchylogpdf).
+
+    Reduce residual array `r` to scalar using negative log-likelihood and a
+    Cauchy (Lorentzian) distribution of `r`:
+
+       -scipy.stats.cauchy.logpdf(r)
+
+    (where the Cauchy pdf = 1/(pi*(1+r*r))). This gives greater
+    suppression of outliers compared to normal sum-of-squares.
+
+    Parameters
+    ----------
+    r : numpy.ndarray
+        Residual array.
+
+    Returns
+    -------
+    float
+        Negative entropy value calculated from the residual array
+
+    """
+    return -cauchy_dist.logpdf(r).sum()
+
+
+class MinimizerResult:
+    r"""The results of a minimization.
+
+    Minimization results include data such as status and error messages,
+    fit statistics, and the updated (i.e., best-fit) parameters themselves
+    in the :attr:`params` attribute.
+
+    The list of (possible) `MinimizerResult` attributes is given below:
+
+    Attributes
+    ----------
+    params : :class:`~lmfit.parameter.Parameters`
+        The best-fit parameters resulting from the fit.
+    status : int
+        Termination status of the optimizer. Its value depends on the
+        underlying solver. Refer to `message` for details.
+    var_names : list
+        Ordered list of variable parameter names used in optimization, and
+        useful for understanding the values in :attr:`init_vals` and
+        :attr:`covar`.
+    covar : numpy.ndarray
+        Covariance matrix from minimization, with rows and columns
+        corresponding to :attr:`var_names`.
+    init_vals : list
+        List of initial values for variable parameters using :attr:`var_names`.
+    init_values : dict
+        Dictionary of initial values for variable parameters.
+    nfev : int
+        Number of function evaluations.
+    success : bool
+        True if the fit succeeded, otherwise False.
+    errorbars : bool
+        True if uncertainties were estimated, otherwise False.
+    message : str
+        Message about fit success.
+    ier : int
+        Integer error value from :scipydoc:`optimize.leastsq` (`leastsq` only).
+    lmdif_message : str
+        Message from :scipydoc:`optimize.leastsq` (`leastsq` only).
+    nvarys : int
+        Number of variables in fit: :math:`N_{\rm varys}`.
+    ndata : int
+        Number of data points: :math:`N`.
+    nfree : int
+        Degrees of freedom in fit: :math:`N - N_{\rm varys}`.
+    residual : numpy.ndarray
+        Residual array :math:`{\rm Resid_i}`. Return value of the objective
+        function when using the best-fit values of the parameters.
+    chisqr : float
+        Chi-square: :math:`\chi^2 = \sum_i^N [{\rm Resid}_i]^2`.
+    redchi : float
+        Reduced chi-square:
+        :math:`\chi^2_{\nu}= {\chi^2} / {(N - N_{\rm varys})}`.
+    aic : float
+        Akaike Information Criterion statistic:
+        :math:`N \ln(\chi^2/N) + 2 N_{\rm varys}`.
+    bic : float
+        Bayesian Information Criterion statistic:
+        :math:`N \ln(\chi^2/N) + \ln(N) N_{\rm varys}`.
+    flatchain : pandas.DataFrame
+        A flatchain view of the sampling chain from the `emcee` method.
+
+    Methods
+    -------
+    show_candidates
+        Pretty_print() representation of candidates from the `brute` method.
+
+    """
+
+    def __init__(self, **kws):
+        for key, val in kws.items():
+            setattr(self, key, val)
+
+    @property
+    def flatchain(self):
+        """Show flatchain view of the sampling chain from `emcee` method."""
+        if hasattr(self, 'chain'):
+            if HAS_PANDAS:
+                if len(self.chain.shape) == 4:
+                    return pd.DataFrame(self.chain[0, ...].reshape((-1, self.nvarys)),
+                                        columns=self.var_names)
+                elif len(self.chain.shape) == 3:
+                    return pd.DataFrame(self.chain.reshape((-1, self.nvarys)),
+                                        columns=self.var_names)
+            else:
+                raise NotImplementedError('Please install Pandas to see the '
+                                          'flattened chain')
+        else:
+            return None
+
+    def show_candidates(self, candidate_nmb='all'):
+        """Show pretty_print() representation of candidates from `brute` method.
+
+        Showing all stored candidates (default) or the specified candidate-#
+        from the `brute` method.
+
+        Parameters
+        ----------
+        candidate_nmb : int or 'all'
+            The candidate-number to show using the :meth:`pretty_print` method.
+
+        """
+        if hasattr(self, 'candidates'):
+            if candidate_nmb == 'all':
+                for i, candidate in enumerate(self.candidates):
+                    print("\nCandidate #{}, chisqr = "
+                          "{:.3f}".format(i+1, candidate.score))
+                    candidate.params.pretty_print()
+            elif (candidate_nmb < 1 or candidate_nmb > len(self.candidates)):
+                raise ValueError("'candidate_nmb' should be between 1 and {}."
+                                 .format(len(self.candidates)))
+            else:
+                candidate = self.candidates[candidate_nmb-1]
+                print("\nCandidate #{}, chisqr = "
+                      "{:.3f}".format(candidate_nmb, candidate.score))
+                candidate.params.pretty_print()
+
+    def _calculate_statistics(self):
+        """Calculate the fitting statistics."""
+        self.nvarys = len(self.init_vals)
+        if isinstance(self.residual, ndarray):
+            self.chisqr = (self.residual**2).sum()
+            self.ndata = len(self.residual)
+            self.nfree = self.ndata - self.nvarys
+        else:
+            self.chisqr = self.residual
+            self.ndata = 1
+            self.nfree = 1
+        self.redchi = self.chisqr / max(1, self.nfree)
+        # this is -2*loglikelihood
+        _neg2_log_likel = self.ndata * np.log(self.chisqr / self.ndata)
+        self.aic = _neg2_log_likel + 2 * self.nvarys
+        self.bic = _neg2_log_likel + np.log(self.ndata) * self.nvarys
+
+    def _repr_html_(self, show_correl=True, min_correl=0.1):
+        """Returns a HTML representation of parameters data."""
+        return fitreport_html_table(self, show_correl=show_correl,
+                                    min_correl=min_correl)
+
+
+class Minimizer:
+    """A general minimizer for curve fitting and optimization."""
+
+    _err_nonparam = ("params must be a minimizer.Parameters() instance or list "
+                     "of Parameters()")
+    _err_maxfev = ("Too many function calls (max set to %i)!  Use:"
+                   " minimize(func, params, ..., maxfev=NNN)"
+                   "or set leastsq_kws['maxfev']  to increase this maximum.")
+
+    def __init__(self, userfcn, params, fcn_args=None, fcn_kws=None,
+                 iter_cb=None, scale_covar=True, nan_policy='raise',
+                 reduce_fcn=None, calc_covar=True, **kws):
+        """
+        Parameters
+        ----------
+        userfcn : callable
+            Objective function that returns the residual (difference between
+            model and data) to be minimized in a least-squares sense.  This
+            function must have the signature::
+
+                userfcn(params, *fcn_args, **fcn_kws)
+
+        params : :class:`~lmfit.parameter.Parameters`
+            Contains the Parameters for the model.
+        fcn_args : tuple, optional
+            Positional arguments to pass to `userfcn`.
+        fcn_kws : dict, optional
+            Keyword arguments to pass to `userfcn`.
+        iter_cb : callable, optional
+            Function to be called at each fit iteration. This function should
+            have the signature::
+
+                iter_cb(params, iter, resid, *fcn_args, **fcn_kws)
+
+            where `params` will have the current parameter values, `iter`
+            the iteration number, `resid` the current residual array, and `*fcn_args`
+            and `**fcn_kws` are passed to the objective function.
+        scale_covar : bool, optional
+            Whether to automatically scale the covariance matrix (default is True).
+        nan_policy : str, optional
+            Specifies action if `userfcn` (or a Jacobian) returns NaN
+            values. One of:
+
+            - 'raise' : a `ValueError` is raised
+            - 'propagate' : the values returned from `userfcn` are un-altered
+            - 'omit' : non-finite values are filtered
+
+        reduce_fcn : str or callable, optional
+            Function to convert a residual array to a scalar value for the scalar
+            minimizers. Optional values are (where `r` is the residual array):
+
+            - None : sum of squares of residual [default]
+
+               = (r*r).sum()
+
+            - 'negentropy' : neg entropy, using normal distribution
+
+               = rho*log(rho).sum()`, where  rho = exp(-r*r/2)/(sqrt(2*pi))
+
+            - 'neglogcauchy': neg log likelihood, using Cauchy distribution
+
+               = -log(1/(pi*(1+r*r))).sum()
+
+            - callable : must take one argument (`r`) and return a float.
+
+        calc_covar : bool, optional
+            Whether to calculate the covariance matrix (default is True) for
+            solvers other than `leastsq` and `least_squares`. Requires the
+            `numdifftools` package to be installed.
+        **kws : dict, optional
+            Options to pass to the minimizer being used.
+
+        Notes
+        -----
+        The objective function should return the value to be minimized. For
+        the Levenberg-Marquardt algorithm from :meth:`leastsq` or
+        :meth:`least_squares`, this returned value must be an array, with a
+        length greater than or equal to the number of fitting variables in
+        the model. For the other methods, the return value can either be a
+        scalar or an array. If an array is returned, the sum of squares of
+        the array will be sent to the underlying fitting method, effectively
+        doing a least-squares optimization of the return values. If the
+        objective function returns non-finite values then a `ValueError`
+        will be raised because the underlying solvers cannot deal with them.
+
+        A common use for the `fcn_args` and `fcn_kws` would be to pass in
+        other data needed to calculate the residual, including such things
+        as the data array, dependent variable, uncertainties in the data,
+        and other data structures for the model calculation.
+
+        """
+        self.userfcn = userfcn
+        self.userargs = fcn_args
+        if self.userargs is None:
+            self.userargs = []
+
+        self.userkws = fcn_kws
+        if self.userkws is None:
+            self.userkws = {}
+        self.kws = kws
+        self.iter_cb = iter_cb
+        self.calc_covar = calc_covar
+        self.scale_covar = scale_covar
+        self.nfev = 0
+        self.nfree = 0
+        self.ndata = 0
+        self.ier = 0
+        self._abort = False
+        self.success = True
+        self.errorbars = False
+        self.message = None
+        self.lmdif_message = None
+        self.chisqr = None
+        self.redchi = None
+        self.covar = None
+        self.residual = None
+        self.reduce_fcn = reduce_fcn
+        self.params = params
+        self.jacfcn = None
+        self.nan_policy = nan_policy
+
+    @property
+    def values(self):
+        """Return Parameter values in a simple dictionary."""
+        return {name: p.value for name, p in self.result.params.items()}
+
+    def __residual(self, fvars, apply_bounds_transformation=True):
+        """Residual function used for least-squares fit.
+
+        With the new, candidate values of `fvars` (the fitting variables),
+        this evaluates all parameters, including setting bounds and
+        evaluating constraints, and then passes those to the user-supplied
+        function to calculate the residual.
+
+        Parameters
+        ----------
+        fvars : numpy.ndarray
+            Array of new parameter values suggested by the minimizer.
+        apply_bounds_transformation : bool, optional
+            Whether to apply lmfits parameter transformation to constrain
+            parameters (default is True). This is needed for solvers without
+            inbuilt support for bounds.
+
+        Returns
+        -------
+        residual : numpy.ndarray
+             The evaluated function values for given `fvars`.
+
+        """
+        params = self.result.params
+
+        if fvars.shape == ():
+            fvars = fvars.reshape((1,))
+
+        if apply_bounds_transformation:
+            for name, val in zip(self.result.var_names, fvars):
+                params[name].value = params[name].from_internal(val)
+        else:
+            for name, val in zip(self.result.var_names, fvars):
+                params[name].value = val
+        params.update_constraints()
+
+        self.result.nfev += 1
+
+        out = self.userfcn(params, *self.userargs, **self.userkws)
+
+        if callable(self.iter_cb):
+            abort = self.iter_cb(params, self.result.nfev, out,
+                                 *self.userargs, **self.userkws)
+            self._abort = self._abort or abort
+
+        if self._abort:
+            self.result.residual = out
+            self.result.aborted = True
+            self.result.message = "Fit aborted by user callback. Could not estimate error-bars."
+            self.result.success = False
+            raise AbortFitException("fit aborted by user.")
+        else:
+            return _nan_policy(np.asarray(out).ravel(),
+                               nan_policy=self.nan_policy)
+
+    def __jacobian(self, fvars):
+        """Return analytical jacobian to be used with Levenberg-Marquardt.
+
+        modified 02-01-2012 by Glenn Jones, Aberystwyth University
+        modified 06-29-2015 M Newville to apply gradient scaling for
+        bounded variables (thanks to JJ Helmus, N Mayorov)
+
+        """
+        pars = self.result.params
+        grad_scale = ones_like(fvars)
+        for ivar, name in enumerate(self.result.var_names):
+            val = fvars[ivar]
+            pars[name].value = pars[name].from_internal(val)
+            grad_scale[ivar] = pars[name].scale_gradient(val)
+
+        pars.update_constraints()
+
+        # compute the jacobian for "internal" unbounded variables,
+        # then rescale for bounded "external" variables.
+        jac = self.jacfcn(pars, *self.userargs, **self.userkws)
+        jac = _nan_policy(jac, nan_policy=self.nan_policy)
+
+        if self.col_deriv:
+            jac = (jac.transpose()*grad_scale).transpose()
+        else:
+            jac *= grad_scale
+        return jac
+
+    def penalty(self, fvars):
+        """Penalty function for scalar minimizers.
+
+        Parameters
+        ----------
+        fvars : numpy.ndarray
+            Array of values for the variable parameters.
+
+        Returns
+        -------
+        r : float
+            The evaluated user-supplied objective function.
+
+            If the objective function is an array of size greater than 1,
+            use the scalar returned by `self.reduce_fcn`.  This defaults
+            to sum-of-squares, but can be replaced by other options.
+
+        """
+        if self.result.method in ['brute', 'shgo', 'dual_annealing']:
+            apply_bounds_transformation = False
+        else:
+            apply_bounds_transformation = True
+
+        r = self.__residual(fvars, apply_bounds_transformation)
+        if isinstance(r, ndarray) and r.size > 1:
+            r = self.reduce_fcn(r)
+            if isinstance(r, ndarray) and r.size > 1:
+                r = r.sum()
+        return r
+
+    def prepare_fit(self, params=None):
+        """Prepare parameters for fitting.
+
+        Prepares and initializes model and Parameters for subsequent
+        fitting. This routine prepares the conversion of :class:`Parameters`
+        into fit variables, organizes parameter bounds, and parses, "compiles"
+        and checks constrain expressions.   The method also creates and returns
+        a new instance of a :class:`MinimizerResult` object that contains the
+        copy of the Parameters that will actually be varied in the fit.
+
+        Parameters
+        ----------
+        params : :class:`~lmfit.parameter.Parameters`, optional
+            Contains the Parameters for the model; if None, then the
+            Parameters used to initialize the Minimizer object are used.
+
+        Returns
+        -------
+        :class:`MinimizerResult`
+
+        Notes
+        -----
+        This method is called directly by the fitting methods, and it is
+        generally not necessary to call this function explicitly.
+
+        .. versionchanged:: 0.9.0
+            Return value changed to :class:`MinimizerResult`.
+
+        """
+        # determine which parameters are actually variables
+        # and which are defined expressions.
+        self.result = MinimizerResult()
+        result = self.result
+        if params is not None:
+            self.params = params
+        if isinstance(self.params, Parameters):
+            result.params = deepcopy(self.params)
+        elif isinstance(self.params, (list, tuple)):
+            result.params = Parameters()
+            for par in self.params:
+                if not isinstance(par, Parameter):
+                    raise MinimizerException(self._err_nonparam)
+                else:
+                    result.params[par.name] = par
+        elif self.params is None:
+            raise MinimizerException(self._err_nonparam)
+
+        # determine which parameters are actually variables
+        # and which are defined expressions.
+        result.var_names = []  # note that this *does* belong to self...
+        result.init_vals = []
+        result.params.update_constraints()
+        result.nfev = 0
+        result.errorbars = False
+        result.aborted = False
+        for name, par in self.result.params.items():
+            par.stderr = None
+            par.correl = None
+            if par.expr is not None:
+                par.vary = False
+            if par.vary:
+                result.var_names.append(name)
+                result.init_vals.append(par.setup_bounds())
+
+            par.init_value = par.value
+            if par.name is None:
+                par.name = name
+        result.nvarys = len(result.var_names)
+        result.init_values = {n: v for n, v in zip(result.var_names,
+                                                   result.init_vals)}
+
+        # set up reduce function for scalar minimizers
+        #    1. user supplied callable
+        #    2. string starting with 'neglogc' or 'negent'
+        #    3. sum of squares
+        if not callable(self.reduce_fcn):
+            if isinstance(self.reduce_fcn, str):
+                if self.reduce_fcn.lower().startswith('neglogc'):
+                    self.reduce_fcn = reduce_cauchylogpdf
+                elif self.reduce_fcn.lower().startswith('negent'):
+                    self.reduce_fcn = reduce_negentropy
+            if self.reduce_fcn is None:
+                self.reduce_fcn = reduce_chisquare
+        return result
+
+    def unprepare_fit(self):
+        """Clean fit state, so that subsequent fits need to call prepare_fit().
+
+        removes AST compilations of constraint expressions.
+
+        """
+        pass
+
+    def _calculate_covariance_matrix(self, fvars):
+        """Calculate the covariance matrix.
+
+        The `numdiftoools` package is used to estimate the Hessian matrix, and
+        the covariance matrix is calculated as:
+
+        .. math::
+
+            cov_x = inverse(Hessian) * 2.0
+
+        Parameters
+        ----------
+        fvars : numpy.ndarray
+            Array of the optimal internal, freely variable parameters.
+
+        Returns
+        -------
+        cov_x : numpy.ndarray or None
+            Covariance matrix if successful, otherwise None.
+
+        """
+        warnings.filterwarnings(action="ignore", module="scipy",
+                                message="^internal gelsd")
+
+        nfev = deepcopy(self.result.nfev)
+        try:
+            Hfun = ndt.Hessian(self.penalty)
+            hessian_ndt = Hfun(fvars)
+            cov_x = inv(hessian_ndt) * 2.0
+        except (LinAlgError, ValueError):
+            return None
+        finally:
+            self.result.nfev = nfev
+
+        return cov_x
+
+    def _int2ext_cov_x(self, cov_int, fvars):
+        """Transform covariance matrix to external parameter space.
+
+        It makes use of the gradient scaling according to the MINUIT recipe:
+
+            cov_ext = np.dot(grad.T, grad) * cov_int
+
+        Parameters
+        ----------
+        cov_int : numpy.ndarray
+            Covariance matrix in the internal parameter space.
+        fvars : numpy.ndarray
+            Array of the optimal internal, freely variable, parameter values.
+
+        Returns
+        -------
+        cov_ext : numpy.ndarray
+            Covariance matrix, transformed to external parameter space.
+
+        """
+        g = [self.result.params[name].scale_gradient(fvars[i]) for i, name in
+             enumerate(self.result.var_names)]
+        grad2d = np.atleast_2d(g)
+        grad = np.dot(grad2d.T, grad2d)
+
+        cov_ext = cov_int * grad
+        return cov_ext
+
+    def _calculate_uncertainties_correlations(self):
+        """Calculate parameter uncertainties and correlations."""
+        self.result.errorbars = True
+
+        if self.scale_covar:
+            self.result.covar *= self.result.redchi
+
+        vbest = np.atleast_1d([self.result.params[name].value for name in
+                               self.result.var_names])
+
+        has_expr = False
+        for par in self.result.params.values():
+            par.stderr, par.correl = 0, None
+            has_expr = has_expr or par.expr is not None
+
+        for ivar, name in enumerate(self.result.var_names):
+            par = self.result.params[name]
+            par.stderr = sqrt(self.result.covar[ivar, ivar])
+            par.correl = {}
+            try:
+                self.result.errorbars = self.result.errorbars and (par.stderr > 0.0)
+                for jvar, varn2 in enumerate(self.result.var_names):
+                    if jvar != ivar:
+                        par.correl[varn2] = (self.result.covar[ivar, jvar] /
+                                             (par.stderr * sqrt(self.result.covar[jvar, jvar])))
+            except ZeroDivisionError:
+                self.result.errorbars = False
+
+        if has_expr:
+            try:
+                uvars = uncertainties.correlated_values(vbest, self.result.covar)
+            except (LinAlgError, ValueError):
+                uvars = None
+
+            # for uncertainties on constrained parameters, use the calculated
+            # "correlated_values", evaluate the uncertainties on the constrained
+            # parameters and reset the Parameters to best-fit value
+            if uvars is not None:
+                for par in self.result.params.values():
+                    eval_stderr(par, uvars, self.result.var_names, self.result.params)
+                # restore nominal values
+                for v, nam in zip(uvars, self.result.var_names):
+                    self.result.params[nam].value = v.nominal_value
+
+    def scalar_minimize(self, method='Nelder-Mead', params=None, **kws):
+        """Scalar minimization using :scipydoc:`optimize.minimize`.
+
+        Perform fit with any of the scalar minimization algorithms supported by
+        :scipydoc:`optimize.minimize`. Default argument values are:
+
+        +-------------------------+-----------------+-----------------------------------------------------+
+        | :meth:`scalar_minimize` | Default Value   | Description                                         |
+        | arg                     |                 |                                                     |
+        +=========================+=================+=====================================================+
+        |   method                | ``Nelder-Mead`` | fitting method                                      |
+        +-------------------------+-----------------+-----------------------------------------------------+
+        |   tol                   | 1.e-7           | fitting and parameter tolerance                     |
+        +-------------------------+-----------------+-----------------------------------------------------+
+        |   hess                  | None            | Hessian of objective function                       |
+        +-------------------------+-----------------+-----------------------------------------------------+
+
+
+        Parameters
+        ----------
+        method : str, optional
+            Name of the fitting method to use. One of:
+
+            - 'Nelder-Mead' (default)
+            - 'L-BFGS-B'
+            - 'Powell'
+            - 'CG'
+            - 'Newton-CG'
+            - 'COBYLA'
+            - 'BFGS'
+            - 'TNC'
+            - 'trust-ncg'
+            - 'trust-exact'
+            - 'trust-krylov'
+            - 'trust-constr'
+            - 'dogleg'
+            - 'SLSQP'
+            - 'differential_evolution'
+
+        params : :class:`~lmfit.parameter.Parameters`, optional
+           Parameters to use as starting point.
+        **kws : dict, optional
+            Minimizer options pass to :scipydoc:`optimize.minimize`.
+
+        Returns
+        -------
+        :class:`MinimizerResult`
+            Object containing the optimized parameter and several
+            goodness-of-fit statistics.
+
+
+        .. versionchanged:: 0.9.0
+           Return value changed to :class:`MinimizerResult`.
+
+        Notes
+        -----
+        If the objective function returns a NumPy array instead
+        of the expected scalar, the sum of squares of the array
+        will be used.
+
+        Note that bounds and constraints can be set on Parameters
+        for any of these methods, so are not supported separately
+        for those designed to use bounds. However, if you use the
+        differential_evolution method you must specify finite
+        (min, max) for each varying Parameter.
+
+        """
+        result = self.prepare_fit(params=params)
+        result.method = method
+        variables = result.init_vals
+        params = result.params
+
+        fmin_kws = dict(method=method,
+                        options={'maxiter': 1000 * (len(variables) + 1)})
+        fmin_kws.update(self.kws)
+        fmin_kws.update(kws)
+
+        # hess supported only in some methods
+        if 'hess' in fmin_kws and method not in ('Newton-CG', 'dogleg',
+                                                 'trust-constr', 'trust-ncg',
+                                                 'trust-krylov', 'trust-exact'):
+            fmin_kws.pop('hess')
+
+        # jac supported only in some methods (and Dfun could be used...)
+        if 'jac' not in fmin_kws and fmin_kws.get('Dfun', None) is not None:
+            self.jacfcn = fmin_kws.pop('jac')
+            fmin_kws['jac'] = self.__jacobian
+
+        if 'jac' in fmin_kws and method not in ('CG', 'BFGS', 'Newton-CG',
+                                                'L-BFGS-B', 'TNC', 'SLSQP',
+                                                'dogleg', 'trust-ncg',
+                                                'trust-krylov', 'trust-exact'):
+            self.jacfcn = None
+            fmin_kws.pop('jac')
+
+        # workers / updating keywords only supported in differential_evolution
+        for kwd in ('workers', 'updating'):
+            if kwd in fmin_kws and method != 'differential_evolution':
+                fmin_kws.pop(kwd)
+
+        if method == 'differential_evolution':
+            for par in params.values():
+                if (par.vary and
+                        not (np.isfinite(par.min) and np.isfinite(par.max))):
+                    raise ValueError('differential_evolution requires finite '
+                                     'bound for all varying parameters')
+
+            _bounds = [(-np.pi / 2., np.pi / 2.)] * len(variables)
+            kwargs = dict(args=(), strategy='best1bin', maxiter=None,
+                          popsize=15, tol=0.01, mutation=(0.5, 1),
+                          recombination=0.7, seed=None, callback=None,
+                          disp=False, polish=True, init='latinhypercube',
+                          atol=0, updating='immediate', workers=1)
+
+            for k, v in fmin_kws.items():
+                if k in kwargs:
+                    kwargs[k] = v
+
+            try:
+                ret = differential_evolution(self.penalty, _bounds, **kwargs)
+            except AbortFitException:
+                pass
+
+        else:
+            try:
+                ret = scipy_minimize(self.penalty, variables, **fmin_kws)
+            except AbortFitException:
+                pass
+
+        if not result.aborted:
+            if isinstance(ret, dict):
+                for attr, value in ret.items():
+                    setattr(result, attr, value)
+            else:
+                for attr in dir(ret):
+                    if not attr.startswith('_'):
+                        setattr(result, attr, getattr(ret, attr))
+
+            result.x = np.atleast_1d(result.x)
+            result.residual = self.__residual(result.x)
+            result.nfev -= 1
+
+        result._calculate_statistics()
+
+        # calculate the cov_x and estimate uncertanties/correlations
+        if (not result.aborted and self.calc_covar and HAS_NUMDIFFTOOLS and
+                len(result.residual) > len(result.var_names)):
+            _covar_ndt = self._calculate_covariance_matrix(result.x)
+            if _covar_ndt is not None:
+                result.covar = self._int2ext_cov_x(_covar_ndt, result.x)
+                self._calculate_uncertainties_correlations()
+
+        return result
+
+    def _lnprob(self, theta, userfcn, params, var_names, bounds, userargs=(),
+                userkws=None, float_behavior='posterior', is_weighted=True,
+                nan_policy='raise'):
+        """Calculate the log-posterior probability.
+
+        See the `Minimizer.emcee` method for more details.
+
+        Parameters
+        ----------
+        theta : sequence
+            Float parameter values (only those being varied).
+        userfcn : callable
+            User objective function.
+        params : :class:`~lmfit.parameters.Parameters`
+            The entire set of Parameters.
+        var_names : list
+            The names of the parameters that are varying.
+        bounds : numpy.ndarray
+            Lower and upper bounds of parameters. Has shape (nvarys, 2).
+        userargs : tuple, optional
+            Extra positional arguments required for user objective function.
+        userkws : dict, optional
+            Extra keyword arguments required for user objective function.
+        float_behavior : str, optional
+            Specifies meaning of objective when it returns a float. Use
+            'posterior' if objective function returnins a log-posterior
+            probability (default) or 'chi2' if it returns a chi2 value.
+        is_weighted : bool, optional
+            If `userfcn` returns a vector of residuals then `is_weighted`
+            (default is True) specifies if the residuals have been weighted by
+            data uncertainties.
+        nan_policy : str, optional
+            Specifies action if `userfcn` returns NaN values. Use 'raise'
+            (default) to raise a `ValueError`, 'propagate' to use values as-is,
+            or 'omit' to filter out the non-finite values.
+
+        Returns
+        -------
+        lnprob : float
+            Log posterior probability.
+
+        """
+        # the comparison has to be done on theta and bounds. DO NOT inject theta
+        # values into Parameters, then compare Parameters values to the bounds.
+        # Parameters values are clipped to stay within bounds.
+        if np.any(theta > bounds[:, 1]) or np.any(theta < bounds[:, 0]):
+            return -np.inf
+        for name, val in zip(var_names, theta):
+            params[name].value = val
+        userkwargs = {}
+        if userkws is not None:
+            userkwargs = userkws
+        # update the constraints
+        params.update_constraints()
+        # now calculate the log-likelihood
+        out = userfcn(params, *userargs, **userkwargs)
+        self.result.nfev += 1
+        if callable(self.iter_cb):
+            abort = self.iter_cb(params, self.result.nfev, out,
+                                 *userargs, **userkwargs)
+            self._abort = self._abort or abort
+        if self._abort:
+            self.result.residual = out
+            self._lastpos = theta
+            raise AbortFitException("fit aborted by user.")
+        else:
+            out = _nan_policy(np.asarray(out).ravel(),
+                              nan_policy=self.nan_policy)
+        lnprob = np.asarray(out).ravel()
+        if len(lnprob) == 0:
+            lnprob = np.array([-1.e100])
+        if lnprob.size > 1:
+            # objective function returns a vector of residuals
+            if '__lnsigma' in params and not is_weighted:
+                # marginalise over a constant data uncertainty
+                __lnsigma = params['__lnsigma'].value
+                c = np.log(2 * np.pi) + 2 * __lnsigma
+                lnprob = -0.5 * np.sum((lnprob / np.exp(__lnsigma)) ** 2 + c)
+            else:
+                lnprob = -0.5 * (lnprob * lnprob).sum()
+        else:
+            # objective function returns a single value.
+            # use float_behaviour to figure out if the value is posterior or chi2
+            if float_behavior == 'posterior':
+                pass
+            elif float_behavior == 'chi2':
+                lnprob *= -0.5
+            else:
+                raise ValueError("float_behaviour must be either 'posterior' "
+                                 "or 'chi2' " + float_behavior)
+        return lnprob
+
+    def emcee(self, params=None, steps=1000, nwalkers=100, burn=0, thin=1,
+              ntemps=1, pos=None, reuse_sampler=False, workers=1,
+              float_behavior='posterior', is_weighted=True, seed=None,
+              progress=True,
+              check_converge=True, nevery=1000):
+        """Bayesian sampling of the posterior distribution using the `emcee`
+        Markov Chain Monte Carlo package.
+
+        The method assumes that the prior is Uniform. You need to have `emcee`
+        version 3 installed to use this method.
+
+        Parameters
+        ----------
+        params : :class:`~lmfit.parameter.Parameters`, optional
+            Parameters to use as starting point. If this is not specified then
+            the Parameters used to initialize the Minimizer object are used.
+        steps : int, optional
+            How many samples you would like to draw from the posterior
+            distribution for each of the walkers?
+        nwalkers : int, optional
+            Should be set so :math:`nwalkers >> nvarys`, where `nvarys` are
+            the number of parameters being varied during the fit.
+            'Walkers are the members of the ensemble. They are almost like
+            separate Metropolis-Hastings chains but, of course, the proposal
+            distribution for a given walker depends on the positions of all
+            the other walkers in the ensemble.' - from the `emcee` webpage.
+        burn : int, optional
+            Discard this many samples from the start of the sampling regime.
+        thin : int, optional
+            Only accept 1 in every `thin` samples.
+        ntemps : int, deprecated
+            ntemps has no effect.
+        pos : numpy.ndarray, optional
+            Specify the initial positions for the sampler, an ndarray of shape
+            `(nwalkers, nvarys)`.  You can also initialise using a previous
+            chain of the same `nwalkers` and `nvarys`. Note that `nvarys` may
+            be one larger than you expect it to be if your `userfcn` returns
+            an array and `is_weighted` is `False`.
+        reuse_sampler : bool, optional
+            Set to `True` if you have already run `emcee` with the `Minimizer`
+            instance and want to continue to draw from its ``sampler`` (and so
+            retain the chain history). If `False`,  a new sampler is created.
+            The keywords `nwalkers`, `pos`, and `params` will be ignored when
+            this is set, as they will be set by the existing sampler.
+            **Important**: the Parameters used to create the sampler must not
+            change in-between calls to `emcee`. Alteration of Parameters
+            would include changed ``min``, ``max``, ``vary`` and ``expr``
+            attributes. This may happen, for example, if you use an altered
+            Parameters object and call the `minimize` method in-between calls
+            to `emcee`.
+        workers : Pool-like or int, optional
+            For parallelization of sampling.  It can be any Pool-like object
+            with a map method that follows the same calling sequence as the
+            built-in `map` function. If int is given as the argument, then a
+            multiprocessing-based pool is spawned internally with the
+            corresponding number of parallel processes. 'mpi4py'-based
+            parallelization and 'joblib'-based parallelization pools can also
+            be used here. **Note**: because of multiprocessing overhead it may
+            only be worth parallelising if the objective function is expensive
+            to calculate, or if there are a large number of objective
+            evaluations per step (`nwalkers * nvarys`).
+        float_behavior : str, optional
+            Meaning of float (scalar) output of objective function. Use
+            'posterior' if it returns a log-posterior probability or 'chi2'
+            if it returns :math:`\\chi^2`. See Notes for further details.
+        is_weighted : bool, optional
+            Has your objective function been weighted by measurement
+            uncertainties? If `is_weighted is True` then your objective
+            function is assumed to return residuals that have been divided by
+            the true measurement uncertainty `(data - model) / sigma`. If
+            `is_weighted is False` then the objective function is assumed to
+            return unweighted residuals, `data - model`. In this case `emcee`
+            will employ a positive measurement uncertainty during the sampling.
+            This measurement uncertainty will be present in the output params
+            and output chain with the name `__lnsigma`. A side effect of this
+            is that you cannot use this parameter name yourself.
+            **Important** this parameter only has any effect if your objective
+            function returns an array. If your objective function returns a
+            float, then this parameter is ignored. See Notes for more details.
+        seed : int or `numpy.random.RandomState`, optional
+            If `seed` is an int, a new `numpy.random.RandomState` instance is
+            used, seeded with `seed`.
+            If `seed` is already a `numpy.random.RandomState` instance, then
+            that `numpy.random.RandomState` instance is used.
+            Specify `seed` for repeatable minimizations.
+        progress : bool, optional
+            Print a progress bar to the console while running.
+
+        Returns
+        -------
+        :class:`MinimizerResult`
+            MinimizerResult object containing updated params, statistics,
+            etc. The updated params represent the median of the samples,
+            while the uncertainties are half the difference of the 15.87
+            and 84.13 percentiles.  The `MinimizerResult` contains a few
+            additional attributes: ``chain`` contain the samples and has
+            shape `((steps - burn) // thin, nwalkers, nvarys)`.
+            ``flatchain`` is a `pandas.DataFrame` of the flattened chain,
+            that can be accessed with `result.flatchain[parname]`.
+            ``lnprob`` contains the log probability for each sample in
+            ``chain``.  The sample with the highest probability corresponds
+            to the maximum likelihood estimate.  ``acor`` is an array
+            containing the autocorrelation time for each parameter if the
+            autocorrelation time can be computed from the chain. Finally,
+            ``acceptance_fraction`` (an array of the fraction of steps
+            accepted for each walker).
+
+        Notes
+        -----
+        This method samples the posterior distribution of the parameters
+        using Markov Chain Monte Carlo.  It calculates the log-posterior
+        probability of the model parameters, `F`, given the data, `D`,
+        :math:`\\ln p(F_{true} | D)`. This 'posterior probability' is given by:
+
+        .. math::
+
+            \\ln p(F_{true} | D) \\propto \\ln p(D | F_{true}) + \\ln p(F_{true})
+
+        where :math:`\\ln p(D | F_{true})` is the 'log-likelihood' and
+        :math:`\\ln p(F_{true})` is the 'log-prior'. The default log-prior
+        encodes prior information known about the model that the log-prior
+        probability is `-numpy.inf` (impossible) if any of the parameters
+        is outside its limits, and is zero if all the parameters are inside
+        their bounds (uniform prior). The log-likelihood function is [1]_:
+
+        .. math::
+
+            \\ln p(D|F_{true}) = -\\frac{1}{2}\\sum_n \\left[\\frac{(g_n(F_{true}) - D_n)^2}{s_n^2}+\\ln (2\\pi s_n^2)\\right]
+
+        The first term represents the residual (:math:`g` being the generative
+        model, :math:`D_n` the data and :math:`s_n` the measurement
+        uncertainty). This gives :math:`\\chi^2` when summed over all data
+        points.  The objective function may also return the log-posterior
+        probability, :math:`\\ln p(F_{true} | D)`.  Since the default
+        log-prior term is zero, the objective function can also just return
+        the log-likelihood, unless you wish to create a non-uniform prior.
+
+        If the objective function returns a float value, this is assumed by
+        default to be the log-posterior probability, (`float_behavior` default
+        is 'posterior'. If your objective function returns :math:`\\chi^2`,
+        then you should use a value of `float_behavior='chi2'`.
+
+        By default objective functions may return an ndarray of (possibly
+        weighted) residuals. In this case, use `is_weighted` to select whether
+        these are correctly weighted by measurement uncertainty.  Note that
+        this ignores the second term above, so that to calculate a correct
+        log-posterior probability value your objective function should return
+        a float value.  With `is_weighted=False` the data uncertainty, `s_n`,
+        will be treated as a nuisance parameter to be marginalized out. This
+        uses strictly positive uncertainty (homoscedasticity) for each data
+        point, :math:`s_n = \\exp(\\rm{\\_\\_lnsigma})`.  `__lnsigma` will be
+        present in `MinimizerResult.params`, as well as `Minimizer.chain` and
+        `nvarys` will be increased by one.
+
+        References
+        ----------
+        .. [1] https://emcee.readthedocs.io
+
+        """
+        if not HAS_EMCEE:
+            raise NotImplementedError('emcee version 3 is required.')
+
+        if ntemps > 1:
+            msg = ("'ntemps' has no effect anymore, since the PTSampler was "
+                   "removed from emcee version 3.")
+            raise DeprecationWarning(msg)
+
+        tparams = params
+        # if you're reusing the sampler then nwalkers have to be
+        # determined from the previous sampling
+        if reuse_sampler:
+            if not hasattr(self, 'sampler') or not hasattr(self, '_lastpos'):
+                raise ValueError("You wanted to use an existing sampler, but "
+                                 "it hasn't been created yet")
+            if len(self._lastpos.shape) == 2:
+                nwalkers = self._lastpos.shape[0]
+            elif len(self._lastpos.shape) == 3:
+                nwalkers = self._lastpos.shape[1]
+            tparams = None
+
+        result = self.prepare_fit(params=tparams)
+        params = result.params
+
+        # check if the userfcn returns a vector of residuals
+        out = self.userfcn(params, *self.userargs, **self.userkws)
+        out = np.asarray(out).ravel()
+        if out.size > 1 and is_weighted is False:
+            # we need to marginalise over a constant data uncertainty
+            if '__lnsigma' not in params:
+                # __lnsigma should already be in params if is_weighted was
+                # previously set to True.
+                params.add('__lnsigma', value=0.01, min=-np.inf, max=np.inf,
+                           vary=True)
+                # have to re-prepare the fit
+                result = self.prepare_fit(params)
+                params = result.params
+
+        result.method = 'emcee'
+
+        # Removing internal parameter scaling. We could possibly keep it,
+        # but I don't know how this affects the emcee sampling.
+        bounds = []
+        var_arr = np.zeros(len(result.var_names))
+        i = 0
+        for par in params:
+            param = params[par]
+            if param.expr is not None:
+                param.vary = False
+            if param.vary:
+                var_arr[i] = param.value
+                i += 1
+            else:
+                # don't want to append bounds if they're not being varied.
+                continue
+
+            param.from_internal = lambda val: val
+            lb, ub = param.min, param.max
+            if lb is None or lb is np.nan:
+                lb = -np.inf
+            if ub is None or ub is np.nan:
+                ub = np.inf
+            bounds.append((lb, ub))
+        bounds = np.array(bounds)
+
+        self.nvarys = len(result.var_names)
+
+        # set up multiprocessing options for the samplers
+        auto_pool = None
+        sampler_kwargs = {}
+        if isinstance(workers, int) and workers > 1 and HAS_DILL:
+            auto_pool = multiprocessing.Pool(workers)
+            sampler_kwargs['pool'] = auto_pool
+        elif hasattr(workers, 'map'):
+            sampler_kwargs['pool'] = workers
+
+        # function arguments for the log-probability functions
+        # these values are sent to the log-probability functions by the sampler.
+        lnprob_args = (self.userfcn, params, result.var_names, bounds)
+        lnprob_kwargs = {'is_weighted': is_weighted,
+                         'float_behavior': float_behavior,
+                         'userargs': self.userargs,
+                         'userkws': self.userkws,
+                         'nan_policy': self.nan_policy}
+
+        sampler_kwargs['args'] = lnprob_args
+        sampler_kwargs['kwargs'] = lnprob_kwargs
+
+        # TM added:
+        #sampler_kwargs['skip_initial_state_check'] = True 
+
+        # set up the random number generator
+        rng = _make_random_gen(seed)
+
+        # now initialise the samplers
+        if reuse_sampler:
+            if auto_pool is not None:
+                self.sampler.pool = auto_pool
+
+            p0 = self._lastpos
+            if p0.shape[-1] != self.nvarys:
+                raise ValueError("You cannot reuse the sampler if the number "
+                                 "of varying parameters has changed")
+
+        else:
+            p0 = 1 + rng.randn(nwalkers, self.nvarys) * 1.e-4
+            p0 *= var_arr
+            sampler_kwargs['pool'] = auto_pool
+            self.sampler = emcee.EnsembleSampler(nwalkers, self.nvarys,
+                                                 self._lnprob, **sampler_kwargs)
+
+        # user supplies an initialisation position for the chain
+        # If you try to run the sampler with p0 of a wrong size then you'll get
+        # a ValueError. Note, you can't initialise with a position if you are
+        # reusing the sampler.
+        if pos is not None and not reuse_sampler:
+            tpos = np.asfarray(pos)
+            if p0.shape == tpos.shape:
+                pass
+            # trying to initialise with a previous chain
+            elif tpos.shape[-1] == self.nvarys:
+                tpos = tpos[-1]
+            else:
+                raise ValueError('pos should have shape (nwalkers, nvarys)')
+            p0 = tpos
+
+        # if you specified a seed then you also need to seed the sampler
+        if seed is not None:
+            self.sampler.random_state = rng.get_state()
+
+        # now do a production run, sampling all the time
+        try:
+            if check_converge: # TM; 2021.Jan.13
+                # Now we'll sample for up to max_n steps
+                index = 0
+                old_tau = np.inf
+                autocorr = np.empty(steps)
+
+                for sample in self.sampler.sample(p0, iterations=steps, progress=True):
+                    # Only check convergence every 100 steps
+                    if self.sampler.iteration % nevery:
+                        continue
+
+                    # Compute the autocorrelation time so far
+                    # Using tol=0 means that we'll always get an estimate even
+                    # if it isn't trustworthy
+                    tau = self.sampler.get_autocorr_time(tol=0)
+                    autocorr[index] = np.mean(tau)
+                    index += 1
+
+                    # Check convergence
+                    converged = np.all(tau * 100 < self.sampler.iteration)
+                    converged &= np.all(np.abs(old_tau - tau) / tau < 0.01)
+                    if converged:
+                        #print('Converged at %d'%index*nevery)
+                        burn = int(index*nevery / 50) # Burn 50%
+                        result.burn = burn
+                        result.steps = index*nevery
+                        break
+                    old_tau = tau
+
+            else:
+                output = self.sampler.run_mcmc(p0, steps, progress=progress)
+                self._lastpos = output.coords
+
+        except AbortFitException:
+            result.aborted = True
+            result.message = "Fit aborted by user callback. Could not estimate error-bars."
+            result.success = False
+            result.nfev = self.result.nfev
+            output = None
+
+        # discard the burn samples and thin
+        chain = self.sampler.get_chain(thin=thin, discard=burn)[..., :, :]
+        lnprobability = self.sampler.get_log_prob(thin=thin, discard=burn)[..., :]
+        flatchain = chain.reshape((-1, self.nvarys))
+        if not result.aborted:
+            quantiles = np.percentile(flatchain, [15.87, 50, 84.13], axis=0)
+
+            for i, var_name in enumerate(result.var_names):
+                std_l, median, std_u = quantiles[:, i]
+                params[var_name].value = median
+                params[var_name].stderr = 0.5 * (std_u - std_l)
+                params[var_name].correl = {}
+
+            params.update_constraints()
+
+            # work out correlation coefficients
+            corrcoefs = np.corrcoef(flatchain.T)
+
+            for i, var_name in enumerate(result.var_names):
+                for j, var_name2 in enumerate(result.var_names):
+                    if i != j:
+                        result.params[var_name].correl[var_name2] = corrcoefs[i, j]
+
+        result.chain = np.copy(chain)
+        result.lnprob = np.copy(lnprobability)
+        result.errorbars = True
+        result.nvarys = len(result.var_names)
+        result.nfev = nwalkers*steps
+        try:
+            result.acor = self.sampler.get_autocorr_time()
+        except AutocorrError as e:
+            print(str(e))
+            pass
+        result.acceptance_fraction = self.sampler.acceptance_fraction
+
+        # Calculate the residual with the "best fit" parameters
+        out = self.userfcn(params, *self.userargs, **self.userkws)
+        result.residual = _nan_policy(out, nan_policy=self.nan_policy,
+                                      handle_inf=False)
+
+        # If uncertainty was automatically estimated, weight the residual properly
+        if (not is_weighted) and (result.residual.size > 1):
+            if '__lnsigma' in params:
+                result.residual = result.residual/np.exp(params['__lnsigma'].value)
+
+        # Calculate statistics for the two standard cases:
+        if isinstance(result.residual, ndarray) or (float_behavior == 'chi2'):
+            result._calculate_statistics()
+
+        # Handle special case unique to emcee:
+        # This should eventually be moved into result._calculate_statistics.
+        elif float_behavior == 'posterior':
+            result.ndata = 1
+            result.nfree = 1
+
+            # assuming prior prob = 1, this is true
+            _neg2_log_likel = -2*result.residual
+
+            # assumes that residual is properly weighted, avoid overflowing np.exp()
+            result.chisqr = np.exp(min(650, _neg2_log_likel))
+
+            result.redchi = result.chisqr / result.nfree
+            result.aic = _neg2_log_likel + 2 * result.nvarys
+            result.bic = _neg2_log_likel + np.log(result.ndata) * result.nvarys
+
+        if auto_pool is not None:
+            auto_pool.terminate()
+
+        return result
+
+    def least_squares(self, params=None, **kws):
+        """Least-squares minimization using :scipydoc:`optimize.least_squares`.
+
+        This method wraps :scipydoc:`optimize.least_squares`, which has inbuilt
+        support for bounds and robust loss functions. By default it uses the
+        Trust Region Reflective algorithm with a linear loss function (i.e.,
+        the standard least-squares problem).
+
+        Parameters
+        ----------
+        params : :class:`~lmfit.parameter.Parameters`, optional
+           Parameters to use as starting point.
+        **kws : dict, optional
+            Minimizer options to pass to :scipydoc:`optimize.least_squares`.
+
+        Returns
+        -------
+        :class:`MinimizerResult`
+            Object containing the optimized parameter and several
+            goodness-of-fit statistics.
+
+
+        .. versionchanged:: 0.9.0
+           Return value changed to :class:`MinimizerResult`.
+
+        """
+        result = self.prepare_fit(params)
+        result.method = 'least_squares'
+
+        replace_none = lambda x, sign: sign*np.inf if x is None else x
+
+        start_vals, lower_bounds, upper_bounds = [], [], []
+        for vname in result.var_names:
+            par = self.params[vname]
+            start_vals.append(par.value)
+            lower_bounds.append(replace_none(par.min, -1))
+            upper_bounds.append(replace_none(par.max, 1))
+
+        try:
+            ret = least_squares(self.__residual, start_vals,
+                                bounds=(lower_bounds, upper_bounds),
+                                kwargs=dict(apply_bounds_transformation=False),
+                                **kws)
+            result.residual = ret.fun
+        except AbortFitException:
+            pass
+
+        # note: upstream least_squares is actually returning
+        # "last evaluation", not "best result", but we do this
+        # here for consistency, and assuming it will be fixed.
+        if not result.aborted:
+            result.residual = self.__residual(ret.x, False)
+            result.nfev -= 1
+        result._calculate_statistics()
+
+        if not result.aborted:
+            for attr in ret:
+                setattr(result, attr, ret[attr])
+
+            result.x = np.atleast_1d(result.x)
+
+            # calculate the cov_x and estimate uncertainties/correlations
+            try:
+                if issparse(ret.jac):
+                    hess = (ret.jac.T * ret.jac).toarray()
+                elif isinstance(ret.jac, LinearOperator):
+                    identity = np.eye(ret.jac.shape[1], dtype=ret.jac.dtype)
+                    # TODO: Remove try-except when scipy < 1.4.0 support dropped
+                    try:
+                        # For scipy >= 1.4.0 (with Linear Operator transpose)
+                        # https://github.com/scipy/scipy/pull/9064
+                        hess = (ret.jac.T * ret.jac) * identity
+                    except AttributeError:
+                        # For scipy < 1.4.0 (without Linear Operator transpose)
+                        jac = ret.jac * identity
+                        hess = np.matmul(jac.T, jac)
+                else:
+                    hess = np.matmul(ret.jac.T, ret.jac)
+                result.covar = np.linalg.inv(hess)
+                self._calculate_uncertainties_correlations()
+            except LinAlgError:
+                pass
+
+        return result
+
+    def leastsq(self, params=None, **kws):
+        """Use Levenberg-Marquardt minimization to perform a fit.
+
+        It assumes that the input Parameters have been initialized, and
+        a function to minimize has been properly set up.
+        When possible, this calculates the estimated uncertainties and
+        variable correlations from the covariance matrix.
+
+        This method calls :scipydoc:`optimize.leastsq`.
+        By default, numerical derivatives are used, and the following
+        arguments are set:
+
+        +------------------+----------------+------------------------------------------------------------+
+        | :meth:`leastsq`  |  Default Value | Description                                                |
+        | arg              |                |                                                            |
+        +==================+================+============================================================+
+        |   xtol           |  1.e-7         | Relative error in the approximate solution                 |
+        +------------------+----------------+------------------------------------------------------------+
+        |   ftol           |  1.e-7         | Relative error in the desired sum of squares               |
+        +------------------+----------------+------------------------------------------------------------+
+        |   maxfev         | 2000*(nvar+1)  | Maximum number of function calls (nvar= # of variables)    |
+        +------------------+----------------+------------------------------------------------------------+
+        |   Dfun           | None           | Function to call for Jacobian calculation                  |
+        +------------------+----------------+------------------------------------------------------------+
+
+        Parameters
+        ----------
+        params : :class:`~lmfit.parameter.Parameters`, optional
+           Parameters to use as starting point.
+        **kws : dict, optional
+            Minimizer options to pass to :scipydoc:`optimize.leastsq`.
+
+        Returns
+        -------
+        :class:`MinimizerResult`
+            Object containing the optimized parameter
+            and several goodness-of-fit statistics.
+
+
+        .. versionchanged:: 0.9.0
+           Return value changed to :class:`MinimizerResult`.
+
+        """
+        result = self.prepare_fit(params=params)
+        result.method = 'leastsq'
+        result.nfev -= 2  # correct for "pre-fit" initialization/checks
+        variables = result.init_vals
+        nvars = len(variables)
+        lskws = dict(full_output=1, xtol=1.e-7, ftol=1.e-7, col_deriv=False,
+                     gtol=1.e-7, maxfev=2000*(nvars+1), Dfun=None)
+
+        lskws.update(self.kws)
+        lskws.update(kws)
+
+        self.col_deriv = False
+        if lskws['Dfun'] is not None:
+            self.jacfcn = lskws['Dfun']
+            self.col_deriv = lskws['col_deriv']
+            lskws['Dfun'] = self.__jacobian
+
+        # suppress runtime warnings during fit and error analysis
+        orig_warn_settings = np.geterr()
+        np.seterr(all='ignore')
+
+        try:
+            lsout = scipy_leastsq(self.__residual, variables, **lskws)
+        except AbortFitException:
+            pass
+
+        if not result.aborted:
+            _best, _cov, infodict, errmsg, ier = lsout
+            result.residual = self.__residual(_best)
+            result.nfev -= 1
+        result._calculate_statistics()
+
+        if result.aborted:
+            return result
+
+        result.ier = ier
+        result.lmdif_message = errmsg
+        result.success = ier in [1, 2, 3, 4]
+        if ier in {1, 2, 3}:
+            result.message = 'Fit succeeded.'
+        elif ier == 0:
+            result.message = ('Invalid Input Parameters. I.e. more variables '
+                              'than data points given, tolerance < 0.0, or '
+                              'no data provided.')
+        elif ier == 4:
+            result.message = 'One or more variable did not affect the fit.'
+        elif ier == 5:
+            result.message = self._err_maxfev % lskws['maxfev']
+        else:
+            result.message = 'Tolerance seems to be too small.'
+
+        # self.errorbars = error bars were successfully estimated
+        result.errorbars = (_cov is not None)
+        if result.errorbars:
+            # transform the covariance matrix to "external" parameter space
+            result.covar = self._int2ext_cov_x(_cov, _best)
+            # calculate parameter uncertainties and correlations
+            self._calculate_uncertainties_correlations()
+        else:
+            result.message = '%s Could not estimate error-bars.' % result.message
+
+        np.seterr(**orig_warn_settings)
+
+        return result
+
+    def basinhopping(self, params=None, **kws):
+        """Use the `basinhopping` algorithm to find the global minimum of a function.
+
+        This method calls :scipydoc:`optimize.basinhopping` using the default
+        arguments. The default minimizer is `BFGS`, but since lmfit supports
+        parameter bounds for all minimizers, the user can choose any of the
+        solvers present in :scipydoc:`optimize.minimize`.
+
+        Parameters
+        ----------
+        params : :class:`~lmfit.parameter.Parameters` object, optional
+            Contains the Parameters for the model. If None, then the
+            Parameters used to initialize the Minimizer object are used.
+
+        Returns
+        -------
+        :class:`MinimizerResult`
+            Object containing the optimization results from the basinhopping
+            algorithm.
+
+
+        .. versionadded:: 0.9.10
+
+        """
+        result = self.prepare_fit(params=params)
+        result.method = 'basinhopping'
+
+        basinhopping_kws = dict(niter=100, T=1.0, stepsize=0.5,
+                                minimizer_kwargs={}, take_step=None,
+                                accept_test=None, callback=None, interval=50,
+                                disp=False, niter_success=None, seed=None)
+
+        basinhopping_kws.update(self.kws)
+        basinhopping_kws.update(kws)
+
+        x0 = result.init_vals
+
+        try:
+            ret = scipy_basinhopping(self.penalty, x0, **basinhopping_kws)
+        except AbortFitException:
+            pass
+
+        if not result.aborted:
+            result.message = ret.message
+            result.residual = self.__residual(ret.x)
+            result.nfev -= 1
+
+        result._calculate_statistics()
+
+        # calculate the cov_x and estimate uncertanties/correlations
+        if (not result.aborted and self.calc_covar and HAS_NUMDIFFTOOLS and
+                len(result.residual) > len(result.var_names)):
+            _covar_ndt = self._calculate_covariance_matrix(ret.x)
+            if _covar_ndt is not None:
+                result.covar = self._int2ext_cov_x(_covar_ndt, ret.x)
+                self._calculate_uncertainties_correlations()
+
+        return result
+
+    def brute(self, params=None, Ns=20, keep=50, workers=1):
+        """Use the `brute` method to find the global minimum of a function.
+
+        The following parameters are passed to :scipydoc:`optimize.brute`
+        and cannot be changed:
+
+        +-------------------+-------+----------------------------------------+
+        | :meth:`brute` arg | Value | Description                            |
+        +===================+=======+========================================+
+        |   full_output     | 1     | Return the evaluation grid and         |
+        |                   |       | the objective function's values on it. |
+        +-------------------+-------+----------------------------------------+
+        |   finish          | None  | No "polishing" function is to be used  |
+        |                   |       | after the grid search.                 |
+        +-------------------+-------+----------------------------------------+
+        |   disp            | False | Do not print convergence messages      |
+        |                   |       | (when finish is not None).             |
+        +-------------------+-------+----------------------------------------+
+
+        It assumes that the input Parameters have been initialized, and a
+        function to minimize has been properly set up.
+
+        Parameters
+        ----------
+        params : :class:`~lmfit.parameter.Parameters`, optional
+            Contains the Parameters for the model. If None, then the
+            Parameters used to initialize the Minimizer object are used.
+        Ns : int, optional
+            Number of grid points along the axes, if not otherwise specified
+            (see Notes).
+        keep : int, optional
+            Number of best candidates from the brute force method that are
+            stored in the :attr:`candidates` attribute. If 'all', then all grid
+            points from :scipydoc:`optimize.brute` are stored as candidates.
+        workers : int or map-like callable, optional
+            For parallel evaluation of the grid, added in SciPy v1.3 (see
+            :scipydoc:`optimize.brute` for more details).
+
+        Returns
+        -------
+        :class:`MinimizerResult`
+            Object containing the parameters from the brute force method.
+            The return values (`x0`, `fval`, `grid`, `Jout`) from
+            :scipydoc:`optimize.brute` are stored as `brute_<parname>` attributes.
+            The `MinimizerResult` also contains the `candidates` attribute and
+            `show_candidates()` method. The `candidates` attribute contains the
+            parameters and chisqr from the brute force method as a namedtuple,
+            ('Candidate', ['params', 'score']), sorted on the (lowest) chisqr
+            value. To access the values for a particular candidate one can use
+            `result.candidate[#].params` or `result.candidate[#].score`, where
+            a lower # represents a better candidate. The `show_candidates(#)`
+            uses the :meth:`pretty_print` method to show a specific candidate-#
+            or all candidates when no number is specified.
+
+
+        .. versionadded:: 0.9.6
+
+
+        Notes
+        -----
+        The :meth:`brute` method evalutes the function at each point of a
+        multidimensional grid of points. The grid points are generated from the
+        parameter ranges using `Ns` and (optional) `brute_step`.
+        The implementation in :scipydoc:`optimize.brute` requires finite bounds
+        and the `range` is specified as a two-tuple `(min, max)` or slice-object
+        `(min, max, brute_step)`. A slice-object is used directly, whereas a
+        two-tuple is converted to a slice object that interpolates `Ns` points
+        from `min` to `max`, inclusive.
+
+        In addition, the :meth:`brute` method in lmfit, handles three other
+        scenarios given below with their respective slice-object:
+
+            - lower bound (:attr:`min`) and :attr:`brute_step` are specified:
+                range = (`min`, `min` + `Ns` * `brute_step`, `brute_step`).
+            - upper bound (:attr:`max`) and :attr:`brute_step` are specified:
+                range = (`max` - `Ns` * `brute_step`, `max`, `brute_step`).
+            - numerical value (:attr:`value`) and :attr:`brute_step` are specified:
+                range = (`value` - (`Ns`//2) * `brute_step`, `value` +
+                (`Ns`//2) * `brute_step`, `brute_step`).
+
+        """
+        result = self.prepare_fit(params=params)
+        result.method = 'brute'
+
+        brute_kws = dict(full_output=1, finish=None, disp=False)
+        # keyword 'workers' is introduced in SciPy v1.3
+        # FIXME: remove this check after updating the requirement >= 1.3
+        major, minor, micro = scipy_version.split('.', 2)
+
+        if int(major) == 1 and int(minor) >= 3:
+            brute_kws.update({'workers': workers})
+
+        varying = np.asarray([par.vary for par in self.params.values()])
+        replace_none = lambda x, sign: sign*np.inf if x is None else x
+        lower_bounds = np.asarray([replace_none(i.min, -1) for i in
+                                   self.params.values()])[varying]
+        upper_bounds = np.asarray([replace_none(i.max, 1) for i in
+                                   self.params.values()])[varying]
+        value = np.asarray([i.value for i in self.params.values()])[varying]
+        stepsize = np.asarray([i.brute_step for i in self.params.values()])[varying]
+
+        ranges = []
+        for i, step in enumerate(stepsize):
+            if np.all(np.isfinite([lower_bounds[i], upper_bounds[i]])):
+                # lower AND upper bounds are specified (brute_step optional)
+                par_range = ((lower_bounds[i], upper_bounds[i], step)
+                             if step else (lower_bounds[i], upper_bounds[i]))
+            elif np.isfinite(lower_bounds[i]) and step:
+                # lower bound AND brute_step are specified
+                par_range = (lower_bounds[i], lower_bounds[i] + Ns*step, step)
+            elif np.isfinite(upper_bounds[i]) and step:
+                # upper bound AND brute_step are specified
+                par_range = (upper_bounds[i] - Ns*step, upper_bounds[i], step)
+            elif np.isfinite(value[i]) and step:
+                # no bounds, but an initial value is specified
+                par_range = (value[i] - (Ns//2)*step, value[i] + (Ns//2)*step,
+                             step)
+            else:
+                raise ValueError('Not enough information provided for the brute '
+                                 'force method. Please specify bounds or at '
+                                 'least an initial value and brute_step for '
+                                 'parameter "{}".'.format(result.var_names[i]))
+            ranges.append(par_range)
+
+        try:
+            ret = scipy_brute(self.penalty, tuple(ranges), Ns=Ns, **brute_kws)
+        except AbortFitException:
+            pass
+
+        if not result.aborted:
+            result.brute_x0 = ret[0]
+            result.brute_fval = ret[1]
+            result.brute_grid = ret[2]
+            result.brute_Jout = ret[3]
+
+            # sort the results of brute and populate .candidates attribute
+            grid_score = ret[3].ravel()  # chisqr
+            grid_points = [par.ravel() for par in ret[2]]
+
+            if len(result.var_names) == 1:
+                grid_result = np.array([res for res in zip(zip(grid_points), grid_score)],
+                                       dtype=[('par', 'O'), ('score', 'float64')])
+            else:
+                grid_result = np.array([res for res in zip(zip(*grid_points), grid_score)],
+                                       dtype=[('par', 'O'), ('score', 'float64')])
+            grid_result_sorted = grid_result[grid_result.argsort(order='score')]
+
+            result.candidates = []
+
+            if keep == 'all':
+                keep_candidates = len(grid_result_sorted)
+            else:
+                keep_candidates = min(len(grid_result_sorted), keep)
+
+            for data in grid_result_sorted[:keep_candidates]:
+                pars = deepcopy(self.params)
+                for i, par in enumerate(result.var_names):
+                    pars[par].value = data[0][i]
+                result.candidates.append(Candidate(params=pars, score=data[1]))
+
+            result.params = result.candidates[0].params
+            result.residual = self.__residual(result.brute_x0, apply_bounds_transformation=False)
+            result.nfev = len(result.brute_Jout.ravel())
+
+        result._calculate_statistics()
+
+        return result
+
+    def ampgo(self, params=None, **kws):
+        """Find the global minimum of a multivariate function using AMPGO.
+
+        AMPGO stands for 'Adaptive Memory Programming for Global Optimization'
+        and is an efficient algorithm to find the global minimum.
+
+        Parameters
+        ----------
+        params : :class:`~lmfit.parameter.Parameters`, optional
+            Contains the Parameters for the model. If None, then the
+            Parameters used to initialize the Minimizer object are used.
+        **kws : dict, optional
+            Minimizer options to pass to the ampgo algorithm, the options are
+            listed below::
+
+                local: str (default is 'L-BFGS-B')
+                    Name of the local minimization method. Valid options are:
+                    - 'L-BFGS-B'
+                    - 'Nelder-Mead'
+                    - 'Powell'
+                    - 'TNC'
+                    - 'SLSQP'
+                local_opts: dict (default is None)
+                    Options to pass to the local minimizer.
+                maxfunevals: int (default is None)
+                    Maximum number of function evaluations. If None, the optimization will stop
+                    after `totaliter` number of iterations.
+                totaliter: int (default is 20)
+                    Maximum number of global iterations.
+                maxiter: int (default is 5)
+                    Maximum number of `Tabu Tunneling` iterations during each global iteration.
+                glbtol: float (default is 1e-5)
+                    Tolerance whether or not to accept a solution after a tunneling phase.
+                eps1: float (default is 0.02)
+                    Constant used to define an aspiration value for the objective function during
+                    the Tunneling phase.
+                eps2: float (default is 0.1)
+                    Perturbation factor used to move away from the latest local minimum at the
+                    start of a Tunneling phase.
+                tabulistsize: int (default is 5)
+                    Size of the (circular) tabu search list.
+                tabustrategy: str (default is 'farthest')
+                    Strategy to use when the size of the tabu list exceeds `tabulistsize`. It
+                    can be 'oldest' to drop the oldest point from the tabu list or 'farthest'
+                    to drop the element farthest from the last local minimum found.
+                disp: bool (default is False)
+                    Set to True to print convergence messages.
+
+        Returns
+        -------
+        :class:`MinimizerResult`
+            Object containing the parameters from the ampgo method, with fit
+            parameters, statistics and such. The return values (`x0`, `fval`,
+            `eval`, `msg`, `tunnel`) are stored as `ampgo_<parname>` attributes.
+
+
+        .. versionadded:: 0.9.10
+
+
+        Notes
+        ----
+        The Python implementation was written by Andrea Gavana in 2014
+        (http://infinity77.net/global_optimization/index.html).
+
+        The details of the AMPGO algorithm are described in the paper
+        "Adaptive Memory Programming for Constrained Global Optimization"
+        located here:
+
+        http://leeds-faculty.colorado.edu/glover/fred%20pubs/416%20-%20AMP%20(TS)%20for%20Constrained%20Global%20Opt%20w%20Lasdon%20et%20al%20.pdf
+
+        """
+        result = self.prepare_fit(params=params)
+
+        ampgo_kws = dict(local='L-BFGS-B', local_opts=None, maxfunevals=None,
+                         totaliter=20, maxiter=5, glbtol=1e-5, eps1=0.02,
+                         eps2=0.1, tabulistsize=5, tabustrategy='farthest',
+                         disp=False)
+        ampgo_kws.update(self.kws)
+        ampgo_kws.update(kws)
+
+        values = result.init_vals
+        result.method = "ampgo, with {} as local solver".format(ampgo_kws['local'])
+
+        try:
+            ret = ampgo(self.penalty, values, **ampgo_kws)
+        except AbortFitException:
+            pass
+
+        if not result.aborted:
+            result.ampgo_x0 = ret[0]
+            result.ampgo_fval = ret[1]
+            result.ampgo_eval = ret[2]
+            result.ampgo_msg = ret[3]
+            result.ampgo_tunnel = ret[4]
+
+            for i, par in enumerate(result.var_names):
+                result.params[par].value = result.ampgo_x0[i]
+
+            result.residual = self.__residual(result.ampgo_x0)
+            result.nfev -= 1
+
+        result._calculate_statistics()
+
+        # calculate the cov_x and estimate uncertanties/correlations
+        if (not result.aborted and self.calc_covar and HAS_NUMDIFFTOOLS and
+                len(result.residual) > len(result.var_names)):
+            _covar_ndt = self._calculate_covariance_matrix(result.ampgo_x0)
+            if _covar_ndt is not None:
+                result.covar = self._int2ext_cov_x(_covar_ndt, result.ampgo_x0)
+                self._calculate_uncertainties_correlations()
+
+        return result
+
+    def shgo(self, params=None, **kws):
+        """Use the `SHGO` algorithm to find the global minimum.
+
+        SHGO stands for "simplicial homology global optimization" and calls
+        :scipydoc:`optimize.shgo` using its default arguments.
+
+        Parameters
+        ----------
+        params : :class:`~lmfit.parameter.Parameters`, optional
+            Contains the Parameters for the model. If None, then the
+            Parameters used to initialize the Minimizer object are used.
+        **kws : dict, optional
+            Minimizer options to pass to the SHGO algorithm.
+
+        Returns
+        -------
+        :class:`MinimizerResult`
+            Object containing the parameters from the SHGO method.
+            The return values specific to :scipydoc:`optimize.shgo` (`x`,
+            `xl`, `fun`, `funl`, `nfev`, `nit`, `nlfev`, `nlhev`, and
+            `nljev`) are stored as `shgo_<parname>` attributes.
+
+
+        .. versionadded:: 0.9.14
+
+        """
+        result = self.prepare_fit(params=params)
+        result.method = 'shgo'
+
+        shgo_kws = dict(constraints=None, n=100, iters=1, callback=None,
+                        minimizer_kwargs=None, options=None,
+                        sampling_method='simplicial')
+
+        shgo_kws.update(self.kws)
+        shgo_kws.update(kws)
+
+        varying = np.asarray([par.vary for par in self.params.values()])
+        bounds = np.asarray([(par.min, par.max) for par in
+                             self.params.values()])[varying]
+
+        try:
+            ret = scipy_shgo(self.penalty, bounds, **shgo_kws)
+        except AbortFitException:
+            pass
+
+        if not result.aborted:
+            for attr, value in ret.items():
+                if attr in ['success', 'message']:
+                    setattr(result, attr, value)
+                else:
+                    setattr(result, 'shgo_{}'.format(attr), value)
+
+            result.residual = self.__residual(result.shgo_x, False)
+            result.nfev -= 1
+
+        result._calculate_statistics()
+
+        # calculate the cov_x and estimate uncertanties/correlations
+        if (not result.aborted and self.calc_covar and HAS_NUMDIFFTOOLS and
+                len(result.residual) > len(result.var_names)):
+            result.covar = self._calculate_covariance_matrix(result.shgo_x)
+            if result.covar is not None:
+                self._calculate_uncertainties_correlations()
+
+        return result
+
+    def dual_annealing(self, params=None, **kws):
+        """Use the `dual_annealing` algorithm to find the global minimum.
+
+        This method calls :scipydoc:`optimize.dual_annealing` using its
+        default arguments.
+
+        Parameters
+        ----------
+        params : :class:`~lmfit.parameter.Parameters`, optional
+            Contains the Parameters for the model. If None, then the
+            Parameters used to initialize the Minimizer object are used.
+        **kws : dict, optional
+            Minimizer options to pass to the dual_annealing algorithm.
+
+        Returns
+        -------
+        :class:`MinimizerResult`
+            Object containing the parameters from the dual_annealing method.
+            The return values specific to :scipydoc:`optimize.dual_annealing`
+            (`x`, `fun`, `nfev`, `nhev`, `njev`, and `nit`) are stored as
+            `da_<parname>` attributes.
+
+
+        .. versionadded:: 0.9.14
+
+        """
+        result = self.prepare_fit(params=params)
+        result.method = 'dual_annealing'
+
+        da_kws = dict(maxiter=1000, local_search_options={},
+                      initial_temp=5230.0, restart_temp_ratio=2e-05,
+                      visit=2.62, accept=-5.0, maxfun=10000000.0, seed=None,
+                      no_local_search=False, callback=None, x0=None)
+
+        da_kws.update(self.kws)
+        da_kws.update(kws)
+
+        varying = np.asarray([par.vary for par in self.params.values()])
+        bounds = np.asarray([(par.min, par.max) for par in
+                             self.params.values()])[varying]
+
+        if not np.all(np.isfinite(bounds)):
+            raise ValueError('dual_annealing requires finite bounds for all'
+                             ' varying parameters')
+
+        try:
+            ret = scipy_dual_annealing(self.penalty, bounds, **da_kws)
+        except AbortFitException:
+            pass
+
+        if not result.aborted:
+            for attr, value in ret.items():
+                if attr in ['success', 'message']:
+                    setattr(result, attr, value)
+                else:
+                    setattr(result, 'da_{}'.format(attr), value)
+
+            result.residual = self.__residual(result.da_x, False)
+            result.nfev -= 1
+
+        result._calculate_statistics()
+
+        # calculate the cov_x and estimate uncertanties/correlations
+        if (not result.aborted and self.calc_covar and HAS_NUMDIFFTOOLS and
+                len(result.residual) > len(result.var_names)):
+            result.covar = self._calculate_covariance_matrix(result.da_x)
+            if result.covar is not None:
+                self._calculate_uncertainties_correlations()
+
+        return result
+
+    def minimize(self, method='leastsq', params=None, **kws):
+        """Perform the minimization.
+
+        Parameters
+        ----------
+        method : str, optional
+            Name of the fitting method to use. Valid values are:
+
+            - `'leastsq'`: Levenberg-Marquardt (default)
+            - `'least_squares'`: Least-Squares minimization, using Trust Region Reflective method
+            - `'differential_evolution'`: differential evolution
+            - `'brute'`: brute force method
+            - `'basinhopping'`: basinhopping
+            - `'ampgo'`: Adaptive Memory Programming for Global Optimization
+            - '`nelder`': Nelder-Mead
+            - `'lbfgsb'`: L-BFGS-B
+            - `'powell'`: Powell
+            - `'cg'`: Conjugate-Gradient
+            - `'newton'`: Newton-CG
+            - `'cobyla'`: Cobyla
+            - `'bfgs'`: BFGS
+            - `'tnc'`: Truncated Newton
+            - `'trust-ncg'`: Newton-CG trust-region
+            - `'trust-exact'`: nearly exact trust-region
+            - `'trust-krylov'`: Newton GLTR trust-region
+            - `'trust-constr'`: trust-region for constrained optimization
+            - `'dogleg'`: Dog-leg trust-region
+            - `'slsqp'`: Sequential Linear Squares Programming
+            - `'emcee'`: Maximum likelihood via Monte-Carlo Markov Chain
+            - `'shgo'`: Simplicial Homology Global Optimization
+            - `'dual_annealing'`: Dual Annealing optimization
+
+            In most cases, these methods wrap and use the method with the
+            same name from `scipy.optimize`, or use
+            `scipy.optimize.minimize` with the same `method` argument.
+            Thus '`leastsq`' will use `scipy.optimize.leastsq`, while
+            '`powell`' will use `scipy.optimize.minimizer(...,
+            method='powell')`
+
+            For more details on the fitting methods please refer to the
+            `SciPy docs <https://docs.scipy.org/doc/scipy/reference/optimize.html>`__.
+
+        params : :class:`~lmfit.parameter.Parameters`, optional
+            Parameters of the model to use as starting values.
+
+        **kws : optional
+            Additional arguments are passed to the underlying minimization
+            method.
+
+        Returns
+        -------
+        :class:`MinimizerResult`
+            Object containing the optimized parameter and several
+            goodness-of-fit statistics.
+
+
+        .. versionchanged:: 0.9.0
+           Return value changed to :class:`MinimizerResult`.
+
+        """
+        function = self.leastsq
+        kwargs = {'params': params}
+        kwargs.update(self.kws)
+        kwargs.update(kws)
+
+        user_method = method.lower()
+        if user_method.startswith('leasts'):
+            function = self.leastsq
+        elif user_method.startswith('least_s'):
+            function = self.least_squares
+        elif user_method == 'brute':
+            function = self.brute
+        elif user_method == 'basinhopping':
+            function = self.basinhopping
+        elif user_method == 'ampgo':
+            function = self.ampgo
+        elif user_method == 'emcee':
+            function = self.emcee
+        elif user_method == 'shgo':
+            function = self.shgo
+        elif user_method == 'dual_annealing':
+            function = self.dual_annealing
+        else:
+            function = self.scalar_minimize
+            for key, val in SCALAR_METHODS.items():
+                if (key.lower().startswith(user_method) or
+                        val.lower().startswith(user_method)):
+                    kwargs['method'] = val
+        return function(**kwargs)
+
+
+def _make_random_gen(seed):
+    """Turn seed into a numpy.random.RandomState instance.
+
+    If seed is None, return the RandomState singleton used by
+    numpy.random. If seed is an int, return a new RandomState instance
+    seeded with seed. If seed is already a RandomState instance, return
+    it. Otherwise raise ValueError.
+
+    """
+    if seed is None or seed is np.random:
+        return np.random.mtrand._rand
+    if isinstance(seed, (numbers.Integral, np.integer)):
+        return np.random.RandomState(seed)
+    if isinstance(seed, np.random.RandomState):
+        return seed
+    raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
+                     ' instance' % seed)
+
+
+def _nan_policy(arr, nan_policy='raise', handle_inf=True):
+    """Specify behaviour when an array contains numpy.nan or numpy.inf.
+
+    Parameters
+    ----------
+    arr : array_like
+        Input array to consider.
+    nan_policy : str, optional
+        One of:
+
+        'raise' - raise a `ValueError` if `arr` contains NaN (default)
+        'propagate' - propagate NaN
+        'omit' - filter NaN from input array
+    handle_inf : bool, optional
+        As well as NaN consider +/- inf.
+
+    Returns
+    -------
+    filtered_array : array_like
+
+    Note
+    ----
+    This function is copied, then modified, from
+    scipy/stats/stats.py/_contains_nan
+
+    """
+    if nan_policy not in ('propagate', 'omit', 'raise'):
+        raise ValueError("nan_policy must be 'propagate', 'omit', or 'raise'.")
+
+    if handle_inf:
+        handler_func = lambda x: ~np.isfinite(x)
+    else:
+        handler_func = isnull
+
+    if nan_policy == 'omit':
+        # mask locates any values to remove
+        mask = ~handler_func(arr)
+        if not np.all(mask):  # there are some NaNs/infs/missing values
+            return arr[mask]
+
+    if nan_policy == 'raise':
+        try:
+            # Calling np.sum to avoid creating a huge array into memory
+            # e.g. np.isnan(a).any()
+            with np.errstate(invalid='ignore'):
+                contains_nan = handler_func(np.sum(arr))
+        except TypeError:
+            # If the check cannot be properly performed we fallback to omiting
+            # nan values and raising a warning. This can happen when attempting to
+            # sum things that are not numbers (e.g. as in the function `mode`).
+            contains_nan = False
+            warnings.warn("The input array could not be checked for NaNs. "
+                          "NaNs will be ignored.", RuntimeWarning)
+
+        if contains_nan:
+            msg = ('NaN values detected in your input data or the output of '
+                   'your objective/model function - fitting algorithms cannot '
+                   'handle this! Please read https://lmfit.github.io/lmfit-py/faq.html#i-get-errors-from-nan-in-my-fit-what-can-i-do '
+                   'for more information.')
+            raise ValueError(msg)
+    return arr
+
+
+def minimize(fcn, params, method='leastsq', args=None, kws=None, iter_cb=None,
+             scale_covar=True, nan_policy='raise', reduce_fcn=None,
+             calc_covar=True, **fit_kws):
+    """Perform a fit of a set of parameters by minimizing an objective (or
+    cost) function using one of the several available methods.
+
+    The minimize function takes an objective function to be minimized,
+    a dictionary (:class:`~lmfit.parameter.Parameters`) containing the model
+    parameters, and several optional arguments.
+
+    Parameters
+    ----------
+    fcn : callable
+        Objective function to be minimized. When method is `leastsq` or
+        `least_squares`, the objective function should return an array
+        of residuals (difference between model and data) to be minimized
+        in a least-squares sense. With the scalar methods the objective
+        function can either return the residuals array or a single scalar
+        value. The function must have the signature:
+        `fcn(params, *args, **kws)`
+    params : :class:`~lmfit.parameter.Parameters`
+        Contains the Parameters for the model.
+    method : str, optional
+        Name of the fitting method to use. Valid values are:
+
+        - `'leastsq'`: Levenberg-Marquardt (default)
+        - `'least_squares'`: Least-Squares minimization, using Trust Region Reflective method
+        - `'differential_evolution'`: differential evolution
+        - `'brute'`: brute force method
+        - `'basinhopping'`: basinhopping
+        - `'ampgo'`: Adaptive Memory Programming for Global Optimization
+        - '`nelder`': Nelder-Mead
+        - `'lbfgsb'`: L-BFGS-B
+        - `'powell'`: Powell
+        - `'cg'`: Conjugate-Gradient
+        - `'newton'`: Newton-CG
+        - `'cobyla'`: Cobyla
+        - `'bfgs'`: BFGS
+        - `'tnc'`: Truncated Newton
+        - `'trust-ncg'`: Newton-CG trust-region
+        - `'trust-exact'`: nearly exact trust-region
+        - `'trust-krylov'`: Newton GLTR trust-region
+        - `'trust-constr'`: trust-region for constrained optimization
+        - `'dogleg'`: Dog-leg trust-region
+        - `'slsqp'`: Sequential Linear Squares Programming
+        - `'emcee'`: Maximum likelihood via Monte-Carlo Markov Chain
+        - `'shgo'`: Simplicial Homology Global Optimization
+        - `'dual_annealing'`: Dual Annealing optimization
+
+        In most cases, these methods wrap and use the method of the same
+        name from `scipy.optimize`, or use `scipy.optimize.minimize` with
+        the same `method` argument.  Thus '`leastsq`' will use
+        `scipy.optimize.leastsq`, while '`powell`' will use
+        `scipy.optimize.minimizer(..., method='powell')`
+
+        For more details on the fitting methods please refer to the
+        `SciPy docs <https://docs.scipy.org/doc/scipy/reference/optimize.html>`__.
+
+    args : tuple, optional
+        Positional arguments to pass to `fcn`.
+    kws : dict, optional
+        Keyword arguments to pass to `fcn`.
+    iter_cb : callable, optional
+        Function to be called at each fit iteration. This function should
+        have the signature `iter_cb(params, iter, resid, *args, **kws)`,
+        where `params` will have the current parameter values, `iter`
+        the iteration number, `resid` the current residual array, and `*args`
+        and `**kws` as passed to the objective function.
+    scale_covar : bool, optional
+        Whether to automatically scale the covariance matrix (default is True).
+    nan_policy : str, optional
+        Specifies action if `userfcn` (or a Jacobian) returns NaN
+        values. One of:
+
+        - 'raise' : a `ValueError` is raised
+        - 'propagate' : the values returned from `userfcn` are un-altered
+        - 'omit' : non-finite values are filtered
+
+    reduce_fcn : str or callable, optional
+        Function to convert a residual array to a scalar value for the scalar
+        minimizers. See notes in `Minimizer`.
+    calc_covar : bool, optional
+        Whether to calculate the covariance matrix (default is True) for
+        solvers other than `leastsq` and `least_squares`. Requires the
+        `numdifftools` package to be installed.
+    **fit_kws : dict, optional
+        Options to pass to the minimizer being used.
+
+    Returns
+    -------
+    :class:`MinimizerResult`
+        Object containing the optimized parameter and several
+        goodness-of-fit statistics.
+
+
+    .. versionchanged:: 0.9.0
+        Return value changed to :class:`MinimizerResult`.
+
+    Notes
+    -----
+    The objective function should return the value to be minimized. For the
+    Levenberg-Marquardt algorithm from leastsq(), this returned value must
+    be an array, with a length greater than or equal to the number of
+    fitting variables in the model. For the other methods, the return value
+    can either be a scalar or an array. If an array is returned, the sum of
+    squares of the array will be sent to the underlying fitting method,
+    effectively doing a least-squares optimization of the return values.
+
+    A common use for `args` and `kws` would be to pass in other
+    data needed to calculate the residual, including such things as the
+    data array, dependent variable, uncertainties in the data, and other
+    data structures for the model calculation.
+
+    On output, `params` will be unchanged.  The best-fit values and, where
+    appropriate, estimated uncertainties and correlations, will all be
+    contained in the returned :class:`MinimizerResult`.  See
+    :ref:`fit-results-label` for further details.
+
+    This function is simply a wrapper around :class:`Minimizer`
+    and is equivalent to::
+
+        fitter = Minimizer(fcn, params, fcn_args=args, fcn_kws=kws,
+                           iter_cb=iter_cb, scale_covar=scale_covar,
+                           nan_policy=nan_policy, reduce_fcn=reduce_fcn,
+                           calc_covar=calc_covar, **fit_kws)
+        fitter.minimize(method=method)
+
+    """
+    fitter = Minimizer(fcn, params, fcn_args=args, fcn_kws=kws,
+                       iter_cb=iter_cb, scale_covar=scale_covar,
+                       nan_policy=nan_policy, reduce_fcn=reduce_fcn,
+                       calc_covar=calc_covar, **fit_kws)
+    return fitter.minimize(method=method)
diff --git a/build/lib/gsf/plot_sed.py b/build/lib/gsf/plot_sed.py
new file mode 100644
index 0000000..6a5bd89
--- /dev/null
+++ b/build/lib/gsf/plot_sed.py
@@ -0,0 +1,4122 @@
+import numpy as np
+import sys
+import os
+import asdf
+
+import matplotlib.pyplot as plt
+from numpy import log10
+from scipy.integrate import simps
+from astropy.io import fits
+from matplotlib.ticker import FormatStrFormatter
+import matplotlib.ticker as ticker
+
+from .function import *
+from .function_class import Func
+from .basic_func import Basic
+import corner
+
+col = ['violet', 'indigo', 'b', 'lightblue', 'lightgreen', 'g', 'orange', 'coral', 'r', 'darkred']#, 'k']
+
+
+def plot_sed(MB, flim=0.01, fil_path='./', scale=1e-19, f_chind=True, figpdf=False, save_sed=True, 
+    mmax=300, dust_model=0, DIR_TMP='./templates/', f_label=False, f_bbbox=False, verbose=False, f_silence=True,
+    f_fill=False, f_fancyplot=False, f_Alog=True, dpi=300, f_plot_filter=True, f_plot_resid=False, NRbb_lim=10000,
+    x1min=4000):
+    '''
+    Parameters
+    ----------
+    MB.SNlim : float
+        SN limit to show flux or up lim in SED.
+    f_chind : bool
+        If include non-detection in chi2 calculation, using Sawicki12.
+    mmax : int
+        Number of mcmc realization for plot. Not for calculation.
+    f_fancy : bool
+        plot each SED component.
+    f_fill: bool
+        if True, and so is f_fancy, fill each SED component.
+
+    Returns
+    -------
+    plots
+
+    '''
+
+    from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
+    from mpl_toolkits.axes_grid1.inset_locator import mark_inset
+    from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+    from scipy.optimize import curve_fit
+    from scipy import asarray as ar,exp
+    import matplotlib
+    import scipy.integrate as integrate
+    import scipy.special as special
+    import os.path
+    from astropy.io import ascii
+    import time
+
+    # if f_silence:
+    #     try:
+    #         import matplotlib
+    #         matplotlib.use("Agg")
+    #     except:
+    #         print('matplotlib Agg backend is not found.')
+    #         pass
+    # else:
+    #     try:
+    #         matplotlib.use("MacOSX")
+    #     except:
+    #         pass
+
+    def gaus(x,a,x0,sigma):
+        return a*exp(-(x-x0)**2/(2*sigma**2))
+
+    print('\n### Running plot_sed ###\n')
+
+    lcb = '#4682b4' # line color, blue
+
+    fnc  = MB.fnc 
+    bfnc = MB.bfnc
+    ID   = MB.ID
+    Z    = MB.Zall
+    age  = MB.age
+    nage = MB.nage 
+    tau0 = MB.tau0
+    
+    #col = ['violet', 'indigo', 'b', 'lightblue', 'lightgreen', 'g', 'orange', 'coral', 'r', 'darkred']#, 'k']
+    NUM_COLORS = len(age)
+    cm = plt.get_cmap('gist_rainbow')
+    col = [cm(1 - 1.*i/NUM_COLORS) for i in range(NUM_COLORS)]
+
+    nstep_plot = 1
+    if MB.f_bpass:
+        nstep_plot = 30
+
+    SNlim = MB.SNlim
+
+    ################
+    # RF colors.
+    home = os.path.expanduser('~')
+    c = MB.c
+    chimax = 1.
+    m0set = MB.m0set
+    Mpc_cm = MB.Mpc_cm
+
+    ##################
+    # Fitting Results
+    ##################
+    DIR_FILT = MB.DIR_FILT
+    SFILT = MB.filts
+
+    try:
+        f_err = MB.ferr
+    except:
+        f_err = 0
+
+    ###########################
+    # Open result file
+    ###########################
+    file = MB.DIR_OUT + 'summary_' + ID + '.fits'
+    hdul = fits.open(file)
+    
+    ndim_eff = hdul[0].header['NDIM']
+
+    # Redshift MC
+    zp16  = hdul[1].data['zmc'][0]
+    zp50  = hdul[1].data['zmc'][1]
+    zp84  = hdul[1].data['zmc'][2]
+
+    # Stellar mass MC
+    M16 = hdul[1].data['ms'][0]
+    M50 = hdul[1].data['ms'][1]
+    M84 = hdul[1].data['ms'][2]
+    if verbose:
+        print('Total stellar mass is %.2e'%(M50))
+
+    # Amplitude MC
+    A50 = np.zeros(len(age), dtype='float')
+    A16 = np.zeros(len(age), dtype='float')
+    A84 = np.zeros(len(age), dtype='float')
+    for aa in range(len(age)):
+        A16[aa] = 10**hdul[1].data['A'+str(aa)][0]
+        A50[aa] = 10**hdul[1].data['A'+str(aa)][1]
+        A84[aa] = 10**hdul[1].data['A'+str(aa)][2]
+
+    Asum  = np.sum(A50)
+
+    if MB.fneb:
+        logU50 = hdul[1].data['logU'][1]
+        Aneb50 = 10**hdul[1].data['Aneb'][1]
+
+    aa = 0
+    Av16 = hdul[1].data['Av'+str(aa)][0]
+    Av50 = hdul[1].data['Av'+str(aa)][1]
+    Av84 = hdul[1].data['Av'+str(aa)][2]
+    AAv = [Av50]
+
+    Z50 = np.zeros(len(age), dtype='float')
+    Z16 = np.zeros(len(age), dtype='float')
+    Z84 = np.zeros(len(age), dtype='float')
+    NZbest = np.zeros(len(age), dtype='int')
+    for aa in range(len(age)):
+        Z16[aa] = hdul[1].data['Z'+str(aa)][0]
+        Z50[aa] = hdul[1].data['Z'+str(aa)][1]
+        Z84[aa] = hdul[1].data['Z'+str(aa)][2]
+        NZbest[aa]= bfnc.Z2NZ(Z50[aa])
+
+    # Light weighted Z.
+    ZZ50 = np.sum(Z50*A50)/np.sum(A50)
+
+    # FIR Dust;
+    if MB.f_dust:
+        MD16 = hdul[1].data['MDUST'][0]
+        MD50 = hdul[1].data['MDUST'][1]
+        MD84 = hdul[1].data['MDUST'][2]
+        AD16 = hdul[1].data['ADUST'][0]
+        AD50 = hdul[1].data['ADUST'][1]
+        AD84 = hdul[1].data['ADUST'][2]
+        TD16 = hdul[1].data['TDUST'][0]
+        TD50 = hdul[1].data['TDUST'][1]
+        TD84 = hdul[1].data['TDUST'][2]
+        nTD16 = hdul[1].data['nTDUST'][0]
+        nTD50 = hdul[1].data['nTDUST'][1]
+        nTD84 = hdul[1].data['nTDUST'][2]
+        DFILT = MB.inputs['FIR_FILTER'] # filter band string.
+        DFILT = [x.strip() for x in DFILT.split(',')]
+        DFWFILT = fil_fwhm(DFILT, DIR_FILT)
+        if verbose:
+            print('Total dust mass is %.2e'%(MD50))
+
+    chi = hdul[1].data['chi'][0]
+    chin = hdul[1].data['chi'][1]
+    fitc = chin
+
+    Cz0 = hdul[0].header['Cz0']
+    Cz1 = hdul[0].header['Cz1']
+    Cz2 = hdul[0].header['Cz2']
+    zbes = zp50 
+    zscl = (1.+zbes)
+
+    ###############################
+    # Data taken from
+    ###############################
+    if MB.f_dust:
+        MB.dict = MB.read_data(Cz0, Cz1, Cz2, zbes, add_fir=True)
+    else:
+        MB.dict = MB.read_data(Cz0, Cz1, Cz2, zbes)
+
+    NR   = MB.dict['NR']
+    x    = MB.dict['x']
+    fy   = MB.dict['fy']
+    ey   = MB.dict['ey']
+    
+    con0 = (NR<1000)
+    xg0  = x[con0]
+    fg0  = fy[con0]
+    eg0  = ey[con0]
+    con1 = (NR>=1000) & (NR<2000)
+    xg1  = x[con1]
+    fg1  = fy[con1]
+    eg1  = ey[con1]
+    con2 = (NR>=1000) & (NR<NRbb_lim)
+    xg2  = x[con2]
+    fg2  = fy[con2]
+    eg2  = ey[con2]
+
+    if len(xg0)>0 or len(xg1)>0 or len(xg2)>0:
+        f_grsm = True
+    else:
+        f_grsm = False
+
+    wht = fy * 0
+    con_wht = (ey>0)
+    wht[con_wht] = 1./np.square(ey[con_wht])
+
+    # BB data points;
+    NRbb = MB.dict['NRbb']
+    xbb  = MB.dict['xbb']
+    fybb = MB.dict['fybb']
+    eybb = MB.dict['eybb']
+    exbb = MB.dict['exbb']
+    snbb = fybb/eybb
+
+    ######################
+    # Weight by line
+    ######################
+    wh0  = 1./np.square(eg0)
+    LW0  = []
+    model = fg0
+    wht3 = check_line_man(fy, x, wht, fy, zbes, LW0)
+
+    ######################
+    # Mass-to-Light ratio.
+    ######################
+    ms = np.zeros(len(age), dtype='float')
+    af = MB.af
+    sedpar = af['ML']
+
+    for aa in range(len(age)):
+        ms[aa] = sedpar['ML_' +  str(int(NZbest[aa]))][aa]
+    try:
+        isochrone = af['isochrone']
+        LIBRARY = af['library']
+        nimf = af['NIMF']
+    except:
+        isochrone = ''
+        LIBRARY = ''
+        nimf = ''
+
+    #############
+    # Plot.
+    #############
+    # Set the inset.
+    if f_grsm or MB.f_dust:
+        fig = plt.figure(figsize=(7.,3.2))
+        fig.subplots_adjust(top=0.98, bottom=0.16, left=0.1, right=0.99, hspace=0.15, wspace=0.25)
+        ax1 = fig.add_subplot(111)
+        xsize = 0.29
+        ysize = 0.25
+        if f_grsm:
+            ax2t = ax1.inset_axes((1-xsize-0.01,1-ysize-0.01,xsize,ysize))
+        if MB.f_dust:
+            ax3t = ax1.inset_axes((0.7,.35,.28,.25))
+
+        f_plot_resid = False
+        print('Grism data. f_plot_resid is turned off.')
+    else:
+        if f_plot_resid:
+            fig_mosaic = """
+            AAAA
+            AAAA
+            BBBB
+            """
+            fig,axes = plt.subplot_mosaic(mosaic=fig_mosaic, figsize=(5.5,4.))
+            fig.subplots_adjust(top=0.98, bottom=0.16, left=0.08, right=0.99, hspace=0.15, wspace=0.25)
+            ax1 = axes['A']
+        else:
+            if f_plot_filter:
+                fig = plt.figure(figsize=(5.5,2.))
+            else:
+                fig = plt.figure(figsize=(5.5,1.8))
+            fig.subplots_adjust(top=0.98, bottom=0.16, left=0.08, right=0.99, hspace=0.15, wspace=0.25)
+            ax1 = fig.add_subplot(111)
+
+    # Determine scale here;
+    if scale == None:
+        conbb_hs = (fybb/eybb > SNlim)
+        scale = 10**(int(np.log10(np.nanmax(fybb[conbb_hs] * c / np.square(xbb[conbb_hs])) / MB.d))) / 10
+    d = MB.d * scale
+
+    #######################################
+    # D.Kelson like Box for BB photometry
+    #######################################
+    col_dat = 'r'
+    if f_bbbox:
+        for ii in range(len(xbb)):
+            if eybb[ii]<100 and fybb[ii]/eybb[ii]>1:
+                xx = [xbb[ii]-exbb[ii],xbb[ii]-exbb[ii]]
+                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
+                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
+                xx = [xbb[ii]+exbb[ii],xbb[ii]+exbb[ii]]
+                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
+                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
+                xx = [xbb[ii]-exbb[ii],xbb[ii]+exbb[ii]]
+                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d]
+                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
+                xx = [xbb[ii]-exbb[ii],xbb[ii]+exbb[ii]]
+                yy = [(fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
+                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
+    else: # Normal BB plot;
+        # Detection;
+        conbb_hs = (fybb/eybb>SNlim)
+        ax1.errorbar(xbb[conbb_hs], fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d, \
+        yerr=eybb[conbb_hs]*c/np.square(xbb[conbb_hs])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
+        ax1.plot(xbb[conbb_hs], fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d, \
+        marker='.', color=col_dat, linestyle='', linewidth=0, zorder=4, ms=8)#, label='Obs.(BB)')
+        try:
+            # For any data removed fron fit (i.e. IRAC excess):
+            #data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
+            NR_ex = MB.data['bb_obs_removed']['NR']# data_ex['col1']
+        except:
+            NR_ex = []
+
+        # Upperlim;
+        sigma = 1.0
+        leng = np.max(fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d) * 0.05 #0.2
+        conebb_ls = (fybb/eybb<=SNlim) & (eybb>0)
+        
+        for ii in range(len(xbb)):
+            if NRbb[ii] in NR_ex[:]:
+                conebb_ls[ii] = False
+
+        ax1.errorbar(xbb[conebb_ls], eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, yerr=leng,\
+            uplims=eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, linestyle='', color=col_dat, marker='', ms=4, label='', zorder=4, capsize=3)
+
+
+    # For any data removed fron fit (i.e. IRAC excess):
+    f_exclude = False
+    try:
+        col_ex = 'lawngreen'
+        # Currently, this file is made after FILTER_SKIP;
+        # data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
+        # x_ex = data_ex['col2']
+        # fy_ex = data_ex['col3']
+        # ey_ex = data_ex['col4']
+        # ex_ex = data_ex['col5']
+        x_ex, fy_ex, ey_ex, ex_ex = MB.data['bb_obs_removed']['x'], MB.data['bb_obs_removed']['fy'], MB.data['bb_obs_removed']['ey'], MB.data['bb_obs_removed']['ex']
+
+        ax1.errorbar(
+            x_ex, fy_ex * c / np.square(x_ex) / d,
+            xerr=ex_ex, yerr=ey_ex*c/np.square(x_ex)/d, color='k', linestyle='', linewidth=0.5, zorder=5
+            )
+        ax1.scatter(
+            x_ex, fy_ex * c / np.square(x_ex) / d, marker='s', color=col_ex, edgecolor='k', zorder=5, s=30
+            )
+        f_exclude = True
+    except:
+        pass
+
+
+    #####################################
+    # Open ascii file and stock to array.
+    lib = fnc.open_spec_fits(fall=0)
+    lib_all = fnc.open_spec_fits(fall=1, orig=True)
+
+    if MB.f_dust:
+        MB.lib_dust = fnc.open_spec_dust_fits(fall=0)
+        MB.lib_dust_all = fnc.open_spec_dust_fits(fall=1)
+    if MB.fneb:
+        lib_neb = MB.fnc.open_spec_fits(fall=0, f_neb=True)
+        lib_neb_all = MB.fnc.open_spec_fits(fall=1, orig=True, f_neb=True)
+
+    iimax = len(nage)-1
+
+    # FIR dust plot;
+    if MB.f_dust:
+        from lmfit import Parameters
+        par = Parameters()
+        par.add('MDUST',value=AD50)
+        par.add('TDUST',value=nTD50)
+        par.add('zmc',value=zp50)
+
+        y0d, x0d = fnc.tmp04_dust(par.valuesdict())#, zbes, lib_dust_all)
+        y0d_cut, x0d_cut = fnc.tmp04_dust(par.valuesdict())#, zbes, lib_dust)
+
+        # data;
+        dat_d = ascii.read(MB.DIR_TMP + 'bb_dust_obs_' + MB.ID + '.cat')
+        NRbbd = dat_d['col1']
+        xbbd = dat_d['col2']
+        fybbd = dat_d['col3']
+        eybbd = dat_d['col4']
+        exbbd = dat_d['col5']
+        snbbd = fybbd/eybbd
+
+        try:
+            conbbd_hs = (fybbd/eybbd>SNlim)
+            ax1.errorbar(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
+            yerr=eybbd[conbbd_hs]*c/np.square(xbbd[conbbd_hs])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
+            ax1.plot(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
+            '.r', linestyle='', linewidth=0, zorder=4)#, label='Obs.(BB)')
+            ax3t.plot(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
+            '.r', linestyle='', linewidth=0, zorder=4)#, label='Obs.(BB)')
+        except:
+            pass
+
+        try:
+            conebbd_ls = (fybbd/eybbd<=SNlim)
+            ax1.errorbar(xbbd[conebbd_ls], eybbd[conebbd_ls] * c / np.square(xbbd[conebbd_ls]) / d, \
+            yerr=fybbd[conebbd_ls]*0+np.max(fybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d)*0.05, \
+            uplims=eybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d, color='r', linestyle='', linewidth=0.5, zorder=4)
+            ax3t.errorbar(xbbd[conebbd_ls], eybbd[conebbd_ls] * c / np.square(xbbd[conebbd_ls]) / d, \
+            yerr=fybbd[conebbd_ls]*0+np.max(fybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d)*0.05, \
+            uplims=eybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d, color='r', linestyle='', linewidth=0.5, zorder=4)
+        except:
+            pass
+
+
+    #
+    # This is for UVJ color time evolution.
+    #
+    Asum = np.sum(A50[:])
+    alp = .5
+
+    for jj in range(len(age)):
+        ii = int(len(nage) - jj - 1) # from old to young templates.
+        if jj == 0:
+            y0, x0 = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib_all)
+            y0p, x0p = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib)
+
+            ysum = y0
+            ysump = y0p
+            nopt = len(ysump)
+
+            f_50_comp = np.zeros((len(age),len(y0)),'float') 
+            # Keep each component;
+            f_50_comp[ii,:] = y0[:] * c / np.square(x0) / d
+
+            if MB.f_dust:
+                ysump[:] += y0d_cut[:nopt]
+                ysump = np.append(ysump,y0d_cut[nopt:])
+                # Keep each component;
+                f_50_comp_dust = y0d * c / np.square(x0d) / d
+
+            if MB.fneb: 
+                # Only at one age pixel;
+                y0_r, x0_tmp = fnc.get_template_single(Aneb50, AAv[0], ii, Z50[ii], zbes, lib_neb_all, logU=logU50)
+                y0p, x0p = fnc.get_template_single(Aneb50, AAv[0], ii, Z50[ii], zbes, lib_neb, logU=logU50)
+                ysum += y0_r
+                ysump[:nopt] += y0p
+
+        else:
+            y0_r, x0_tmp = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib_all)
+            y0p, x0p = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib)
+            ysum += y0_r
+            ysump[:nopt] += y0p
+
+            f_50_comp[ii,:] = y0_r[:] * c / np.square(x0_tmp) / d
+
+        # The following needs revised.
+        f_uvj = False
+        if f_uvj:
+            if jj == 0:
+                fwuvj = open(MB.DIR_OUT + ID + '_uvj.txt', 'w')
+                fwuvj.write('# age uv vj\n')
+            ysum_wid = ysum * 0
+            for kk in range(0,ii+1,1):
+                tt = int(len(nage) - kk - 1)
+                nn = int(len(nage) - ii - 1)
+
+                nZ = bfnc.Z2NZ(Z50[tt])
+                y0_wid, x0_wid = fnc.open_spec_fits_dir(tt, nZ, nn, AAv[0], zbes, A50[tt])
+                ysum_wid += y0_wid
+
+            lmrest_wid = x0_wid/(1.+zbes)
+            band0 = ['u','v','j']
+            lmconv,fconv = filconv(band0, lmrest_wid, ysum_wid, fil_path) # f0 in fnu
+            fu_t = fconv[0]
+            fv_t = fconv[1]
+            fj_t = fconv[2]
+            uvt = -2.5*log10(fu_t/fv_t)
+            vjt = -2.5*log10(fv_t/fj_t)
+            fwuvj.write('%.2f %.3f %.3f\n'%(age[ii], uvt, vjt))
+            fwuvj.close()
+
+
+    #############
+    # Main result
+    #############
+    conbb_ymax = (xbb>0) & (fybb>0) & (eybb>0) & (fybb/eybb>SNlim)
+    ymax = np.max(fybb[conbb_ymax]*c/np.square(xbb[conbb_ymax])/d) * 1.6
+
+    xboxl = 17000
+    xboxu = 28000
+
+    ax1.set_xlabel('Observed wavelength [$\mathrm{\mu m}$]', fontsize=11)
+    ax1.set_ylabel('$f_\lambda$ [$10^{%d}\mathrm{erg}/\mathrm{s}/\mathrm{cm}^{2}/\mathrm{\AA}$]'%(np.log10(scale)),fontsize=11,labelpad=2)
+
+    x1max = 100000
+    if x1max < np.max(xbb):
+        x1max = np.max(xbb) * 1.5
+    if x1min > np.min(xbb[conbb_ymax]):
+        x1min = np.min(xbb[conbb_ymax]) / 1.5
+
+    xticks = [2500, 5000, 10000, 20000, 40000, 80000, x1max]
+    xlabels= ['0.25', '0.5', '1', '2', '4', '8', '']
+    if MB.f_dust:
+        x1max = 400000
+        xticks = [2500, 5000, 10000, 20000, 40000, 80000, 400000]
+        xlabels= ['0.25', '0.5', '1', '2', '4', '8', '']
+
+    if x1min > 2500:
+        xticks = xticks[1:]
+        xlabels = xlabels[1:]
+
+    ax1.set_xlim(x1min, x1max)
+    ax1.set_xscale('log')
+    if f_plot_filter:
+        scl_yaxis = 0.2
+    else:
+        scl_yaxis = 0.1
+    ax1.set_ylim(-ymax*scl_yaxis,ymax)
+
+    if False:
+        ax1.text(x1min+100,-ymax*0.08,'SNlimit:%.1f'%(SNlim),fontsize=7)
+
+    ax1.set_xticks(xticks)
+    ax1.set_xticklabels(xlabels)
+
+    if False:
+        dely1 = 0.5
+        while (ymax-0)/dely1<1:
+            dely1 /= 2.
+        while (ymax-0)/dely1>4:
+            dely1 *= 2.
+
+        y1ticks = np.arange(0, ymax, dely1)
+        ax1.set_yticks(y1ticks)
+        ax1.set_yticklabels(np.arange(0, ymax, dely1), minor=False)
+        ax1.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+        ax1.yaxis.labelpad = 1.5
+
+    xx = np.arange(100,400000)
+    yy = xx * 0
+    ax1.plot(xx, yy, ls='--', lw=0.5, color='k')
+
+    #############
+    # Plot
+    #############
+    eAAl = np.zeros(len(age),dtype='float')
+    eAAu = np.zeros(len(age),dtype='float')
+    eAMl = np.zeros(len(age),dtype='float')
+    eAMu = np.zeros(len(age),dtype='float')
+    MSsum = np.sum(ms)
+    Asum = np.sum(A50)
+    A50 /= Asum
+    A16 /= Asum
+    A84 /= Asum
+
+    AM50 = A50 * M50 * ms / MSsum
+    CM = M50/np.sum(AM50)
+
+    AM50 = A50 * M50 * ms / MSsum * CM
+    AM16 = A16 * M50 * ms / MSsum * CM
+    AM84 = A84 * M50 * ms / MSsum * CM
+
+    AC50 = A50 * 0 # Cumulative
+    for ii in range(len(A50)):
+        eAAl[ii] = A50[ii] - A16[ii]
+        eAAu[ii] = A84[ii] - A50[ii]
+        eAMl[ii] = AM50[ii] - AM16[ii]
+        eAMu[ii] = AM84[ii] - AM50[ii]
+        AC50[ii] = np.sum(AM50[ii:])
+
+    ################
+    # Lines
+    ################
+    LN = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3', 'O3', 'Mgb', 'Halpha', 'S2L', 'S2H']
+    FLW = np.zeros(len(LN),dtype='int')
+
+    ####################
+    # For cosmology
+    ####################
+    DL = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm #, **cosmo) # Luminositydistance in cm
+    Cons = (4.*np.pi*DL**2/(1.+zbes))
+    if f_grsm:
+        print('This function (write_lines) needs to be revised.')
+        write_lines(ID, zbes, DIR_OUT=MB.DIR_OUT)
+
+
+    ##########################
+    # Zoom in Line regions
+    ##########################
+    if f_grsm:
+        conspec = (NR<10000) #& (fy/ey>1)
+        ax2t.errorbar(xg2, fg2 * c/np.square(xg2)/d, yerr=eg2 * c/np.square(xg2)/d, lw=0.5, color='#DF4E00', zorder=10, alpha=1., label='', capsize=0)
+        ax2t.errorbar(xg1, fg1 * c/np.square(xg1)/d, yerr=eg1 * c/np.square(xg1)/d, lw=0.5, color='g', zorder=10, alpha=1., label='', capsize=0)
+        ax2t.errorbar(xg0, fg0 * c/np.square(xg0)/d, yerr=eg0 * c/np.square(xg0)/d, lw=0.5, linestyle='', color='royalblue', zorder=10, alpha=1., label='', capsize=0)
+
+        xgrism = np.concatenate([xg0,xg1,xg2])
+        fgrism = np.concatenate([fg0,fg1,fg2])
+        egrism = np.concatenate([eg0,eg1,eg2])
+        con4000b = (xgrism/zscl>3400) & (xgrism/zscl<3800) & (fgrism>0) & (egrism>0)
+        con4000r = (xgrism/zscl>4200) & (xgrism/zscl<5000) & (fgrism>0) & (egrism>0)
+        print('Median SN at 3400-3800 is;', np.median((fgrism/egrism)[con4000b]))
+        print('Median SN at 4200-5000 is;', np.median((fgrism/egrism)[con4000r]))
+
+
+    #
+    # From MCMC chain
+    #
+    file = MB.DIR_OUT + 'chain_' + ID + '_corner.cpkl'
+    niter = 0
+    data = loadcpkl(file)
+    try:
+        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
+        burnin = data['burnin']
+        nmc    = data['niter']
+        nwalk  = data['nwalkers']
+        Nburn  = burnin #*20
+        res    = data['chain'][:]
+    except:
+        if verbose: print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
+
+    samples = res
+
+    # Saved template;
+    ytmp = np.zeros((mmax,len(ysum)), dtype='float')
+    ytmp_each = np.zeros((mmax,len(ysum),len(age)), dtype='float')
+    ytmp_nl = np.zeros((mmax,len(ysum)), dtype='float') # no line
+
+    ytmpmax = np.zeros(len(ysum), dtype='float')
+    ytmpmin = np.zeros(len(ysum), dtype='float')
+
+    # MUV;
+    DL      = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm # Luminositydistance in cm
+    DL10    = Mpc_cm/1e6 * 10 # 10pc in cm
+    Fuv     = np.zeros(mmax, dtype='float') # For Muv
+    Fuv28   = np.zeros(mmax, dtype='float') # For Fuv(1500-2800)
+    Lir     = np.zeros(mmax, dtype='float') # For L(8-1000um)
+    UVJ     = np.zeros((mmax,4), dtype='float') # For UVJ color;
+    Cmznu   = 10**((48.6+m0set)/(-2.5)) # Conversion from m0_25 to fnu
+
+    # From random chain;
+    alp=0.02
+    for kk in range(0,mmax,1):
+        nr = np.random.randint(Nburn, len(samples['A%d'%MB.aamin[0]]))
+        try:
+            Av_tmp = samples['Av'][nr]
+        except:
+            Av_tmp = MB.AVFIX
+
+        try:
+            zmc = samples['zmc'][nr]
+        except:
+            zmc = zbes
+
+        for ss in MB.aamin:
+            try:
+                AA_tmp = 10**samples['A'+str(ss)][nr]
+            except:
+                AA_tmp = 0
+                pass
+            try:
+                Ztest = samples['Z'+str(len(age)-1)][nr]
+                ZZ_tmp = samples['Z'+str(ss)][nr]
+            except:
+                try:
+                    ZZ_tmp = samples['Z0'][nr]
+                except:
+                    ZZ_tmp = MB.ZFIX
+
+            if ss == MB.aamin[0]:
+                mod0_tmp, xm_tmp = fnc.get_template_single(AA_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_all)
+                fm_tmp = mod0_tmp.copy()
+                fm_tmp_nl = mod0_tmp.copy()
+                if MB.fneb:
+                    Aneb_tmp = 10**samples['Aneb'][nr]
+                    if not MB.logUFIX == None:
+                        logU_tmp = MB.logUFIX
+                    else:
+                        logU_tmp = samples['logU'][nr]
+                    mod0_tmp, xm_tmp = fnc.get_template_single(Aneb_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_neb_all, logU=logU_tmp)
+                    fm_tmp += mod0_tmp
+                    # Make no emission line template;
+                    mod0_tmp_nl, xm_tmp_nl = fnc.get_template_single(0, Av_tmp, ss, ZZ_tmp, zmc, lib_neb_all, logU=logU_tmp)
+                    fm_tmp_nl += mod0_tmp_nl
+            else:
+                mod0_tmp, xx_tmp = fnc.get_template_single(AA_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_all)
+                fm_tmp += mod0_tmp
+                fm_tmp_nl += mod0_tmp
+
+            # Each;
+            ytmp_each[kk,:,ss] = mod0_tmp[:] * c / np.square(xm_tmp[:]) / d
+
+        #
+        # Dust component;
+        #
+        if MB.f_dust:
+            if kk == 0:
+                par = Parameters()
+                par.add('MDUST',value=samples['MDUST'][nr])
+                try:
+                    par.add('TDUST',value=samples['TDUST'][nr])
+                except:
+                    par.add('TDUST',value=0)
+
+            par['MDUST'].value = samples['MDUST'][nr]
+            if not MB.TDUSTFIX == None:
+                par['TDUST'].value = MB.NTDUST
+            else:
+                par['TDUST'].value = samples['TDUST'][nr]
+                #except:
+                #    par['TDUST'].value = 0
+
+            model_dust, x1_dust = fnc.tmp04_dust(par.valuesdict())
+            if kk == 0:
+                deldt  = (x1_dust[1] - x1_dust[0])
+                x1_tot = np.append(xm_tmp,np.arange(np.max(xm_tmp),np.max(x1_dust)*2,deldt))
+                # Redefine??
+                ytmp = np.zeros((mmax,len(x1_tot)), dtype='float')
+                ytmp_dust = np.zeros((mmax,len(x1_dust)), dtype='float')
+                ytmp_comp = np.zeros((mmax,len(x1_tot)), dtype='float')
+
+            ytmp_dust[kk,:] = model_dust * c/np.square(x1_dust)/d
+            model_tot = np.interp(x1_tot,xx_tmp,fm_tmp) + np.interp(x1_tot,x1_dust,model_dust)
+            model_tot_nl = np.interp(x1_tot,xx_tmp,fm_tmp_nl) + np.interp(x1_tot,x1_dust,model_dust)
+
+            ytmp[kk,:] = model_tot[:] * c/np.square(x1_tot[:])/d
+            ytmp_nl[kk,:] = model_tot_nl[:] * c/np.square(x1_tot[:])/d
+
+        else:
+            x1_tot = xm_tmp
+            ytmp[kk,:] = fm_tmp[:] * c / np.square(xm_tmp[:]) / d
+            ytmp_nl[kk,:] = fm_tmp_nl[:] * c / np.square(xm_tmp[:]) / d
+
+        #
+        # Grism plot + Fuv flux + LIR.
+        #
+        #if f_grsm:
+            #ax2t.plot(x1_tot, ytmp[kk,:], '-', lw=0.5, color='gray', zorder=3., alpha=0.02)
+
+        # Get FUV flux density;
+        Fuv[kk] = get_Fuv(x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)) * (DL**2/(1.+zbes)) / (DL10**2), lmin=1250, lmax=1650)
+        Fuv28[kk] = get_Fuv(x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)) * (4*np.pi*DL**2/(1.+zbes))*Cmznu, lmin=1500, lmax=2800)
+        Lir[kk] = 0
+
+        # Get UVJ Color;
+        lmconv,fconv = filconv_fast(MB.filts_rf, MB.band_rf, x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)))
+        UVJ[kk,0] = -2.5*np.log10(fconv[0]/fconv[2])
+        UVJ[kk,1] = -2.5*np.log10(fconv[1]/fconv[2])
+        UVJ[kk,2] = -2.5*np.log10(fconv[2]/fconv[3])
+        UVJ[kk,3] = -2.5*np.log10(fconv[4]/fconv[3])
+
+        # Do stuff...
+        time.sleep(0.01)
+        # Update Progress Bar
+        printProgressBar(kk, mmax, prefix = 'Progress:', suffix = 'Complete', length = 40)
+
+    #
+    # Plot Median SED;
+    #
+    ytmp16 = np.percentile(ytmp[:,:],16,axis=0)
+    ytmp50 = np.percentile(ytmp[:,:],50,axis=0)
+    ytmp84 = np.percentile(ytmp[:,:],84,axis=0)
+    ytmp16_nl = np.percentile(ytmp_nl[:,:],16,axis=0)
+    ytmp50_nl = np.percentile(ytmp_nl[:,:],50,axis=0)
+    ytmp84_nl = np.percentile(ytmp_nl[:,:],84,axis=0)
+    
+    if MB.f_dust:
+        ytmp_dust50 = np.percentile(ytmp_dust[:,:],50, axis=0)
+
+    #if not f_fill:
+    ax1.fill_between(x1_tot[::nstep_plot], ytmp16[::nstep_plot], ytmp84[::nstep_plot], ls='-', lw=.5, color='gray', zorder=-2, alpha=0.5)
+    ax1.plot(x1_tot[::nstep_plot], ytmp50[::nstep_plot], '-', lw=.5, color='gray', zorder=-1, alpha=1.)
+
+    # For grism;
+    if f_grsm:
+        from astropy.convolution import convolve
+        from .maketmp_filt import get_LSF
+        LSF, _ = get_LSF(MB.inputs, MB.DIR_EXTR, ID, x1_tot[:]/(1.+zbes), c=3e18)
+        try:
+            spec_grsm16 = convolve(ytmp16[:], LSF, boundary='extend')
+            spec_grsm50 = convolve(ytmp50[:], LSF, boundary='extend')
+            spec_grsm84 = convolve(ytmp84[:], LSF, boundary='extend')
+        except:
+            spec_grsm16 = ytmp16[:]
+            spec_grsm50 = ytmp50[:]
+            spec_grsm84 = ytmp84[:]
+
+        if True:
+            ax2t.plot(x1_tot[:], ytmp50, '-', lw=0.5, color='gray', zorder=3., alpha=1.0)
+        else:
+            ax2t.plot(x1_tot[:], spec_grsm50, '-', lw=0.5, color='gray', zorder=3., alpha=1.0)
+        
+    # Attach the data point in MB;
+    MB.sed_wave_obs = xbb
+    MB.sed_flux_obs = fybb * c / np.square(xbb) / d
+    MB.sed_eflux_obs = eybb * c / np.square(xbb) / d
+    # Attach the best SED to MB;
+    MB.sed_wave = x1_tot
+    MB.sed_flux16 = ytmp16
+    MB.sed_flux50 = ytmp50
+    MB.sed_flux84 = ytmp84
+
+    if f_fancyplot:
+        alp_fancy = 0.5
+        #ax1.plot(x1_tot[::nstep_plot], np.percentile(ytmp[:, ::nstep_plot], 50, axis=0), '-', lw=.5, color='gray', zorder=-1, alpha=1.)
+        ysumtmp = ytmp[0, ::nstep_plot] * 0
+        ysumtmp2 = ytmp[:, ::nstep_plot] * 0
+        ysumtmp2_prior = ytmp[0, ::nstep_plot] * 0
+        for ss in range(len(age)):
+            ii = int(len(nage) - ss - 1)
+            # !! Take median after summation;
+            ysumtmp2[:,:len(xm_tmp)] += ytmp_each[:, ::nstep_plot, ii]
+            if f_fill:
+                ax1.fill_between(x1_tot[::nstep_plot], ysumtmp2_prior,  np.percentile(ysumtmp2[:,:], 50, axis=0), linestyle='None', lw=0., color=col[ii], alpha=alp_fancy, zorder=-3)
+            else:
+                ax1.plot(x1_tot[::nstep_plot], np.percentile(ysumtmp2[:, ::nstep_plot], 50, axis=0), linestyle='--', lw=.5, color=col[ii], alpha=alp_fancy, zorder=-3)
+            ysumtmp2_prior[:] = np.percentile(ysumtmp2[:, :], 50, axis=0)
+    elif f_fill:
+        print('f_fancyplot is False. f_fill is set to False.')
+
+    #########################
+    # Calculate non-det chi2
+    # based on Sawick12
+    #########################
+    def func_tmp(xint,eobs,fmodel):
+        int_tmp = np.exp(-0.5 * ((xint-fmodel)/eobs)**2)
+        return int_tmp
+
+    if f_chind:
+        conw = (wht3>0) & (ey>0) & (fy/ey>SNlim)
+    else:
+        conw = (wht3>0) & (ey>0) #& (fy/ey>SNlim)
+
+    #chi2 = sum((np.square(fy-ysump) * np.sqrt(wht3))[conw])
+    try:
+        logf = hdul[1].data['logf'][1]
+        ey_revised = np.sqrt(ey**2+ ysump**2 * np.exp(logf)**2)
+    except:
+        ey_revised = ey
+
+    chi2 = sum((np.square(fy-ysump) / ey_revised)[conw])
+
+    chi_nd = 0.0
+    if f_chind:
+        f_ex = np.zeros(len(fy), 'int')
+        if f_exclude:
+            for ii in range(len(fy)):
+                if x[ii] in x_ex:
+                    f_ex[ii] = 1
+
+        con_up = (ey>0) & (fy/ey<=SNlim) & (f_ex == 0)
+        from scipy import special
+        x_erf = (ey_revised[con_up] - ysump[con_up]) / (np.sqrt(2) * ey_revised[con_up])
+        f_erf = special.erf(x_erf)
+        chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey_revised[con_up] * (1 + f_erf)) )
+
+    # Number of degree;
+    con_nod = (wht3>0) & (ey>0) #& (fy/ey>SNlim)
+    nod = int(len(wht3[con_nod])-ndim_eff)
+
+    print('\n')
+    print('No-of-detection    : %d'%(len(wht3[conw])))
+    print('chi2               : %.2f'%(chi2))
+    if f_chind:
+        print('No-of-non-detection: %d'%(len(ey[con_up])))
+        print('chi2 for non-det   : %.2f'%(- 2 * chi_nd))
+    print('No-of-params       : %d'%(ndim_eff))
+    print('Degrees-of-freedom : %d'%(nod))
+    if nod>0:
+        fin_chi2 = (chi2 - 2 * chi_nd) / nod
+    else:
+        fin_chi2 = -99
+    print('Final chi2/nu      : %.2f'%(fin_chi2))
+
+    #
+    # plot BB model from best template (blue squares)
+    #
+    col_dia = 'blue'
+    if MB.f_dust:
+        ALLFILT = np.append(SFILT,DFILT)
+        #for ii in range(len(x1_tot)):
+        #    print(x1_tot[ii], model_tot[ii]*c/np.square(x1_tot[ii])/d)
+        lbb, fbb, lfwhm = filconv(ALLFILT, x1_tot, ytmp50, DIR_FILT, fw=True)
+        lbb, fbb16, lfwhm = filconv(ALLFILT, x1_tot, ytmp16, DIR_FILT, fw=True)
+        lbb, fbb84, lfwhm = filconv(ALLFILT, x1_tot, ytmp84, DIR_FILT, fw=True)
+
+        ax1.plot(x1_tot, ytmp50, '--', lw=0.5, color='purple', zorder=-1, label='')
+        ax3t.plot(x1_tot, ytmp50, '--', lw=0.5, color='purple', zorder=-1, label='')
+
+        iix = []
+        for ii in range(len(fbb)):
+            iix.append(ii)
+        con_sed = ()
+        ax1.scatter(lbb[iix][con_sed], fbb[iix][con_sed], lw=0.5, color='none', edgecolor=col_dia, zorder=3, alpha=1.0, marker='d', s=50)
+
+        # plot FIR range;
+        ax3t.scatter(lbb, fbb, lw=0.5, color='none', edgecolor=col_dia, \
+        zorder=2, alpha=1.0, marker='d', s=50)
+
+    else:
+        lbb, fbb, lfwhm = filconv(SFILT, x1_tot, ytmp50, DIR_FILT, fw=True, MB=MB, f_regist=False)
+        lbb, fbb16, lfwhm = filconv(SFILT, x1_tot, ytmp16, DIR_FILT, fw=True, MB=MB, f_regist=False)
+        lbb, fbb84, lfwhm = filconv(SFILT, x1_tot, ytmp84, DIR_FILT, fw=True, MB=MB, f_regist=False)
+
+        iix = []
+        for ii in range(len(fbb)):
+            iix.append(np.argmin(np.abs(lbb[ii]-xbb[:])))
+        con_sed = (eybb>0)
+        ax1.scatter(lbb[iix][con_sed], fbb[iix][con_sed], lw=0.5, color='none', edgecolor=col_dia, zorder=3, alpha=1.0, marker='d', s=50)
+
+        if f_plot_resid:
+            conbb_hs = (fybb/eybb>SNlim)
+            axes['B'].scatter(lbb[iix][conbb_hs], ((fybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs], lw=0.5, color='none', edgecolor='r', zorder=3, alpha=1.0, marker='.', s=50)
+            conbb_hs = (fybb/eybb<=SNlim) & (eybb>0)
+            axes['B'].errorbar(lbb[iix][conbb_hs], ((eybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs], yerr=leng,\
+                uplims=((fybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs] * sigma, linestyle='',\
+                color=col_dat, lw=0.5, marker='', ms=4, label='', zorder=4, capsize=1.5)
+            axes['B'].set_xscale(ax1.get_xscale())
+            axes['B'].set_xlim(ax1.get_xlim())
+            axes['B'].set_xticks(ax1.get_xticks())
+            axes['B'].set_xticklabels(ax1.get_xticklabels())
+            axes['B'].set_xlabel(ax1.get_xlabel())
+            xx = np.arange(axes['B'].get_xlim()[0],axes['B'].get_xlim()[1],100)
+            axes['B'].plot(xx,xx*0,linestyle='--',lw=0.5,color='k')
+            axes['B'].set_ylabel('Residual / $\sigma$')
+            axes['A'].set_xlabel('')
+            axes['A'].set_xticks(ax1.get_xticks())
+            axes['A'].set_xticklabels('')
+
+        # Calculate EW, if there is excess band;
+        try:
+            iix2 = []
+            for ii in range(len(fy_ex)):
+                iix2.append(np.argmin(np.abs(lbb[:]-x_ex[ii])))
+
+            # Rest-frame EW;
+            # Note about 16/84 in fbb
+            EW16 = (fy_ex * c / np.square(x_ex) / d - fbb84[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
+            EW50 = (fy_ex * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
+            EW84 = (fy_ex * c / np.square(x_ex) / d - fbb16[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
+
+            EW50_er1 = ((fy_ex-ey_ex) * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
+            EW50_er2 = ((fy_ex+ey_ex) * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
+
+            cnt50 = fbb[iix2]
+            cnt16 = fbb16[iix2]
+            cnt84 = fbb84[iix2]
+ 
+            # Luminosity;
+            #Lsun = 3.839 * 1e33 #erg s-1
+            L16 = EW16 * cnt16 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
+            L50 = EW50 * cnt50 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
+            L84 = EW84 * cnt84 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
+
+            ew_label = []
+            for ii in range(len(fy_ex)):
+                lres = MB.band['%s_lam'%MB.filts[iix2[ii]]][:]
+                fres = MB.band['%s_res'%MB.filts[iix2[ii]]][:]
+                ew_label.append(MB.filts[iix2[ii]])
+
+                print('\n')
+                print('EW016 for', x_ex[ii], 'is %d'%EW16[ii])
+                print('EW050 for', x_ex[ii], 'is %d'%EW50[ii])
+                print('EW084 for', x_ex[ii], 'is %d'%EW84[ii])
+                print('%d_{-%d}^{+%d} , for sed error'%(EW50[ii],EW50[ii]-EW84[ii],EW16[ii]-EW50[ii]))
+                print('Or, %d\pm{%d} , for flux error'%(EW50[ii],EW50[ii]-EW50_er1[ii]))
+        except:
+            pass
+
+
+    if save_sed:
+        fbb16_nu = flamtonu(lbb, fbb16*scale, m0set=m0set)
+        fbb_nu = flamtonu(lbb, fbb*scale, m0set=m0set)
+        fbb84_nu = flamtonu(lbb, fbb84*scale, m0set=m0set)
+
+        # Then save full spectrum;
+        col00  = []
+        col1  = fits.Column(name='wave_model', format='E', unit='AA', array=x1_tot)
+        col00.append(col1)
+        col2  = fits.Column(name='f_model_16', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp16[:])
+        col00.append(col2)
+        col3  = fits.Column(name='f_model_50', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp50[:])
+        col00.append(col3)
+        col4  = fits.Column(name='f_model_84', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp84[:])
+        col00.append(col4)
+        col2  = fits.Column(name='f_model_noline_16', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp16_nl[:])
+        col00.append(col2)
+        col3  = fits.Column(name='f_model_noline_50', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp50_nl[:])
+        col00.append(col3)
+        col4  = fits.Column(name='f_model_noline_84', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp84_nl[:])
+        col00.append(col4)
+
+        # Each component
+        # Stellar
+        col1 = fits.Column(name='wave_model_stel', format='E', unit='AA', array=x0)
+        col00.append(col1)
+        for aa in range(len(age)):
+            col1 = fits.Column(name='f_model_stel_%d'%aa, format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=f_50_comp[aa,:])
+            col00.append(col1)
+        if MB.f_dust:
+            col1 = fits.Column(name='wave_model_dust', format='E', unit='AA', array=x1_dust)
+            col00.append(col1)
+            col1 = fits.Column(name='f_model_dust', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp_dust50)
+            col00.append(col1)
+            
+        # Grism;
+        if f_grsm:
+            col2 = fits.Column(name='f_model_conv_16', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=spec_grsm16)
+            col00.append(col2)
+            col3 = fits.Column(name='f_model_conv_50', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=spec_grsm50)
+            col00.append(col3)
+            col4 = fits.Column(name='f_model_conv_84', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=spec_grsm84)
+            col00.append(col4)
+
+        # BB for dust
+        if MB.f_dust:
+            xbb = np.append(xbb,xbbd)
+            fybb = np.append(fybb,fybbd)
+            eybb = np.append(eybb,eybbd)
+
+        col5 = fits.Column(name='wave_obs', format='E', unit='AA', array=xbb)
+        col00.append(col5)
+        col6 = fits.Column(name='f_obs', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=fybb[:] * c / np.square(xbb[:]) / d)
+        col00.append(col6)
+        col7 = fits.Column(name='e_obs', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=eybb[:] * c / np.square(xbb[:]) / d)
+        col00.append(col7)
+
+        hdr = fits.Header()
+        hdr['redshift'] = zbes
+        hdr['id'] = ID
+        hdr['hierarch isochrone'] = isochrone
+        hdr['library'] = LIBRARY
+        hdr['nimf'] = nimf
+        hdr['scale'] = scale
+
+        try:
+            # Chi square:
+            hdr['chi2'] = chi2
+            hdr['hierarch No-of-effective-data-points'] = len(wht3[conw])
+            hdr['hierarch No-of-nondetectioin'] = len(ey[con_up])
+            hdr['hierarch Chi2-of-nondetection'] = chi_nd
+            hdr['hierarch No-of-params'] = ndim_eff
+            hdr['hierarch Degree-of-freedom']  = nod
+            hdr['hierarch reduced-chi2'] = fin_chi2
+        except:
+            print('Chi seems to be wrong...')
+            pass
+
+        try:
+            # Muv
+            MUV = -2.5 * np.log10(Fuv[:]) + MB.m0set
+            hdr['MUV16'] = -2.5 * np.log10(np.percentile(Fuv[:],16)) + MB.m0set
+            hdr['MUV50'] = -2.5 * np.log10(np.percentile(Fuv[:],50)) + MB.m0set
+            hdr['MUV84'] = -2.5 * np.log10(np.percentile(Fuv[:],84)) + MB.m0set
+
+            # Flam to Fnu
+            hdr['LUV16'] = 10**(-0.4*hdr['MUV16'])
+            hdr['LUV50'] = 10**(-0.4*hdr['MUV50'])
+            hdr['LUV84'] = 10**(-0.4*hdr['MUV84'])
+
+            # Fuv (!= flux of Muv)
+            hdr['FUV16'] = np.percentile(Fuv28[:],16)
+            hdr['FUV50'] = np.percentile(Fuv28[:],50)
+            hdr['FUV84'] = np.percentile(Fuv28[:],84)
+
+            # # LIR
+            # hdr['LIR16'] = np.percentile(Lir[:],16)
+            # hdr['LIR50'] = np.percentile(Lir[:],50)
+            # hdr['LIR84'] = np.percentile(Lir[:],84)
+        except:
+            pass
+
+        # UV beta;
+        from .function import get_uvbeta
+        beta_16 = get_uvbeta(x1_tot, ytmp16, zbes)
+        beta_50 = get_uvbeta(x1_tot, ytmp50, zbes)
+        beta_84 = get_uvbeta(x1_tot, ytmp84, zbes)
+        hdr['UVBETA16'] = beta_16
+        hdr['UVBETA50'] = beta_50
+        hdr['UVBETA84'] = beta_84
+
+        # UVJ
+        try:
+            hdr['uv16'] = np.percentile(UVJ[:,0],16)
+            hdr['uv50'] = np.percentile(UVJ[:,0],50)
+            hdr['uv84'] = np.percentile(UVJ[:,0],84)
+            hdr['bv16'] = np.percentile(UVJ[:,1],16)
+            hdr['bv50'] = np.percentile(UVJ[:,1],50)
+            hdr['bv84'] = np.percentile(UVJ[:,1],84)
+            hdr['vj16'] = np.percentile(UVJ[:,2],16)
+            hdr['vj50'] = np.percentile(UVJ[:,2],50)
+            hdr['vj84'] = np.percentile(UVJ[:,2],84)
+            hdr['zj16'] = np.percentile(UVJ[:,3],16)
+            hdr['zj50'] = np.percentile(UVJ[:,3],50)
+            hdr['zj84'] = np.percentile(UVJ[:,3],84)
+        except:
+            print('\nError when writinf UVJ colors;\n')
+            pass
+
+        # EW;
+        try:
+            for ii in range(len(EW50)):
+                hdr['EW_%s_16'%(ew_label[ii])] = EW16[ii]
+                hdr['EW_%s_50'%(ew_label[ii])] = EW50[ii]
+                hdr['EW_%s_84'%(ew_label[ii])] = EW84[ii]
+                hdr['EW_%s_e1'%(ew_label[ii])] = EW50_er1[ii]
+                hdr['EW_%s_e2'%(ew_label[ii])] = EW50_er2[ii]
+                hdr['HIERARCH cnt_%s_16'%(ew_label[ii])]= cnt16[ii]
+                hdr['HIERARCH cnt_%s_50'%(ew_label[ii])]= cnt50[ii]
+                hdr['HIERARCH cnt_%s_84'%(ew_label[ii])]= cnt84[ii]
+                hdr['L_%s_16'%(ew_label[ii])] = L16[ii]
+                hdr['L_%s_50'%(ew_label[ii])] = L50[ii]
+                hdr['L_%s_84'%(ew_label[ii])] = L84[ii]
+        except:
+            pass
+
+        # Version;
+        import gsf
+        from astropy import units as u
+        hdr['version'] = gsf.__version__
+
+        # Write;
+        colspec = fits.ColDefs(col00)
+        hdu0 = fits.BinTableHDU.from_columns(colspec, header=hdr)
+        hdu0.writeto(MB.DIR_OUT + 'gsf_spec_%s.fits'%(ID), overwrite=True)
+
+        # ASDF;
+        tree_spec = {
+            'id': ID,
+            'redshift': '%.3f'%zbes,
+            'isochrone': '%s'%(isochrone),
+            'library': '%s'%(LIBRARY),
+            'nimf': '%s'%(nimf),
+            'scale': scale,
+            'm0set': MB.m0set,
+            'version_gsf': gsf.__version__
+        }
+        tree_spec['model'] = {}
+        tree_spec['obs'] = {}
+        Cnu_to_Jy = 10**((m0set-23.9)) # to microJy.
+
+        # BB;
+        tree_spec['model'].update({'wave_bb': lbb * u.AA})
+        tree_spec['model'].update({'fnu_bb_16': fbb16_nu * Cnu_to_Jy * u.uJy})
+        tree_spec['model'].update({'fnu_bb_50': fbb_nu * Cnu_to_Jy * u.uJy})
+        tree_spec['model'].update({'fnu_bb_84': fbb84_nu * Cnu_to_Jy * u.uJy})
+        # full spectrum;
+        tree_spec['model'].update({'wave': x1_tot * u.AA})
+        tree_spec['model'].update({'fnu_16': ytmp16 / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
+        tree_spec['model'].update({'fnu_50': ytmp50 / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
+        tree_spec['model'].update({'fnu_84': ytmp84 / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
+
+        # EW;
+        try:
+            for ii in range(len(EW50)):
+                tree_spec['model'].update({'EW_%s_16'%(ew_label[ii]): EW16[ii] * u.AA})
+                tree_spec['model'].update({'EW_%s_50'%(ew_label[ii]): EW50[ii] * u.AA})
+                tree_spec['model'].update({'EW_%s_84'%(ew_label[ii]): EW84[ii] * u.AA})
+                tree_spec['model'].update({'EW_%s_e1'%(ew_label[ii]): EW50_er1[ii] * u.AA})
+                tree_spec['model'].update({'EW_%s_e2'%(ew_label[ii]): EW50_er2[ii] * u.AA})
+                tree_spec['model'].update({'cnt_%s_16'%(ew_label[ii]): cnt16[ii] / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
+                tree_spec['model'].update({'cnt_%s_50'%(ew_label[ii]): cnt50[ii] / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
+                tree_spec['model'].update({'cnt_%s_84'%(ew_label[ii]): cnt84[ii] / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
+                tree_spec['model'].update({'L_%s_16'%(ew_label[ii]): L16[ii] * u.erg / u.s})
+                tree_spec['model'].update({'L_%s_50'%(ew_label[ii]): L50[ii] * u.erg / u.s})
+                tree_spec['model'].update({'L_%s_84'%(ew_label[ii]): L84[ii] * u.erg / u.s})
+        except:
+            pass
+
+        # Each component
+        # Stellar
+        tree_spec['model'].update({'wave_stel': x0 * u.AA})
+        for aa in range(len(age)):
+            tree_spec['model'].update({'fnu_stel_%d'%aa: f_50_comp[aa,:] / (c / np.square(x0[:]) / d) * Cnu_to_Jy * u.uJy})
+        if MB.f_dust:
+            # dust
+            tree_spec['model'].update({'wave_dust': x1_dust * u.AA})
+            tree_spec['model'].update({'fnu_dust': ytmp_dust50 / (c / np.square(x1_dust[:]) / d) * Cnu_to_Jy * u.uJy})
+
+        # Obs BB
+        tree_spec['obs'].update({'wave_bb': xbb * u.AA})
+        tree_spec['obs'].update({'fnu_bb': fybb[:] * Cnu_to_Jy * u.uJy})
+        tree_spec['obs'].update({'enu_bb': eybb[:] * Cnu_to_Jy * u.uJy})
+        # grism:
+        if f_grsm:
+            tree_spec['obs'].update({'fg0': fg0 * Cnu_to_Jy * u.uJy})
+            tree_spec['obs'].update({'eg0': eg0 * Cnu_to_Jy * u.uJy})
+            tree_spec['obs'].update({'wg0': xg0 * u.AA})
+            tree_spec['obs'].update({'fg1': fg1 * Cnu_to_Jy * u.uJy})
+            tree_spec['obs'].update({'eg1': eg1 * Cnu_to_Jy * u.uJy})
+            tree_spec['obs'].update({'wg1': xg1 * u.AA})
+            tree_spec['obs'].update({'fg2': fg2 * Cnu_to_Jy * u.uJy})
+            tree_spec['obs'].update({'eg2': eg2 * Cnu_to_Jy * u.uJy})
+            tree_spec['obs'].update({'wg2': xg2 * u.AA})
+
+        af = asdf.AsdfFile(tree_spec)
+        af.write_to(os.path.join(MB.DIR_OUT, 'gsf_spec_%s.asdf'%(ID)), all_array_compression='zlib')
+
+    #
+    # SED params in plot
+    #
+    if f_label:
+        fs_label = 8
+        fd = fits.open(MB.DIR_OUT + 'SFH_' + ID + '.fits')[0].header
+        if MB.f_dust:
+            label = 'ID: %s\n$z:%.2f$\n$\log M_\mathrm{*}/M_\odot:%.2f$\n$\log M_\mathrm{dust}/M_\odot:%.2f$\n$T_\mathrm{dust}/K:%.1f$\n$\log Z_\mathrm{*}/Z_\odot:%.2f$\n$\log T_\mathrm{*}$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$'\
+            %(ID, zbes, float(fd['Mstel_50']), MD50, TD50, float(fd['Z_MW_50']), float(fd['T_MW_50']), float(fd['AV_50']))#, fin_chi2)
+            ylabel = ymax*0.45
+        else:
+            label = 'ID: %s\n$z:%.2f$\n$\log M_\mathrm{*}/M_\odot:%.2f$\n$\log Z_\mathrm{*}/Z_\odot:%.2f$\n$\log T_\mathrm{*}$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$'\
+            %(ID, zbes, float(fd['Mstel_50']), float(fd['Z_MW_50']), float(fd['T_MW_50']), float(fd['AV_50']))
+            ylabel = ymax*0.25
+
+        if f_grsm:
+            ax1.text(0.02, 0.68, label,\
+            fontsize=fs_label, bbox=dict(facecolor='w', alpha=0.8, lw=1.), zorder=10,
+            ha='left', va='center', transform=ax1.transAxes)
+        else:
+            ax1.text(0.02, 0.68, label,\
+            fontsize=fs_label, bbox=dict(facecolor='w', alpha=0.8, lw=1.), zorder=10,
+            ha='left', va='center', transform=ax1.transAxes)
+
+    ax1.xaxis.labelpad = -3
+
+    if f_grsm:
+        if np.max(xg0)<23000: # E.g. WFC3, NIRISS grisms
+            conlim = (x0>10000) & (x0<25000)
+            xgmin, xgmax = np.min(x0[conlim]),np.max(x0[conlim]), #7500, 17000
+            ax2t.set_xlabel('')
+            ax2t.set_xlim(xgmin, xgmax)
+
+            conaa = (x0>xgmin-50) & (x0<xgmax+50)
+            ymaxzoom = np.max(ysum[conaa]*c/np.square(x0[conaa])/d) * 1.15
+            yminzoom = np.min(ysum[conaa]*c/np.square(x0[conaa])/d) / 1.15
+
+            ax2t.set_ylim(yminzoom, ymaxzoom)
+            ax2t.xaxis.labelpad = -2
+            if xgmax>20000:
+                ax2t.set_xticks([8000, 12000, 16000, 20000, 24000])
+                ax2t.set_xticklabels(['0.8', '1.2', '1.6', '2.0', '2.4'])
+            else:
+                ax2t.set_xticks([8000, 10000, 12000, 14000, 16000])
+                ax2t.set_xticklabels(['0.8', '1.0', '1.2', '1.4', '1.6'])
+        else:
+            conlim = (x0>10000) & (x0<54000) # NIRSPEC spectrum;
+            xgmin, xgmax = np.min(x0[conlim]),np.max(x0[conlim]), #7500, 17000
+            ax2t.set_xlabel('')
+            ax2t.set_xlim(xgmin, xgmax)
+
+            conaa = (x0>xgmin-50) & (x0<xgmax+50)
+            ymaxzoom = np.max(ysum[conaa]*c/np.square(x0[conaa])/d) * 1.15
+            yminzoom = np.min(ysum[conaa]*c/np.square(x0[conaa])/d) / 1.15
+
+            ax2t.set_ylim(yminzoom, ymaxzoom)
+            ax2t.xaxis.labelpad = -2
+            if xgmax>40000:
+                ax2t.set_xticks([8000, 20000, 32000, 44000, 56000])
+                ax2t.set_xticklabels(['0.8', '2.0', '3.2', '4.4', '5.6'])
+            else:
+                ax2t.set_xticks([8000, 20000, 32000, 44000])
+                ax2t.set_xticklabels(['0.8', '2.0', '3.2', '4.4'])
+
+    if MB.f_dust:
+        try:
+            contmp = (x1_tot>10*1e4) #& (fybbd/eybbd>SNlim)
+            y3min, y3max = -.2*np.max((model_tot * c/ np.square(x1_tot) / d)[contmp]), np.max((model_tot * c/ np.square(x1_tot) / d)[contmp])*2.0
+            ax3t.set_ylim(y3min, y3max)
+        except:
+            if verbose:
+                print('y3 limit is not specified.')
+            pass
+        ax3t.set_xlim(1e5, 3e7)
+        ax3t.set_xscale('log')
+        ax3t.set_xticks([100000, 1000000, 10000000])
+        ax3t.set_xticklabels(['10', '100', '1000'])
+
+    ###############
+    # Line name
+    ###############
+    LN0 = ['Mg2', '$NeIV$', '[OII]', 'H$\theta$', 'H$\eta$', 'Ne3?', 'H$\delta$', 'H$\gamma$', 'H$\\beta$', 'O3', 'O3', 'Mgb', 'Halpha', 'S2L', 'S2H']
+    LW0 = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4959, 5007, 5175, 6563, 6717, 6731]
+    fsl = 9 # Fontsize for line
+    if f_grsm:
+        try:
+            for ii in range(len(LW)):
+                ll = np.argmin(np.abs(LW[ii]-LW0[:]))
+
+                if ll == 2 and FLW[ii] == 1: # FLW is the flag for line fitting.
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
+                    ax2t.text(xxl[0]-130, yyl[0]*1.28, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
+
+                elif (ll == 9 and FLW[ii] == 1):
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
+
+                elif (ll == 10 and FLW[ii] == 1):
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
+                    ax2t.text(xxl[0]+40, yyl[0]*0.75, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
+
+                elif FLW[ii] == 1 and (ll == 6 or ll == 7 or ll == 8):
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.2,yminzoom+(ymaxzoom-yminzoom)*0.35, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
+                    ax2t.text(xxl[0]+40, yyl[0]*0.95, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
+
+                elif ll == 6 or ll == 7 or ll == 8:
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.2,yminzoom+(ymaxzoom-yminzoom)*0.35, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color='gray', zorder=1, alpha=1., label='', capsize=0)
+                    ax2t.text(xxl[0]+40, yyl[0]*0.95, '%s'%(LN0[ll]),  color='gray', fontsize=9, rotation=90)
+
+                elif FLW[ii] == 1:
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.7,yminzoom+(ymaxzoom-yminzoom)*.95, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
+                    ax2t.text(xxl[0]+40, yyl[0]*1.25, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
+        except:
+            pass
+
+    # Filters
+    ind_remove = np.where((wht3<=0) | (ey<=0))[0]
+    if f_plot_filter:
+        ax1 = plot_filter(MB, ax1, ymax, scl=scl_yaxis, ind_remove=ind_remove)
+
+    ####################
+    ## Save
+    ####################
+    ax1.legend(loc=1, fontsize=11)
+    if figpdf:
+        fig.savefig(MB.DIR_OUT + 'SPEC_' + ID + '_spec.pdf', dpi=dpi)
+    else:
+        fig.savefig(MB.DIR_OUT + 'SPEC_' + ID + '_spec.png', dpi=dpi)
+
+
+def plot_sed_tau(MB, flim=0.01, fil_path='./', scale=1e-19, f_chind=True, figpdf=False, save_sed=True, 
+    mmax=300, dust_model=0, DIR_TMP='./templates/', f_label=False, f_bbbox=False, verbose=False, f_silence=True, 
+    f_fill=False, f_fancyplot=False, f_Alog=True, dpi=300, f_plot_filter=True, f_plot_resid=False, NRbb_lim=10000):
+    '''
+    Parameters
+    ----------
+    MB.SNlim : float
+        SN limit to show flux or up lim in SED.
+    f_chind : bool
+        If include non-detection in chi2 calculation, using Sawicki12.
+    mmax : int
+        Number of mcmc realization for plot. Not for calculation.
+    f_fancy : bool
+        plot each SED component.
+    f_fill : bool
+        if True, and so is f_fancy, fill each SED component.
+
+    Returns
+    -------
+    plots
+    '''
+
+    from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
+    from mpl_toolkits.axes_grid1.inset_locator import mark_inset
+    from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+    from scipy.optimize import curve_fit
+    from scipy import asarray as ar,exp
+    import matplotlib
+    import scipy.integrate as integrate
+    import scipy.special as special
+    import os.path
+    from astropy.io import ascii
+    import time
+
+    if f_silence:
+        import matplotlib
+        matplotlib.use("Agg")
+
+    def gaus(x,a,x0,sigma):
+        return a*exp(-(x-x0)**2/(2*sigma**2))
+    
+    print('\n### Running plot_sed_tau ###\n')
+
+    lcb = '#4682b4' # line color, blue
+
+    fnc  = MB.fnc
+    bfnc = MB.bfnc
+    ID   = MB.ID
+    Z    = MB.Zall
+    age  = MB.age
+    nage = MB.nage 
+    tau0 = MB.tau0
+    
+    NUM_COLORS = len(age)
+    cm = plt.get_cmap('gist_rainbow')
+    col = [cm(1 - 1.*i/NUM_COLORS) for i in range(NUM_COLORS)]
+
+    nstep_plot = 1
+    if MB.f_bpass:
+        nstep_plot = 30
+
+    SNlim = MB.SNlim
+
+    ################
+    # RF colors.
+    home = os.path.expanduser('~')
+    c = MB.c
+    chimax = 1.
+    m0set = MB.m0set
+    Mpc_cm = MB.Mpc_cm
+    
+    ##################
+    # Fitting Results
+    ##################
+    DIR_FILT = MB.DIR_FILT
+    SFILT = MB.filts
+
+    try:
+        f_err = MB.ferr
+    except:
+        f_err = 0
+
+    ###########################
+    # Open result file
+    ###########################
+    file = MB.DIR_OUT + 'summary_' + ID + '.fits'
+    hdul = fits.open(file) 
+    
+    ndim_eff = hdul[0].header['NDIM']
+    vals = {}
+
+    # Redshift MC
+    zp16  = hdul[1].data['zmc'][0]
+    zp50  = hdul[1].data['zmc'][1]
+    zp84  = hdul[1].data['zmc'][2]
+    vals['zmc'] = zp50
+
+    # Stellar mass MC
+    M16 = hdul[1].data['ms'][0]
+    M50 = hdul[1].data['ms'][1]
+    M84 = hdul[1].data['ms'][2]
+    if verbose:
+        print('Total stellar mass is %.2e'%(M50))
+
+    # Amplitude MC
+    A50 = np.zeros(len(age), dtype='float')
+    A16 = np.zeros(len(age), dtype='float')
+    A84 = np.zeros(len(age), dtype='float')
+    for aa in range(len(age)):
+        A16[aa] = 10**hdul[1].data['A'+str(aa)][0]
+        A50[aa] = 10**hdul[1].data['A'+str(aa)][1]
+        A84[aa] = 10**hdul[1].data['A'+str(aa)][2]
+        vals['A'+str(aa)] = np.log10(A50[aa])
+
+    Asum  = np.sum(A50)
+
+    # TAU MC
+    # AGE MC
+    TAU50 = np.zeros(len(age), dtype='float')
+    TAU16 = np.zeros(len(age), dtype='float')
+    TAU84 = np.zeros(len(age), dtype='float')
+    AGE50 = np.zeros(len(age), dtype='float')
+    AGE16 = np.zeros(len(age), dtype='float')
+    AGE84 = np.zeros(len(age), dtype='float')
+    for aa in range(len(age)):
+        TAU16[aa] = 10**hdul[1].data['TAU'+str(aa)][0]
+        TAU50[aa] = 10**hdul[1].data['TAU'+str(aa)][1]
+        TAU84[aa] = 10**hdul[1].data['TAU'+str(aa)][2]
+        AGE16[aa] = 10**hdul[1].data['AGE'+str(aa)][0]
+        AGE50[aa] = 10**hdul[1].data['AGE'+str(aa)][1]
+        AGE84[aa] = 10**hdul[1].data['AGE'+str(aa)][2]
+        vals['TAU'+str(aa)] = np.log10(TAU50[aa])
+        vals['AGE'+str(aa)] = np.log10(AGE50[aa])
+
+    aa = 0
+    Av16 = hdul[1].data['Av'+str(aa)][0]
+    Av50 = hdul[1].data['Av'+str(aa)][1]
+    Av84 = hdul[1].data['Av'+str(aa)][2]
+    AAv = [Av50]
+    vals['Av'] = Av50
+
+    Z50 = np.zeros(len(age), dtype='float')
+    Z16 = np.zeros(len(age), dtype='float')
+    Z84 = np.zeros(len(age), dtype='float')
+    for aa in range(len(age)):
+        Z16[aa] = hdul[1].data['Z'+str(aa)][0]
+        Z50[aa] = hdul[1].data['Z'+str(aa)][1]
+        Z84[aa] = hdul[1].data['Z'+str(aa)][2]
+        vals['Z'+str(aa)] = Z50[aa]
+
+    # Light weighted Z.
+    ZZ50 = np.sum(Z50*A50)/np.sum(A50)
+
+    # FIR Dust;
+    try:
+        MD16 = hdul[1].data['MDUST'][0]
+        MD50 = hdul[1].data['MDUST'][1]
+        MD84 = hdul[1].data['MDUST'][2]
+        AD16 = hdul[1].data['ADUST'][0]
+        AD50 = hdul[1].data['ADUST'][1]
+        AD84 = hdul[1].data['ADUST'][2]
+        TD16 = hdul[1].data['TDUST'][0]
+        TD50 = hdul[1].data['TDUST'][1]
+        TD84 = hdul[1].data['TDUST'][2]
+        nTD16 = hdul[1].data['nTDUST'][0]
+        nTD50 = hdul[1].data['nTDUST'][1]
+        nTD84 = hdul[1].data['nTDUST'][2]
+        DFILT = MB.inputs['FIR_FILTER'] # filter band string.
+        DFILT = [x.strip() for x in DFILT.split(',')]
+        DFWFILT = fil_fwhm(DFILT, DIR_FILT)
+        if verbose:
+            print('Total dust mass is %.2e'%(MD50))
+        f_dust = True
+    except:
+        f_dust = False
+
+    chi = hdul[1].data['chi'][0]
+    chin = hdul[1].data['chi'][1]
+    fitc = chin
+
+    Cz0 = hdul[0].header['Cz0']
+    Cz1 = hdul[0].header['Cz1']
+    Cz2 = hdul[0].header['Cz2']
+    zbes = zp50 
+    zscl = (1.+zbes)
+
+    ###############################
+    # Data taken from
+    ###############################
+    if MB.f_dust:
+        MB.dict = MB.read_data(Cz0, Cz1, Cz2, zbes, add_fir=True)
+    else:
+        MB.dict = MB.read_data(Cz0, Cz1, Cz2, zbes)
+
+    NR   = MB.dict['NR']
+    x    = MB.dict['x']
+    fy   = MB.dict['fy']
+    ey   = MB.dict['ey']
+    
+    con0 = (NR<1000)
+    xg0  = x[con0]
+    fg0  = fy[con0]
+    eg0  = ey[con0]
+    con1 = (NR>=1000) & (NR<2000) #& (fy/ey>SNlim)
+    xg1  = x[con1]
+    fg1  = fy[con1]
+    eg1  = ey[con1]
+    con2 = (NR>=2000) & (NR<NRbb_lim) #& (fy/ey>SNlim)
+    xg2  = x[con2]
+    fg2  = fy[con2]
+    eg2  = ey[con2]
+    if len(xg0)>0 or len(xg1)>0 or len(xg2)>0:
+        f_grsm = True
+    else:
+        f_grsm = False
+
+    # Weight is set to zero for those no data (ey<0).
+    wht = fy * 0
+    con_wht = (ey>0)
+    wht[con_wht] = 1./np.square(ey[con_wht])
+
+    # BB data points;
+    NRbb = MB.dict['NRbb']
+    xbb  = MB.dict['xbb']
+    fybb = MB.dict['fybb']
+    eybb = MB.dict['eybb']
+    exbb = MB.dict['exbb']
+    snbb = fybb/eybb
+
+    ######################
+    # Weight by line
+    ######################
+    wh0  = 1./np.square(eg0)
+    LW0  = []
+    model = fg0
+    wht3 = check_line_man(fy, x, wht, fy, zbes, LW0)
+
+    ######################
+    # Mass-to-Light ratio.
+    ######################
+    af = MB.af
+    sedpar = af['ML']
+    try:
+        isochrone = af['isochrone']
+        LIBRARY = af['library']
+        nimf = af['nimf']
+    except:
+        isochrone = ''
+        LIBRARY = ''
+        nimf = ''
+
+    #############
+    # Plot.
+    #############
+    # Set the inset.
+    if f_grsm or f_dust:
+        fig = plt.figure(figsize=(7.,3.2))
+        fig.subplots_adjust(top=0.98, bottom=0.16, left=0.1, right=0.99, hspace=0.15, wspace=0.25)
+        ax1 = fig.add_subplot(111)
+        xsize = 0.29
+        ysize = 0.25
+        if f_grsm:
+            ax2t = ax1.inset_axes((1-xsize-0.01,1-ysize-0.01,xsize,ysize))
+        if f_dust:
+            ax3t = ax1.inset_axes((0.7,.35,.28,.25))
+        f_plot_resid = False
+    else:
+        if f_plot_resid:
+            fig_mosaic = """
+            AAAA
+            AAAA
+            BBBB
+            """
+            fig,axes = plt.subplot_mosaic(mosaic=fig_mosaic, figsize=(5.5,4.2))
+            fig.subplots_adjust(top=0.98, bottom=0.16, left=0.1, right=0.99, hspace=0.15, wspace=0.25)
+            ax1 = axes['A']
+        else:
+            fig = plt.figure(figsize=(5.5,2.2))
+            fig.subplots_adjust(top=0.98, bottom=0.16, left=0.1, right=0.99, hspace=0.15, wspace=0.25)
+            ax1 = fig.add_subplot(111)
+
+    # Determine scale here;
+    if scale == None:
+        conbb_hs = (fybb/eybb > SNlim)
+        scale = 10**(int(np.log10(np.nanmax(fybb[conbb_hs] * c / np.square(xbb[conbb_hs])) / MB.d)))
+    d = MB.d * scale
+
+    #######################################
+    # D.Kelson like Box for BB photometry
+    #######################################
+    #col_dat = 'darkgreen'
+    #col_dat = 'tomato'
+    col_dat = 'r'
+    if f_bbbox:
+        for ii in range(len(xbb)):
+            if eybb[ii]<100 and fybb[ii]/eybb[ii]>1:
+                xx = [xbb[ii]-exbb[ii],xbb[ii]-exbb[ii]]
+                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
+                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
+                xx = [xbb[ii]+exbb[ii],xbb[ii]+exbb[ii]]
+                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
+                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
+                xx = [xbb[ii]-exbb[ii],xbb[ii]+exbb[ii]]
+                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d]
+                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
+                xx = [xbb[ii]-exbb[ii],xbb[ii]+exbb[ii]]
+                yy = [(fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
+                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
+    else: # Normal BB plot;
+        # Detection;
+        conbb_hs = (fybb/eybb>SNlim)
+        ax1.errorbar(xbb[conbb_hs], fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d, \
+        yerr=eybb[conbb_hs]*c/np.square(xbb[conbb_hs])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
+        ax1.plot(xbb[conbb_hs], fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d, \
+        marker='.', color=col_dat, linestyle='', linewidth=0, zorder=4, ms=8)#, label='Obs.(BB)')
+
+        try:
+            # For any data removed fron fit (i.e. IRAC excess):
+            data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
+            NR_ex = data_ex['col1']
+        except:
+            NR_ex = []
+
+        # Upperlim;
+        sigma = 1.0
+        leng = np.max(fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d) * 0.05 #0.2
+        conebb_ls = (fybb/eybb<=SNlim) & (eybb>0)
+        
+        for ii in range(len(xbb)):
+            if NR[ii] in NR_ex[:]:
+                conebb_ls[ii] = False
+        
+        ax1.errorbar(xbb[conebb_ls], eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, yerr=leng,\
+            uplims=eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, linestyle='',color=col_dat, marker='', ms=4, label='', zorder=4, capsize=3)
+
+
+    # For any data removed fron fit (i.e. IRAC excess):
+    f_exclude = False
+    try:
+        col_ex = 'lawngreen'
+        #col_ex = 'limegreen'
+        #col_ex = 'r'
+        # Currently, this file is made after FILTER_SKIP;
+        data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
+        x_ex = data_ex['col2']
+        fy_ex = data_ex['col3']
+        ey_ex = data_ex['col4']
+        ex_ex = data_ex['col5']
+
+        ax1.errorbar(x_ex, fy_ex * c / np.square(x_ex) / d, \
+        xerr=ex_ex, yerr=ey_ex*c/np.square(x_ex)/d, color='k', linestyle='', linewidth=0.5, zorder=5)
+        ax1.scatter(x_ex, fy_ex * c / np.square(x_ex) / d, marker='s', color=col_ex, edgecolor='k', zorder=5, s=30)
+        f_exclude = True
+    except:
+        pass
+
+
+    #####################################
+    # Open ascii file and stock to array.
+    MB.lib = fnc.open_spec_fits(fall=0)
+    MB.lib_all = fnc.open_spec_fits(fall=1, orig=True)
+
+    if f_dust:
+        DT0 = float(MB.inputs['TDUST_LOW'])
+        DT1 = float(MB.inputs['TDUST_HIG'])
+        dDT = float(MB.inputs['TDUST_DEL'])
+        Temp = np.arange(DT0,DT1,dDT)
+        MB.lib_dust = fnc.open_spec_dust_fits(fall=0)
+        MB.lib_dust_all = fnc.open_spec_dust_fits(fall=1)
+
+    # FIR dust plot;
+    if f_dust:
+        from lmfit import Parameters
+        par = Parameters()
+        par.add('MDUST',value=AD50)
+        par.add('TDUST',value=nTD50)
+        par.add('zmc',value=zp50)
+
+        y0d, x0d = fnc.tmp04_dust(par.valuesdict())#, zbes, lib_dust_all)
+        y0d_cut, x0d_cut = fnc.tmp04_dust(par.valuesdict())#, zbes, lib_dust)
+        
+        # data;
+        dat_d = ascii.read(MB.DIR_TMP + 'bb_dust_obs_' + MB.ID + '.cat')
+        NRbbd = dat_d['col1']
+        xbbd = dat_d['col2']
+        fybbd = dat_d['col3']
+        eybbd = dat_d['col4']
+        exbbd = dat_d['col5']
+        snbbd = fybbd/eybbd
+
+        try:
+            conbbd_hs = (fybbd/eybbd>SNlim)
+            ax1.errorbar(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
+            yerr=eybbd[conbbd_hs]*c/np.square(xbbd[conbbd_hs])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
+            ax1.plot(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
+            '.r', linestyle='', linewidth=0, zorder=4)#, label='Obs.(BB)')
+            ax3t.plot(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
+            '.r', linestyle='', linewidth=0, zorder=4)#, label='Obs.(BB)')
+        except:
+            pass
+
+        try:
+            conebbd_ls = (fybbd/eybbd<=SNlim)
+            ax1.errorbar(xbbd[conebbd_ls], eybbd[conebbd_ls] * c / np.square(xbbd[conebbd_ls]) / d, \
+            yerr=fybbd[conebbd_ls]*0+np.max(fybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d)*0.05, \
+            uplims=eybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d, color='r', linestyle='', linewidth=0.5, zorder=4)
+            ax3t.errorbar(xbbd[conebbd_ls], eybbd[conebbd_ls] * c / np.square(xbbd[conebbd_ls]) / d, \
+            yerr=fybbd[conebbd_ls]*0+np.max(fybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d)*0.05, \
+            uplims=eybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d, color='r', linestyle='', linewidth=0.5, zorder=4)
+        except:
+            pass
+
+
+    #
+    # This is for UVJ color time evolution.
+    #
+    Asum = np.sum(A50[:])
+    alp = .5
+
+    # Get total templates
+    y0p, x0p = MB.fnc.tmp04(vals, f_val=False, check_bound=False)
+    y0, x0 = MB.fnc.tmp04(vals, f_val=False, check_bound=False, lib_all=True)
+
+    ysum = y0
+    f_50_comp = y0[:] * c / np.square(x0) / d
+
+    ysump = y0p
+    nopt = len(ysump)
+
+    if f_dust:
+        ysump[:] += y0d_cut[:nopt]
+        ysump = np.append(ysump,y0d_cut[nopt:])
+        f_50_comp_dust = y0d * c / np.square(x0d) / d
+
+    # Plot each best fit:
+    vals_each = vals.copy()
+    for aa in range(len(age)):
+        vals_each['A%d'%aa] = -99
+    for aa in range(len(age)):
+        vals_each['A%d'%aa] = vals['A%d'%aa]
+        y0tmp, x0tmp = MB.fnc.tmp04(vals_each, f_val=False, check_bound=False, lib_all=True)
+        if aa == 0:
+            y0keep = y0tmp
+        else:
+            y0keep += y0tmp
+        ax1.plot(x0tmp, y0tmp * c / np.square(x0tmp) / d, linestyle='--', lw=0.5, color=col[aa])
+        vals_each['A%d'%aa] = 0
+
+    # Plot best fit;
+    ax1.plot(x0, f_50_comp, linestyle='-', lw=0.5, color='k')
+
+    #############
+    # Main result
+    #############
+    conbb_ymax = (xbb>0) & (fybb>0) & (eybb>0) & (fybb/eybb>1) # (conbb) &
+    ymax = np.max(fybb[conbb_ymax]*c/np.square(xbb[conbb_ymax])/d) * 1.6
+
+    xboxl = 17000
+    xboxu = 28000
+
+    x1max = 22000
+    if x1max < np.max(xbb[conbb_ymax]):
+        x1max = np.max(xbb[conbb_ymax]) * 1.5
+    ax1.set_xlim(2000, 11000)
+    ax1.set_xscale('log')
+    if f_plot_filter:
+        scl_yaxis = 0.2
+    else:
+        scl_yaxis = 0.1
+    ax1.set_ylim(-ymax*scl_yaxis,ymax)
+    ax1.text(2100,-ymax*0.08,'SNlimit:%.1f'%(SNlim),fontsize=8)
+
+    ax1.set_xlabel('Observed wavelength ($\mathrm{\mu m}$)', fontsize=12)
+    ax1.set_ylabel('Flux ($10^{%d}\mathrm{erg}/\mathrm{s}/\mathrm{cm}^{2}/\mathrm{\AA}$)'%(np.log10(scale)),fontsize=12,labelpad=-2)
+
+    xticks = [2500, 5000, 10000, 20000, 40000, 80000, 110000]
+    xlabels= ['0.25', '0.5', '1', '2', '4', '8', '']
+    if f_dust:
+        xticks = [2500, 5000, 10000, 20000, 40000, 80000, 400000]
+        xlabels= ['0.25', '0.5', '1', '2', '4', '8', '']
+
+    ax1.set_xticks(xticks)
+    ax1.set_xticklabels(xlabels)
+
+
+    dely1 = 0.5
+    while (ymax-0)/dely1<1:
+        dely1 /= 2.
+    while (ymax-0)/dely1>4:
+        dely1 *= 2.
+
+    y1ticks = np.arange(0, ymax, dely1)
+    ax1.set_yticks(y1ticks)
+    ax1.set_yticklabels(np.arange(0, ymax, dely1), minor=False)
+    ax1.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+    ax1.yaxis.labelpad = 1.5
+
+    xx = np.arange(1200,400000)
+    yy = xx * 0
+    ax1.plot(xx, yy, ls='--', lw=0.5, color='k')
+
+    #############
+    # Plot
+    #############
+    ms = np.zeros(len(age), dtype='float')
+    af = MB.af
+    sedpar = af['ML']
+
+    eAAl = np.zeros(len(age),dtype='float')
+    eAAu = np.zeros(len(age),dtype='float')
+    eAMl = np.zeros(len(age),dtype='float')
+    eAMu = np.zeros(len(age),dtype='float')
+    MSsum = np.sum(ms)
+    Asum = np.sum(A50)
+    A50 /= Asum
+    A16 /= Asum
+    A84 /= Asum
+
+    AM50 = A50 * M50 * ms / MSsum
+    CM = M50/np.sum(AM50)
+
+    AM50 = A50 * M50 * ms / MSsum * CM
+    AM16 = A16 * M50 * ms / MSsum * CM
+    AM84 = A84 * M50 * ms / MSsum * CM
+
+    AC50 = A50 * 0 # Cumulative
+    for ii in range(len(A50)):
+        eAAl[ii] = A50[ii] - A16[ii]
+        eAAu[ii] = A84[ii] - A50[ii]
+        eAMl[ii] = AM50[ii] - AM16[ii]
+        eAMu[ii] = AM84[ii] - AM50[ii]
+        AC50[ii] = np.sum(AM50[ii:])
+
+
+    ################
+    # Lines
+    ################
+    LN = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3', 'O3', 'Mgb', 'Halpha', 'S2L', 'S2H']
+    FLW = np.zeros(len(LN),dtype='int')
+
+    ####################
+    # For cosmology
+    ####################
+    DL = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm
+    Cons = (4.*np.pi*DL**2/(1.+zbes))
+
+    if f_grsm:
+        print('This function (write_lines) needs to be revised.')
+        write_lines(ID, zbes, DIR_OUT=MB.DIR_OUT)
+
+
+    ##########################
+    # Zoom in Line regions
+    ##########################
+    if f_grsm:
+        conspec = (NR<10000) #& (fy/ey>1)
+        ax2t.errorbar(xg2, fg2 * c/np.square(xg2)/d, yerr=eg2 * c/np.square(xg2)/d, lw=0.5, color='#DF4E00', zorder=10, alpha=1., label='', capsize=0)
+        ax2t.errorbar(xg1, fg1 * c/np.square(xg1)/d, yerr=eg1 * c/np.square(xg1)/d, lw=0.5, color='g', zorder=10, alpha=1., label='', capsize=0)
+        ax2t.errorbar(xg0, fg0 * c/np.square(xg0)/d, yerr=eg0 * c/np.square(xg0)/d, lw=0.5, linestyle='', color='royalblue', zorder=10, alpha=1., label='', capsize=0)
+
+        xgrism = np.concatenate([xg0,xg1,xg2])
+        fgrism = np.concatenate([fg0,fg1,fg2])
+        egrism = np.concatenate([eg0,eg1,eg2])
+        con4000b = (xgrism/zscl>3400) & (xgrism/zscl<3800) & (fgrism>0) & (egrism>0)
+        con4000r = (xgrism/zscl>4200) & (xgrism/zscl<5000) & (fgrism>0) & (egrism>0)
+        print('Median SN at 3400-3800 is;', np.median((fgrism/egrism)[con4000b]))
+        print('Median SN at 4200-5000 is;', np.median((fgrism/egrism)[con4000r]))
+
+
+    #
+    # From MCMC chain
+    #
+    file = MB.DIR_OUT + 'chain_' + ID + '_corner.cpkl'
+    niter = 0
+    data = loadcpkl(file)
+    ndim = data['ndim'] 
+    burnin = data['burnin']
+    nmc = data['niter']
+    nwalk = data['nwalkers']
+    Nburn = burnin
+    res = data['chain'][:]
+
+    samples = res
+
+    # Saved template;
+    ytmp = np.zeros((mmax,len(ysum)), dtype='float')
+    ytmp_each = np.zeros((mmax,len(ysum),len(age)), dtype='float')
+
+    ytmpmax = np.zeros(len(ysum), dtype='float')
+    ytmpmin = np.zeros(len(ysum), dtype='float')
+
+    # MUV;
+    DL      = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm # Luminositydistance in cm
+    DL10    = Mpc_cm/1e6 * 10 # 10pc in cm
+    Fuv     = np.zeros(mmax, dtype='float') # For Muv
+    Fuv28   = np.zeros(mmax, dtype='float') # For Fuv(1500-2800)
+    Lir     = np.zeros(mmax, dtype='float') # For L(8-1000um)
+    UVJ     = np.zeros((mmax,4), dtype='float') # For UVJ color;
+
+    Cmznu   = 10**((48.6+m0set)/(-2.5)) # Conversion from m0_25 to fnu
+
+    # From random chain;
+    alp=0.02
+    for kk in range(0,mmax,1):
+        nr = np.random.randint(Nburn, len(samples['A%d'%MB.aamin[0]]))
+        try:
+            Av_tmp = samples['Av'][nr]
+        except:
+            Av_tmp = MB.AVFIX
+        vals['Av'] = Av_tmp
+
+        try:
+            zmc = samples['zmc'][nr]
+        except:
+            zmc = zbes
+        vals['zmc'] = zmc
+
+        for ss in MB.aamin:
+            try:
+                AA_tmp = 10**samples['A'+str(ss)][nr]
+            except:
+                AA_tmp = 0
+            vals['A%d'%ss] = np.log10(AA_tmp)
+
+            if ss == 0 or MB.ZEVOL:
+                try:
+                    ZZtmp = samples['Z%d'%ss][nr]
+                except:
+                    ZZtmp = MB.ZFIX
+                vals['Z%d'%ss] = ZZtmp
+
+        mod0_tmp, xm_tmp = fnc.tmp04(vals, f_val=False, check_bound=False, lib_all=True)
+        fm_tmp = mod0_tmp
+
+        if False:
+            # Each;
+            ytmp_each[kk,:,ss] = mod0_tmp[:] * c / np.square(xm_tmp[:]) / d
+            #if kk == 100:
+            #    ax1.plot(xm_tmp[:], ytmp_each[kk,:,ss], color=col[ss], linestyle='--')
+
+        #
+        # Dust component;
+        #
+        if f_dust:
+            if kk == 0:
+                par = Parameters()
+                par.add('MDUST',value=samples['MDUST'][nr])
+                try:
+                    par.add('TDUST',value=samples['TDUST'][nr])
+                except:
+                    par.add('TDUST',value=0)
+
+            par['MDUST'].value = samples['MDUST'][nr]
+            if not MB.TDUSTFIX == None:
+                par['TDUST'].value = MB.NTDUST
+            else:
+                par['TDUST'].value = samples['TDUST'][nr]
+
+            model_dust, x1_dust = fnc.tmp04_dust(par.valuesdict())#, zbes, lib_dust_all)
+            if kk == 0:
+                deldt  = (x1_dust[1] - x1_dust[0])
+                x1_tot = np.append(xm_tmp,np.arange(np.max(xm_tmp),np.max(x1_dust),deldt))
+                # Redefine??
+                ytmp = np.zeros((mmax,len(x1_tot)), dtype='float')
+                ytmp_dust = np.zeros((mmax,len(x1_dust)), dtype='float')
+
+            ytmp_dust[kk,:] = model_dust * c/np.square(x1_dust)/d
+            model_tot = np.interp(x1_tot,xm_tmp,fm_tmp) + np.interp(x1_tot,x1_dust,model_dust)
+
+            ytmp[kk,:] = model_tot[:] * c/np.square(x1_tot[:])/d
+
+        else:
+            x1_tot = xm_tmp
+            ytmp[kk,:] = fm_tmp[:] * c / np.square(xm_tmp[:]) / d
+
+        # plot random sed;
+        plot_mc = True
+        if plot_mc:
+            ax1.plot(x1_tot, ytmp[kk,:], '-', lw=1, color='gray', zorder=-2, alpha=0.02)
+
+        # Grism plot + Fuv flux + LIR.
+        #if f_grsm:
+        #    ax2t.plot(x1_tot, ytmp[kk,:], '-', lw=0.5, color='gray', zorder=3., alpha=0.02)
+
+        if True:
+            # Get FUV flux;
+            Fuv[kk] = get_Fuv(x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)) * (DL**2/(1.+zbes)) / (DL10**2), lmin=1250, lmax=1650)
+            Fuv28[kk] = get_Fuv(x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)) * (4*np.pi*DL**2/(1.+zbes))*Cmznu, lmin=1500, lmax=2800)
+            Lir[kk] = 0
+
+            # Get UVJ Color;
+            lmconv,fconv = filconv_fast(MB.filts_rf, MB.band_rf, x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)))
+            UVJ[kk,0] = -2.5*np.log10(fconv[0]/fconv[2])
+            UVJ[kk,1] = -2.5*np.log10(fconv[1]/fconv[2])
+            UVJ[kk,2] = -2.5*np.log10(fconv[2]/fconv[3])
+            UVJ[kk,3] = -2.5*np.log10(fconv[4]/fconv[3])
+
+        # Do stuff...
+        time.sleep(0.01)
+        # Update Progress Bar
+        printProgressBar(kk, mmax, prefix = 'Progress:', suffix = 'Complete', length = 40)
+
+    print('')
+
+    #
+    # Plot Median SED;
+    #
+    ytmp16 = np.percentile(ytmp[:,:],16,axis=0)
+    ytmp50 = np.percentile(ytmp[:,:],50,axis=0)
+    ytmp84 = np.percentile(ytmp[:,:],84,axis=0)
+    
+    if f_dust:
+        ytmp_dust50 = np.percentile(ytmp_dust[:,:],50, axis=0)
+
+    # For grism;
+    if f_grsm:
+        from astropy.convolution import convolve
+        from .maketmp_filt import get_LSF
+        LSF, _ = get_LSF(MB.inputs, MB.DIR_EXTR, ID, x1_tot[:], c=3e18)
+        spec_grsm16 = convolve(ytmp16[:], LSF, boundary='extend')
+        spec_grsm50 = convolve(ytmp50[:], LSF, boundary='extend')
+        spec_grsm84 = convolve(ytmp84[:], LSF, boundary='extend')
+        if False:#True:
+            ax2t.plot(x1_tot[:], ytmp50, '-', lw=0.5, color='gray', zorder=3., alpha=1.0)
+        else:
+            ax2t.plot(x1_tot[:], spec_grsm50, '-', lw=0.5, color='gray', zorder=3., alpha=1.0)
+
+
+    #if not f_fill:
+    ax1.fill_between(x1_tot[::nstep_plot], ytmp16[::nstep_plot], ytmp84[::nstep_plot], ls='-', lw=.5, color='gray', zorder=-2, alpha=0.5)
+    ax1.plot(x1_tot[::nstep_plot], ytmp50[::nstep_plot], '-', lw=.5, color='gray', zorder=-1, alpha=1.)
+
+    # Attach the data point in MB;
+    MB.sed_wave_obs = xbb
+    MB.sed_flux_obs = fybb * c / np.square(xbb) / d
+    MB.sed_eflux_obs = eybb * c / np.square(xbb) / d
+    # Attach the best SED to MB;
+    MB.sed_wave = x1_tot
+    MB.sed_flux16 = ytmp16
+    MB.sed_flux50 = ytmp50
+    MB.sed_flux84 = ytmp84
+
+
+    #########################
+    # Calculate non-det chi2
+    # based on Sawick12
+    #########################
+    #chi2,fin_chi2 = get_chi2(fy, ey, wht3, ysump, ndim_eff, SNlim=1.0, f_chind=f_chind, f_exclude=f_exclude, xbb=xbb, x_ex=x_ex)
+    def func_tmp(xint,eobs,fmodel):
+        int_tmp = np.exp(-0.5 * ((xint-fmodel)/eobs)**2)
+        return int_tmp
+
+    if f_chind:
+        conw = (wht3>0) & (ey>0) & (fy/ey>SNlim)
+    else:
+        conw = (wht3>0) & (ey>0)
+
+    chi2 = sum((np.square(fy-ysump) * np.sqrt(wht3))[conw])
+
+    chi_nd = 0.0
+    if f_chind:
+        f_ex = np.zeros(len(fy), 'int')
+        if f_exclude:
+            for ii in range(len(fy)):
+                if x[ii] in x_ex:
+                    f_ex[ii] = 1
+
+        con_up = (ey>0) & (fy/ey<=SNlim) & (f_ex == 0)
+        from scipy import special
+        x_erf = (ey[con_up] - ysump[con_up]) / (np.sqrt(2) * ey[con_up])
+        f_erf = special.erf(x_erf)
+        chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey[con_up] * (1 + f_erf)) )
+
+    # Number of degree;
+    con_nod = (wht3>0) & (ey>0) #& (fy/ey>SNlim)
+    if MB.ferr:
+        ndim_eff -= 1
+        
+    nod = int(len(wht3[con_nod])-ndim_eff)
+
+    if nod>0:
+        fin_chi2 = (chi2 - 2 * chi_nd) / nod
+    else:
+        fin_chi2 = -99
+
+    if f_chind:
+        conw = (wht3>0) & (ey>0) & (fy/ey>SNlim)
+        con_up = (ey>0) & (fy/ey<=SNlim) & (f_ex == 0)
+    else:
+        conw = (wht3>0) & (ey>0)
+
+    # Print results;
+    print('\n')
+    print('No-of-detection    : %d'%(len(wht3[conw])))
+    print('chi2               : %.2f'%(chi2))
+    if f_chind:
+        print('No-of-non-detection: %d'%(len(ey[con_up])))
+        print('chi2 for non-det   : %.2f'%(- 2 * chi_nd))
+    print('No-of-params       : %d'%(ndim_eff))
+    print('Degrees-of-freedom : %d'%(nod))
+    print('Final chi2/nu      : %.2f'%(fin_chi2))
+
+
+    if False:
+        from lmfit import Model, Parameters, minimize, fit_report, Minimizer
+        from .posterior_flexible import Post
+        class_post = Post(MB)
+        residual = class_post.residual
+        MB.set_param()
+        fit_params = MB.fit_params #Parameters()
+        for key in vals.keys():
+            try:
+                fit_params[key].value=vals[key]
+            except:
+                pass
+        out_tmp = minimize(residual, fit_params, args=(fy, ey, wht3, False), method='differential_evolution') # nelder is the most efficient.
+        csq = out_tmp.chisqr
+        rcsq = out_tmp.redchi
+        print(csq, rcsq)
+
+    #
+    # plot BB model from best template (blue squares)
+    #
+    col_dia = 'blue'
+    if f_dust:
+        ALLFILT = np.append(SFILT,DFILT)
+        #for ii in range(len(x1_tot)):
+        #    print(x1_tot[ii], model_tot[ii]*c/np.square(x1_tot[ii])/d)
+        lbb, fbb, lfwhm = filconv(ALLFILT, x1_tot, ytmp50, DIR_FILT, fw=True)
+        lbb, fbb16, lfwhm = filconv(ALLFILT, x1_tot, ytmp16, DIR_FILT, fw=True)
+        lbb, fbb84, lfwhm = filconv(ALLFILT, x1_tot, ytmp84, DIR_FILT, fw=True)
+
+        ax1.plot(x1_tot, ytmp50, '--', lw=0.5, color='purple', zorder=-1, label='')
+        ax3t.plot(x1_tot, ytmp50, '--', lw=0.5, color='purple', zorder=-1, label='')
+
+        iix = []
+        for ii in range(len(fbb)):
+            iix.append(ii)
+        con_sed = ()
+        ax1.scatter(lbb[iix][con_sed], fbb[iix][con_sed], lw=0.5, color='none', edgecolor=col_dia, zorder=3, alpha=1.0, marker='d', s=50)
+
+        # plot FIR range;
+        ax3t.scatter(lbb, fbb, lw=0.5, color='none', edgecolor=col_dia, \
+        zorder=2, alpha=1.0, marker='d', s=50)
+
+    else:
+        lbb, fbb, lfwhm = filconv(SFILT, x1_tot, ytmp50, DIR_FILT, fw=True, MB=MB, f_regist=False)
+        lbb, fbb16, lfwhm = filconv(SFILT, x1_tot, ytmp16, DIR_FILT, fw=True, MB=MB, f_regist=False)
+        lbb, fbb84, lfwhm = filconv(SFILT, x1_tot, ytmp84, DIR_FILT, fw=True, MB=MB, f_regist=False)
+
+        iix = []
+        for ii in range(len(fbb)):
+            iix.append(np.argmin(np.abs(lbb[ii]-xbb[:])))
+        con_sed = (eybb>0)
+        ax1.scatter(lbb[iix][con_sed], fbb[iix][con_sed], lw=0.5, color='none', edgecolor=col_dia, zorder=3, alpha=1.0, marker='d', s=50)
+
+        if f_plot_resid:
+            conbb_hs = (fybb/eybb>SNlim)
+            axes['B'].scatter(lbb[iix][conbb_hs], ((fybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs], lw=0.5, color='none', edgecolor='r', zorder=3, alpha=1.0, marker='.', s=50)
+            conbb_hs = (fybb/eybb<=SNlim) & (eybb>0)
+            axes['B'].errorbar(lbb[iix][conbb_hs], ((eybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs], yerr=leng,\
+                uplims=((fybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs] * sigma, linestyle='',\
+                color=col_dat, lw=0.5, marker='', ms=4, label='', zorder=4, capsize=1.5)
+            axes['B'].set_xscale(ax1.get_xscale())
+            axes['B'].set_xlim(ax1.get_xlim())
+            axes['B'].set_xticks(ax1.get_xticks())
+            axes['B'].set_xticklabels(ax1.get_xticklabels())
+            axes['B'].set_xlabel(ax1.get_xlabel())
+            xx = np.arange(axes['B'].get_xlim()[0],axes['B'].get_xlim()[1],100)
+            axes['B'].plot(xx,xx*0,linestyle='--',lw=0.5,color='k')
+            axes['B'].set_ylabel('Residual / $\sigma$')
+            axes['A'].set_xlabel('')
+            axes['A'].set_xticks(ax1.get_xticks())
+            axes['A'].set_xticklabels('')
+
+        # Calculate EW, if there is excess band;
+        try:
+            iix2 = []
+            for ii in range(len(fy_ex)):
+                iix2.append(np.argmin(np.abs(lbb[:]-x_ex[ii])))
+
+            # Rest-frame EW;
+            # Note about 16/84 in fbb
+            EW16 = (fy_ex * c / np.square(x_ex) / d - fbb84[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
+            EW50 = (fy_ex * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
+            EW84 = (fy_ex * c / np.square(x_ex) / d - fbb16[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
+
+            EW50_er1 = ((fy_ex-ey_ex) * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
+            EW50_er2 = ((fy_ex+ey_ex) * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
+
+            cnt50 = fbb[iix2]
+            cnt16 = fbb16[iix2]
+            cnt84 = fbb84[iix2]
+ 
+            # Luminosity;
+            #Lsun = 3.839 * 1e33 #erg s-1
+            L16 = EW16 * cnt16 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
+            L50 = EW50 * cnt50 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
+            L84 = EW84 * cnt84 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
+
+            ew_label = []
+            for ii in range(len(fy_ex)):
+                lres = MB.band['%s_lam'%MB.filts[iix2[ii]]][:]
+                fres = MB.band['%s_res'%MB.filts[iix2[ii]]][:]
+                ew_label.append(MB.filts[iix2[ii]])
+
+                print('\n')
+                print('EW016 for', x_ex[ii], 'is %d'%EW16[ii])
+                print('EW050 for', x_ex[ii], 'is %d'%EW50[ii])
+                print('EW084 for', x_ex[ii], 'is %d'%EW84[ii])
+                print('%d_{-%d}^{+%d} , for sed error'%(EW50[ii],EW50[ii]-EW84[ii],EW16[ii]-EW50[ii]))
+                print('Or, %d\pm{%d} , for flux error'%(EW50[ii],EW50[ii]-EW50_er1[ii]))
+        except:
+            print('\nEW calculation; Failed.\n')
+            pass
+
+
+    if save_sed:
+        fbb16_nu = flamtonu(lbb, fbb16*scale, m0set=m0set)
+        fbb_nu = flamtonu(lbb, fbb*scale, m0set=m0set)
+        fbb84_nu = flamtonu(lbb, fbb84*scale, m0set=m0set)
+
+        # Then save full spectrum;
+        col00  = []
+        col1  = fits.Column(name='wave_model', format='E', unit='AA', array=x1_tot)
+        col00.append(col1)
+        col2  = fits.Column(name='f_model_16', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp16[:])
+        col00.append(col2)
+        col3  = fits.Column(name='f_model_50', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp50[:])
+        col00.append(col3)
+        col4  = fits.Column(name='f_model_84', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp84[:])
+        col00.append(col4)
+
+        f_sed_each = False
+        if f_sed_each:
+            # Each component
+            # Stellar
+            col1 = fits.Column(name='wave_model_stel', format='E', unit='AA', array=x0)
+            col00.append(col1)
+            for aa in range(len(age)):
+                col1 = fits.Column(name='f_model_stel_%d'%aa, format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=f_50_comp[aa,:])
+                col00.append(col1)
+            if f_dust:
+                col1 = fits.Column(name='wave_model_dust', format='E', unit='AA', array=x1_dust)
+                col00.append(col1)
+                col1 = fits.Column(name='f_model_dust', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp_dust50)
+                col00.append(col1)
+            
+        # BB for dust
+        if f_dust:
+            xbb = np.append(xbb,xbbd)
+            fybb = np.append(fybb,fybbd)
+            eybb = np.append(eybb,eybbd)
+
+        col5  = fits.Column(name='wave_obs', format='E', unit='AA', array=xbb)
+        col00.append(col5)
+        col6  = fits.Column(name='f_obs', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=fybb[:] * c / np.square(xbb[:]) / d)
+        col00.append(col6)
+        col7  = fits.Column(name='e_obs', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=eybb[:] * c / np.square(xbb[:]) / d)
+        col00.append(col7)
+
+        hdr = fits.Header()
+        hdr['redshift'] = zbes
+        hdr['id'] = ID
+        hdr['hierarch isochrone'] = isochrone
+        hdr['library'] = LIBRARY
+        hdr['nimf'] = nimf
+        hdr['scale'] = scale
+
+        try:
+            # Chi square:
+            hdr['chi2'] = chi2
+            hdr['hierarch No-of-effective-data-points'] = len(wht3[conw])
+            hdr['hierarch No-of-nondetectioin'] = len(ey[con_up])
+            hdr['hierarch Chi2-of-nondetection'] = chi_nd
+            hdr['hierarch No-of-params'] = ndim_eff
+            hdr['hierarch Degree-of-freedom']  = nod
+            hdr['hierarch reduced-chi2'] = fin_chi2
+        except:
+            print('Chi seems to be wrong...')
+            pass
+
+        try:
+            # Muv
+            MUV = -2.5 * np.log10(Fuv[:]) + MB.m0set
+            hdr['MUV16'] = -2.5 * np.log10(np.percentile(Fuv[:],16)) + MB.m0set
+            hdr['MUV50'] = -2.5 * np.log10(np.percentile(Fuv[:],50)) + MB.m0set
+            hdr['MUV84'] = -2.5 * np.log10(np.percentile(Fuv[:],84)) + MB.m0set
+
+            # Fuv (!= flux of Muv)
+            hdr['FUV16'] = np.percentile(Fuv28[:],16)
+            hdr['FUV50'] = np.percentile(Fuv28[:],50)
+            hdr['FUV84'] = np.percentile(Fuv28[:],84)
+
+            # LIR
+            hdr['LIR16'] = np.percentile(Lir[:],16)
+            hdr['LIR50'] = np.percentile(Lir[:],50)
+            hdr['LIR84'] = np.percentile(Lir[:],84)
+        except:
+            pass
+
+        # UV beta;
+        from .function import get_uvbeta
+        beta_16 = get_uvbeta(x1_tot, ytmp16, zbes)
+        beta_50 = get_uvbeta(x1_tot, ytmp50, zbes)
+        beta_84 = get_uvbeta(x1_tot, ytmp84, zbes)
+        hdr['UVBETA16'] = beta_16
+        hdr['UVBETA50'] = beta_50
+        hdr['UVBETA84'] = beta_84
+
+        # UVJ
+        try:
+            hdr['uv16'] = np.percentile(UVJ[:,0],16)
+            hdr['uv50'] = np.percentile(UVJ[:,0],50)
+            hdr['uv84'] = np.percentile(UVJ[:,0],84)
+            hdr['bv16'] = np.percentile(UVJ[:,1],16)
+            hdr['bv50'] = np.percentile(UVJ[:,1],50)
+            hdr['bv84'] = np.percentile(UVJ[:,1],84)
+            hdr['vj16'] = np.percentile(UVJ[:,2],16)
+            hdr['vj50'] = np.percentile(UVJ[:,2],50)
+            hdr['vj84'] = np.percentile(UVJ[:,2],84)
+            hdr['zj16'] = np.percentile(UVJ[:,3],16)
+            hdr['zj50'] = np.percentile(UVJ[:,3],50)
+            hdr['zj84'] = np.percentile(UVJ[:,3],84)
+        except:
+            print('\nError when writinf UVJ colors;\n')
+            pass
+
+        # EW;
+        try:
+            for ii in range(len(EW50)):
+                hdr['EW_%s_16'%(ew_label[ii])] = EW16[ii]
+                hdr['EW_%s_50'%(ew_label[ii])] = EW50[ii]
+                hdr['EW_%s_84'%(ew_label[ii])] = EW84[ii]
+                hdr['EW_%s_e1'%(ew_label[ii])] = EW50_er1[ii]
+                hdr['EW_%s_e2'%(ew_label[ii])] = EW50_er2[ii]
+                hdr['HIERARCH cnt_%s_16'%(ew_label[ii])]= cnt16[ii]
+                hdr['HIERARCH cnt_%s_50'%(ew_label[ii])]= cnt50[ii]
+                hdr['HIERARCH cnt_%s_84'%(ew_label[ii])]= cnt84[ii]
+                hdr['L_%s_16'%(ew_label[ii])] = L16[ii]
+                hdr['L_%s_50'%(ew_label[ii])] = L50[ii]
+                hdr['L_%s_84'%(ew_label[ii])] = L84[ii]
+        except:
+            pass
+
+        # Version;
+        import gsf
+        hdr['version'] = gsf.__version__
+
+        # Write;
+        colspec = fits.ColDefs(col00)
+        hdu0 = fits.BinTableHDU.from_columns(colspec, header=hdr)
+        hdu0.writeto(MB.DIR_OUT + 'gsf_spec_%s.fits'%(ID), overwrite=True)
+
+        # ASDF;
+        tree_spec = {
+            'id': ID,
+            'redshift': '%.3f'%zbes,
+            'isochrone': '%s'%(isochrone),
+            'library': '%s'%(LIBRARY),
+            'nimf': '%s'%(nimf),
+            'scale': scale,
+            'version_gsf': gsf.__version__
+        }
+
+        # BB;
+        tree_spec.update({'wave': lbb})
+        tree_spec.update({'fnu_16': fbb16_nu})
+        tree_spec.update({'fnu_50': fbb_nu})
+        tree_spec.update({'fnu_84': fbb84_nu})
+        # full spectrum;
+        tree_spec.update({'wave_model': x1_tot})
+        tree_spec.update({'f_model_16': ytmp16})
+        tree_spec.update({'f_model_50': ytmp50})
+        tree_spec.update({'f_model_84': ytmp84})
+
+        # EW;
+        try:
+            for ii in range(len(EW50)):
+                tree_spec.update({'EW_%s_16'%(ew_label[ii]): EW16[ii]})
+                tree_spec.update({'EW_%s_50'%(ew_label[ii]): EW50[ii]})
+                tree_spec.update({'EW_%s_84'%(ew_label[ii]): EW84[ii]})
+                tree_spec.update({'EW_%s_e1'%(ew_label[ii]): EW50_er1[ii]})
+                tree_spec.update({'EW_%s_e2'%(ew_label[ii]): EW50_er2[ii]})
+                tree_spec.update({'cnt_%s_16'%(ew_label[ii]): cnt16[ii]})
+                tree_spec.update({'cnt_%s_50'%(ew_label[ii]): cnt50[ii]})
+                tree_spec.update({'cnt_%s_84'%(ew_label[ii]): cnt84[ii]})
+                tree_spec.update({'L_%s_16'%(ew_label[ii]): L16[ii]})
+                tree_spec.update({'L_%s_50'%(ew_label[ii]): L50[ii]})
+                tree_spec.update({'L_%s_84'%(ew_label[ii]): L84[ii]})
+        except:
+            pass
+
+        # Each component
+        # Stellar
+        tree_spec.update({'wave_model_stel': x0})
+
+        if f_sed_each:
+            for aa in range(len(age)):
+                tree_spec.update({'f_model_stel_%d'%aa: f_50_comp[aa,:]})
+
+        if f_dust:
+            # dust
+            tree_spec.update({'wave_model_dust': x1_dust})
+            tree_spec.update({'f_model_dust': ytmp_dust50})            
+        # BB for dust
+        tree_spec.update({'wave_obs': xbb})
+        tree_spec.update({'f_obs': fybb[:] * c / np.square(xbb[:]) / d})
+        tree_spec.update({'e_obs': eybb[:] * c / np.square(xbb[:]) / d})
+        # grism:
+        if f_grsm:
+            tree_spec.update({'fg0_obs': fg0 * c/np.square(xg0)/d})
+            tree_spec.update({'eg0_obs': eg0 * c/np.square(xg0)/d})
+            tree_spec.update({'wg0_obs': xg0})
+            tree_spec.update({'fg1_obs': fg1 * c/np.square(xg1)/d})
+            tree_spec.update({'eg1_obs': eg1 * c/np.square(xg1)/d})
+            tree_spec.update({'wg1_obs': xg1})
+            tree_spec.update({'fg2_obs': fg2 * c/np.square(xg2)/d})
+            tree_spec.update({'eg2_obs': eg2 * c/np.square(xg2)/d})
+            tree_spec.update({'wg2_obs': xg2})
+
+        af = asdf.AsdfFile(tree_spec)
+        af.write_to(MB.DIR_OUT + 'gsf_spec_%s.asdf'%(ID), all_array_compression='zlib')
+
+    #
+    # SED params in plot
+    #
+    if f_label:
+        fd = fits.open(MB.DIR_OUT + 'SFH_' + ID + '.fits')[0].header
+        if f_dust:
+            label = 'ID: %s\n$z:%.2f$\n$\log M_*/M_\odot:%.2f$\n$\log M_\mathrm{dust}/M_\odot:%.2f$\n$T_\mathrm{dust}/K:%.1f$\n$\log Z_*/Z_\odot:%.2f$\n$\log T_0$/Gyr$:%.2f$\n$\log \\tau$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$\n$\\chi^2/\\nu:%.2f$'\
+            %(ID, zbes, float(fd['Mstel_50']), MD50, TD50, float(fd['Z_MW_50']), float(fd['T_MW_50']), float(fd['TAU_50']), float(fd['AV_50']), fin_chi2)
+            ylabel = ymax*0.45
+        else:
+            label = 'ID: %s\n$z:%.2f$\n$\log M_*/M_\odot:%.2f$\n$\log Z_*/Z_\odot:%.2f$\n$\log T_0$/Gyr$:%.2f$\n$\log \\tau$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$\n$\\chi^2/\\nu:%.2f$'\
+            %(ID, zbes, float(fd['Mstel_50']), float(fd['Z_MW_50']), float(fd['T_MW_50']), float(fd['TAU_50']), float(fd['AV_50']), fin_chi2)
+            ylabel = ymax*0.4
+
+        ax1.text(2200, ylabel, label,\
+        fontsize=7, bbox=dict(facecolor='w', alpha=0.7), zorder=10)
+        
+    #######################################
+    ax1.xaxis.labelpad = -3
+    if f_grsm:
+        conlim = (x0>10000) & (x0<25000)
+        xgmin, xgmax = np.min(x0[conlim]),np.max(x0[conlim]), #7500, 17000
+        ax2t.set_xlabel('')
+        ax2t.set_xlim(xgmin, xgmax)
+
+        conaa = (x0>xgmin-50) & (x0<xgmax+50)
+        ymaxzoom = np.max(ysum[conaa]*c/np.square(x0[conaa])/d) * 1.15
+        yminzoom = np.min(ysum[conaa]*c/np.square(x0[conaa])/d) / 1.15
+
+        ax2t.set_ylim(yminzoom, ymaxzoom)
+        ax2t.xaxis.labelpad = -2
+        if xgmax>20000:
+            ax2t.set_xticks([8000, 12000, 16000, 20000, 24000])
+            ax2t.set_xticklabels(['0.8', '1.2', '1.6', '2.0', '2.4'])
+        else:
+            ax2t.set_xticks([8000, 10000, 12000, 14000, 16000])
+            ax2t.set_xticklabels(['0.8', '1.0', '1.2', '1.4', '1.6'])
+
+    if f_dust:
+        try:
+            contmp = (x1_tot>10*1e4)
+            y3min, y3max = -.2*np.max((model_tot * c/ np.square(x1_tot) / d)[contmp]), np.max((model_tot * c/ np.square(x1_tot) / d)[contmp])*2.0
+            ax3t.set_ylim(y3min, y3max)
+        except:
+            if verbose:
+                print('y3 limit is not specified.')
+            pass
+        ax3t.set_xlim(1e5, 3e7)
+        ax3t.set_xscale('log')
+        ax3t.set_xticks([100000, 1000000, 10000000])
+        ax3t.set_xticklabels(['10', '100', '1000'])
+
+    ###############
+    # Line name
+    ###############
+    LN0 = ['Mg2', '$NeIV$', '[OII]', 'H$\theta$', 'H$\eta$', 'Ne3?', 'H$\delta$', 'H$\gamma$', 'H$\\beta$', 'O3', 'O3', 'Mgb', 'Halpha', 'S2L', 'S2H']
+    LW0 = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4959, 5007, 5175, 6563, 6717, 6731]
+    fsl = 9 # Fontsize for line
+    if f_grsm:
+        try:
+            for ii in range(len(LW)):
+                ll = np.argmin(np.abs(LW[ii]-LW0[:]))
+
+                if ll == 2 and FLW[ii] == 1: 
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
+                    ax2t.text(xxl[0]-130, yyl[0]*1.28, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
+
+                elif (ll == 9 and FLW[ii] == 1):
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
+
+                elif (ll == 10 and FLW[ii] == 1):
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
+                    ax2t.text(xxl[0]+40, yyl[0]*0.75, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
+
+                elif FLW[ii] == 1 and (ll == 6 or ll == 7 or ll == 8):
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.2,yminzoom+(ymaxzoom-yminzoom)*0.35, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
+                    ax2t.text(xxl[0]+40, yyl[0]*0.95, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
+
+                elif ll == 6 or ll == 7 or ll == 8:
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.2,yminzoom+(ymaxzoom-yminzoom)*0.35, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color='gray', zorder=1, alpha=1., label='', capsize=0)
+                    ax2t.text(xxl[0]+40, yyl[0]*0.95, '%s'%(LN0[ll]),  color='gray', fontsize=9, rotation=90)
+
+                elif FLW[ii] == 1:
+                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.7,yminzoom+(ymaxzoom-yminzoom)*.95, 0.01)
+                    xxl = yyl * 0 + LW0[ll]
+                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
+                    ax2t.text(xxl[0]+40, yyl[0]*1.25, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
+        except:
+            pass
+
+    # Filters
+    ind_remove = np.where((wht3<=0) | (ey<=0))[0]
+    if f_plot_filter:
+        ax1 = plot_filter(MB, ax1, ymax, scl=scl_yaxis, ind_remove=ind_remove)
+
+    ####################
+    ## Save
+    ####################
+    ax1.legend(loc=1, fontsize=11)
+    if figpdf:
+        fig.savefig(MB.DIR_OUT + 'SPEC_' + ID + '_spec.pdf', dpi=dpi)
+    else:
+        fig.savefig(MB.DIR_OUT + 'SPEC_' + ID + '_spec.png', dpi=dpi)
+
+
+def plot_filter(MB, ax, ymax, scl=0.3, cmap='gist_rainbow', alp=0.4, ind_remove=[]):
+    '''
+    Add filter response curve to ax1.
+
+    '''
+    NUM_COLORS = len(MB.filts)
+    cm = plt.get_cmap(cmap)
+    cols = [cm(1 - 1.*i/NUM_COLORS) for i in range(NUM_COLORS)]
+
+    wavecen = []
+    for ii,filt in enumerate(MB.filts):
+        wave = MB.band['%s_lam'%filt]
+        flux = MB.band['%s_res'%filt]
+        #wavecen.append(np.median(wave * flux)/np.median(flux))
+        con = (flux/flux.max()>0.1)
+        wavecen.append(np.min(wave[con]))
+    wavecen = np.asarray(wavecen)
+    wavecen_sort = np.sort(wavecen)
+
+    for ii,filt in enumerate(MB.filts):
+        iix = np.argmin(np.abs(wavecen_sort[:]-wavecen[ii]))
+        col = cols[iix]
+        wave = MB.band['%s_lam'%filt]
+        flux = MB.band['%s_res'%filt]
+        if ii in ind_remove:
+            continue
+        ax.plot(wave, ((flux / np.max(flux))*0.8 - 1) * ymax * scl, linestyle='-', color='k', lw=0.2)
+        ax.fill_between(wave, (wave*0 - ymax)*scl, ((flux / np.max(flux))*0.8 - 1) * ymax * scl, linestyle='-', lw=0, color=col, alpha=alp)
+
+    return ax
+
+
+def plot_corner_physparam_summary(MB, fig=None, out_ind=0, DIR_OUT='./', mmax=1000, TMIN=0.0001, tau_lim=0.01, f_plot_filter=True, 
+    scale=1e-19, NRbb_lim=10000):
+    '''
+    Purpose
+    -------
+    For summary. In the same format as plot_corner_physparam_frame.
+
+    Notes
+    -----
+    Tau model not supported.
+    '''
+
+    col = ['violet', 'indigo', 'b', 'lightblue', 'lightgreen', 'g', 'orange', 'coral', 'r', 'darkred']#, 'k']
+    import matplotlib
+    import matplotlib.cm as cm
+    import scipy.stats as stats
+
+    nage = MB.nage 
+    fnc  = MB.fnc 
+    bfnc = MB.bfnc 
+    ID   = MB.ID
+    Z    = MB.Zall
+    age  = MB.age
+    c = MB.c
+
+    tau0 = MB.tau0 
+    dust_model = MB.dust_model
+    DIR_TMP = MB.DIR_TMP
+
+    Txmax = np.max(age) + 1.0 
+
+    ###########################
+    # Open result file
+    ###########################
+    lib = fnc.open_spec_fits(fall=0)
+    lib_all = fnc.open_spec_fits(fall=1, orig=True)
+
+    file = MB.DIR_OUT + 'summary_' + ID + '.fits'
+    hdul = fits.open(file) # open a FITS file
+
+    # Redshift MC
+    zp50  = hdul[1].data['zmc'][1]
+    zp16  = hdul[1].data['zmc'][0]
+    zp84  = hdul[1].data['zmc'][2]
+
+    M50 = hdul[1].data['ms'][1]
+    M16 = hdul[1].data['ms'][0]
+    M84 = hdul[1].data['ms'][2]
+    print('Total stellar mass is %.2e'%(M50))
+
+    A50 = np.zeros(len(age), dtype='float')
+    A16 = np.zeros(len(age), dtype='float')
+    A84 = np.zeros(len(age), dtype='float')
+    for aa in range(len(age)):
+        A50[aa] = hdul[1].data['A'+str(aa)][1]
+        A16[aa] = hdul[1].data['A'+str(aa)][0]
+        A84[aa] = hdul[1].data['A'+str(aa)][2]
+
+    Asum = np.sum(A50)
+    aa = 0
+
+    Av16 = hdul[1].data['Av'+str(aa)][0]
+    Av50 = hdul[1].data['Av'+str(aa)][1]
+    Av84 = hdul[1].data['Av'+str(aa)][2]
+
+    Z50  = np.zeros(len(age), dtype='float')
+    Z16  = np.zeros(len(age), dtype='float')
+    Z84  = np.zeros(len(age), dtype='float')
+
+    NZbest = np.zeros(len(age), dtype='int')
+    for aa in range(len(age)):
+        Z50[aa] = hdul[1].data['Z'+str(aa)][1]
+        Z16[aa] = hdul[1].data['Z'+str(aa)][0]
+        Z84[aa] = hdul[1].data['Z'+str(aa)][2]
+        #NZbest[aa]= bfnc.Z2NZ(Z50[aa])
+
+    ZZ50 = np.sum(Z50*A50)/np.sum(A50) # Light weighted Z.
+    chi = hdul[1].data['chi'][0]
+    chin = hdul[1].data['chi'][1]
+    fitc = chin
+    Cz0 = hdul[0].header['Cz0']
+    Cz1 = hdul[0].header['Cz1']
+    Cz2 = hdul[0].header['Cz2']
+    zbes = hdul[0].header['z']
+    zscl = (1.+zbes)
+
+    # plot Configuration
+    if MB.fzmc == 1:
+        Par = ['$\log M_*/M_\odot$', '$\log T_*$/Gyr', '$A_V$/mag', '$\log Z_* / Z_\odot$', '$z$']
+    else:
+        Par = ['$\log M_*/M_\odot$', '$\log T_*$/Gyr', '$A_V$/mag', '$\log Z_* / Z_\odot$']
+
+    K = len(Par) # No of params.
+    factor = 2.0           # size of one side of one panel
+    lbdim  = 0.5 * factor   # size of left/bottom margin
+    trdim  = 0.2 * factor   # size of top/right margin
+    whspace= 0.02         # w/hspace size
+    plotdim= factor * K + factor * (K - 1.) * whspace
+    dim    = lbdim + plotdim + trdim
+    sclfig = 0.7
+
+
+    # Format the figure.
+    fig, axes = plt.subplots(K, K, figsize=(dim*sclfig*2, dim*sclfig))
+    # Format the figure.
+    lb = lbdim / dim
+    tr = (lbdim + plotdim) / dim
+    #fig.subplots_adjust(left=lb*1.06, bottom=lb*.9, right=tr, top=tr*.99,
+    fig.subplots_adjust(left=0.5, bottom=lb*.9, right=tr, top=tr*.99,
+                        wspace=whspace, hspace=whspace)
+
+    # For spec plot
+    ax0 = fig.add_axes([0.05,0.73,0.37,0.23])
+    ax1 = fig.add_axes([0.05,0.40,0.37,0.23])
+    ax2 = fig.add_axes([0.05,0.07,0.37,0.23])
+
+    if MB.f_dust:
+        MB.dict = MB.read_data(MB.Cz0, MB.Cz1, MB.Cz2, MB.zgal, add_fir=True)
+    else:
+        MB.dict = MB.read_data(MB.Cz0, MB.Cz1, MB.Cz2, MB.zgal)
+
+    # Get data points;
+    NRbb = MB.dict['NRbb']
+    xbb = MB.dict['xbb'] 
+    fybb = MB.dict['fybb']
+    eybb = MB.dict['eybb']
+    exbb = MB.dict['exbb']
+    snbb = fybb/eybb
+
+    # Get spec data points;
+    dat = np.loadtxt(DIR_TMP + 'spec_obs_' + ID + '.cat', comments='#')
+    NR  = dat[:,0]
+    x   = dat[:,1]
+    fy00 = dat[:,2]
+    ey00 = dat[:,3]
+
+    con0 = (NR<1000) #& (fy/ey>SNlim)
+    xg0  = x[con0]
+    fg0  = fy00[con0] * Cz0
+    eg0  = ey00[con0] * Cz0
+    con1 = (NR>=1000) & (NR<2000) #& (fy/ey>SNlim)
+    xg1  = x[con1]
+    fg1  = fy00[con1] * Cz1
+    eg1  = ey00[con1] * Cz1
+    con2 = (NR>=2000) & (NR<NRbb_lim) #& (fy/ey>SNlim)
+    xg2  = x[con2]
+    fg2  = fy00[con2] * Cz2
+    eg2  = ey00[con2] * Cz2
+
+    con_bb = (NR>=NRbb_lim)
+    xg_bb = x[con_bb]
+    fg_bb = fy00[con_bb]
+    eg_bb = ey00[con_bb]
+
+    fy01 = np.append(fg0,fg1)
+    ey01 = np.append(eg0,eg1)
+    fy02 = np.append(fy01,fg2)
+    ey02 = np.append(ey01,eg2)
+
+    fy = np.append(fy01,fg_bb)
+    ey = np.append(ey01,eg_bb)
+    wht = 1./np.square(ey)
+
+    # Determine scale here;
+    if scale == None:
+        conbb_hs = (fybb/eybb > SNlim)
+        scale = 10**(int(np.log10(np.nanmax(fybb[conbb_hs] * c / np.square(xbb[conbb_hs])) / MB.d)))
+    d = MB.d * scale
+
+    # BB photometry
+    conspec = (NR<NRbb_lim)
+    sigma = 1.
+    conbb = (fybb/eybb > sigma)
+    ax0.errorbar(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, yerr=eybb[conbb]*c/np.square(xbb[conbb])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
+    ax0.plot(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, '.r', ms=10, linestyle='', linewidth=0, zorder=4)
+
+    conebb_ls = (fybb/eybb <= sigma)
+    if len(fybb[conebb_ls])>0:
+        leng = np.max(fybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d) * 0.05
+        ax0.errorbar(xbb[conebb_ls], eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, yerr=leng,\
+            uplims=eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, linestyle='',color='r', marker='', ms=4, label='', zorder=4, capsize=3)
+    
+    ####################
+    # MCMC corner plot.
+    ####################
+    file  = MB.DIR_OUT + 'chain_' + ID + '_corner.cpkl'
+    niter = 0
+    data  = loadcpkl(file)
+
+    try:
+        ndim = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
+        burnin = data['burnin']
+        nmc = data['niter']
+        nwalk = data['nwalkers']
+        Nburn = burnin
+        samples = data['chain'][:]
+    except:
+        if verbose: print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
+
+    af = MB.af
+    sedpar = af['ML']
+
+    getcmap = matplotlib.cm.get_cmap('jet')
+    nc = np.arange(0, nmc, 1)
+    col = getcmap((nc-0)/(nmc-0))
+
+    Ntmp = np.zeros(mmax, dtype='float')
+    lmtmp= np.zeros(mmax, dtype='float')
+    Avtmp= np.zeros(mmax, dtype='float')
+    Ztmp = np.zeros(mmax, dtype='float')
+    Ttmp = np.zeros(mmax, dtype='float')
+    ACtmp= np.zeros(mmax, dtype='float')
+    redshifttmp = np.zeros(mmax, dtype='float')
+
+    # Time bin
+    Tuni = MB.cosmo.age(zbes).value
+    Tuni0 = (Tuni - age[:])
+    delT  = np.zeros(len(age),dtype='float')
+    delTl = np.zeros(len(age),dtype='float')
+    delTu = np.zeros(len(age),dtype='float')
+
+    if len(age) == 1:
+        for aa in range(len(age)):
+            try:
+                tau_ssp = float(MB.inputs['TAU_SSP'])
+            except:
+                tau_ssp = tau_lim
+            delTl[aa] = tau_ssp/2
+            delTu[aa] = tau_ssp/2
+            delT[aa]  = delTu[aa] + delTl[aa]
+    else:
+        for aa in range(len(age)):
+            if aa == 0:
+                delTl[aa] = age[aa]
+                delTu[aa] = (age[aa+1]-age[aa])/2.
+                delT[aa]  = delTu[aa] + delTl[aa]
+            elif Tuni < age[aa]:
+                delTl[aa] = (age[aa]-age[aa-1])/2.
+                delTu[aa] = 10.
+                delT[aa]  = delTu[aa] + delTl[aa]
+            elif aa == len(age)-1:
+                delTl[aa] = (age[aa]-age[aa-1])/2.
+                delTu[aa] = Tuni - age[aa]
+                delT[aa]  = delTu[aa] + delTl[aa]
+            else:
+                delTl[aa] = (age[aa]-age[aa-1])/2.
+                delTu[aa] = (age[aa+1]-age[aa])/2.
+                delT[aa]  = delTu[aa] + delTl[aa]
+
+            if delT[aa] < tau_lim:
+                # This is because fsps has the minimum tau = tau_lim
+                delT[aa] = tau_lim
+
+
+    delT[:]  *= 1e9 # Gyr to yr
+    delTl[:] *= 1e9 # Gyr to yr
+    delTu[:] *= 1e9 # Gyr to yr
+
+    files = [] # For gif animation
+    SFmax = 0
+    Tsmin = 0
+    Tsmax = 0
+    Zsmin = 0
+    Zsmax = 0
+    AMtmp = 0
+    AMtmp16 = 0
+    AMtmp84 = 0
+
+    for ii in range(len(age)):
+
+        if aa == 0 or MB.ZEVOL:
+            ZZ_tmp = Z50[ii]
+            ZZ_tmp16 = Z16[ii]
+            ZZ_tmp84 = Z84[ii]
+        else:
+            ZZ_tmp = Z50[0]
+            ZZ_tmp16 = Z16[0]
+            ZZ_tmp84 = Z84[0]
+        
+        try:
+            AA_tmp = 10**np.max(samples['A'+str(ii)][:])
+            AA_tmp84 = 10**np.percentile(samples['A'+str(ii)][:],95)
+            AA_tmp16 = 10**np.percentile(samples['A'+str(ii)][:],5)
+        except:
+            AA_tmp = 0
+            AA_tmp84 = 0
+            AA_tmp16 = 0
+
+        nZtmp = bfnc.Z2NZ(ZZ_tmp)
+        mslist = sedpar['ML_'+str(nZtmp)][ii]
+
+        AMtmp16 += mslist*AA_tmp16
+        AMtmp84 += mslist*AA_tmp84
+
+        Tsmax += age[ii] * AA_tmp84 * mslist
+        Tsmin += age[ii] * AA_tmp16 * mslist
+
+        Zsmax += 10**ZZ_tmp84 * AA_tmp84 * mslist
+        Zsmin += 10**ZZ_tmp16 * AA_tmp16 * mslist
+
+        SFtmp = AA_tmp * mslist / delT[ii]
+        if SFtmp > SFmax:
+            SFmax = SFtmp
+    if SFmax > 0.5e4:
+        SFmax = 0.5e4
+
+    delM = np.log10(M84) - np.log10(M16)
+    if MB.fzmc == 1:
+        NPARmin = [np.log10(M16)-.1, np.log10(Tsmin/AMtmp16)-0.1, Av16-0.1, np.log10(Zsmin/AMtmp16)-0.2, np.percentile(samples['zmc'],1)-0.1]
+        NPARmax = [np.log10(M84)+.1, np.log10(Tsmax/AMtmp84)+0.2, Av84+0.1, np.log10(Zsmax/AMtmp84)+0.2, np.percentile(samples['zmc'],99)+0.1]
+    else:
+        NPARmin = [np.log10(M16)-.1, np.log10(Tsmin/AMtmp16)-0.1, Av16-0.1, np.log10(Zsmin/AMtmp16)-0.2]
+        NPARmax = [np.log10(M84)+.1, np.log10(Tsmax/AMtmp84)+0.2, Av84+0.1, np.log10(Zsmax/AMtmp84)+0.2]
+
+    # For redshift
+    if zbes<2:
+        zred  = [zbes, 2, 3, 6]
+        zredl = ['$z$', 2, 3, 6]
+    elif zbes<2.5:
+        zred  = [zbes, 2.5, 3, 6]
+        zredl = ['$z$', 2.5, 3, 6]
+    elif zbes<3.:
+        zred  = [zbes, 3, 6]
+        zredl = ['$z$', 3, 6]
+    elif zbes<6:
+        zred  = [zbes, 6]
+        zredl = ['$z$', 6]
+    else:
+        zred  = [zbes, 12]
+        zredl = ['$z$', 12]
+
+    Tzz = np.zeros(len(zred), dtype='float')
+    for zz in range(len(zred)):
+        Tzz[zz] = (Tuni - MB.cosmo.age(zred[zz]).value) #/ cc.Gyr_s
+        if Tzz[zz] < TMIN:
+            Tzz[zz] = TMIN
+
+
+    def density_estimation(m1, m2):
+        xmin, xmax = np.min(m1), np.max(m1)
+        ymin, ymax = np.min(m2), np.max(m2)
+        X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
+        positions = np.vstack([X.ravel(), Y.ravel()])
+        values = np.vstack([m1, m2])
+        kernel = stats.gaussian_kde(values)
+        Z = np.reshape(kernel(positions).T, X.shape)
+        return X, Y, Z
+
+
+    for kk in range(0,mmax,1):
+        nr = np.random.randint(len(samples))
+        try:
+            Avtmp[kk] = samples['Av'][nr]
+        except:
+            Avtmp[kk] = MB.AVFIX
+
+        ZMM = np.zeros((len(age)), dtype='float') # Mass weighted Z.
+        ZM = np.zeros((len(age)), dtype='float') # Light weighted T.
+        ZC = np.zeros((len(age)), dtype='float') # Light weighted T.
+        SF = np.zeros((len(age)), dtype='float') # SFR
+        AM = np.zeros((len(age)), dtype='float') # Light weighted T.
+        II0 = nage
+
+        for ss in range(len(age)):
+            ii = int(len(II0) - ss - 1) # from old to young templates.
+            
+            try:
+                AA_tmp = 10**samples['A'+str(ii)][nr]
+            except:
+                AA_tmp = 0
+                pass
+
+            try:
+                ZZ_tmp = samples['Z'+str(ii)][nr]
+            except:
+                try:
+                    ZZ_tmp = samples['Z0'][nr]
+                except:
+                    ZZ_tmp = MB.ZFIX
+
+            nZtmp = bfnc.Z2NZ(ZZ_tmp)
+            mslist = sedpar['ML_'+str(nZtmp)][ii]
+            lmtmp[kk] += AA_tmp * mslist
+            Ztmp[kk] += (10 ** ZZ_tmp) * AA_tmp * mslist
+            Ttmp[kk] += age[ii] * AA_tmp * mslist
+            ACtmp[kk] += AA_tmp * mslist
+            if MB.fzmc == 1:
+                redshifttmp[kk] = samples['zmc'][nr]
+
+            AM[ii] = AA_tmp * mslist
+            SF[ii] = AA_tmp * mslist / delT[ii]
+            ZM[ii] = ZZ_tmp
+            ZMM[ii]= (10 ** ZZ_tmp) * AA_tmp * mslist
+
+            # SED
+            flim = 0.05
+            if ss == 0:
+                y0, x0 = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all)
+                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib)
+                ysump = y0p #* 1e18
+                ysum = y0  #* 1e18
+                if AA_tmp/Asum > flim:
+                    ax0.plot(x0, y0 * c/ np.square(x0) / d, '--', lw=.1, color=col[ii], zorder=-1, label='', alpha=0.01)
+            else:
+                y0_r, x0_tmp = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all)
+                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib)
+                ysump += y0p  #* 1e18
+                ysum += y0_r #* 1e18
+                if AA_tmp/Asum > flim:
+                    ax0.plot(x0, y0_r * c/ np.square(x0) / d, '--', lw=.1, color=col[ii], zorder=-1, label='', alpha=0.01)
+
+        for ss in range(len(age)):
+            ii = ss # from old to young templates.
+            AC = np.sum(AM[ss:])
+            if AC > 0:
+                ZC[ss] = np.log10(np.sum(ZMM[ss:])/AC)
+            else:
+                ZC[ss] = -99
+
+        # Plot Total
+        ax0.plot(x0, ysum * c/ np.square(x0) / d, '-', lw=.1, color='gray', zorder=-1, label='', alpha=0.01)
+
+        if len(age)==1:
+            ax1.plot(age[:], SF[:], marker='.', linestyle='-', lw=.1, color='k', zorder=-1, label='', alpha=0.01)
+            ax2.plot(age[:], ZC[:], marker='.', linestyle='-', lw=.1, color='k', zorder=-1, label='', alpha=0.01)
+        else:
+            ax1.plot(age[:], SF[:], marker='', linestyle='-', lw=.1, color='k', zorder=-1, label='', alpha=0.01)
+            ax2.plot(age[:], ZC[:], marker='', linestyle='-', lw=.1, color='k', zorder=-1, label='', alpha=0.01)
+
+        # Get ymax
+        if f_plot_filter:
+            scl_yaxis = 0.2
+        else:
+            scl_yaxis = 0
+
+        ymax_bb = np.max(fybb[conbb] * c / np.square(xbb[conbb]) / d) * 1.10
+        ymax_temp = np.max(ysum * c/ np.square(x0) / d) * 1.10
+        ymax = np.max([ymax_bb, ymax_temp])
+
+        # Convert into log
+        Ztmp[kk] /= ACtmp[kk]
+        Ttmp[kk] /= ACtmp[kk]
+        Ntmp[kk] = kk
+
+        lmtmp[kk] = np.log10(lmtmp[kk])
+        Ztmp[kk] = np.log10(Ztmp[kk])
+        Ttmp[kk] = np.log10(Ttmp[kk])
+
+        if MB.fzmc == 1:
+            NPAR = [lmtmp[:kk+1], Ttmp[:kk+1], Avtmp[:kk+1], Ztmp[:kk+1], redshifttmp[:kk+1]]
+        else:
+            NPAR = [lmtmp[:kk+1], Ttmp[:kk+1], Avtmp[:kk+1], Ztmp[:kk+1]]
+
+        if kk == mmax-1:
+            # Histogram
+            for i, x in enumerate(Par):
+                ax = axes[i, i]
+                x1min, x1max = NPARmin[i], NPARmax[i]
+                nbin = 50
+                binwidth1 = (x1max-x1min)/nbin
+                bins1 = np.arange(x1min, x1max + binwidth1, binwidth1)
+                n, bins, patches = ax.hist(NPAR[i], bins=bins1, orientation='vertical', color='b', histtype='stepfilled', alpha=0.6)
+                yy = np.arange(0,np.max(n)*1.3,1)
+
+                try:
+                    ax.plot(yy*0+np.percentile(NPAR[i],16), yy, linestyle='--', color='gray', lw=1)
+                    ax.plot(yy*0+np.percentile(NPAR[i],84), yy, linestyle='--', color='gray', lw=1)
+                    ax.plot(yy*0+np.percentile(NPAR[i],50), yy, linestyle='-', color='gray', lw=1)
+
+                    ax.text(np.percentile(NPAR[i],16), np.max(yy)*1.02, '%.2f'%(np.percentile(NPAR[i],16)), fontsize=9)
+                    ax.text(np.percentile(NPAR[i],50), np.max(yy)*1.02, '%.2f'%(np.percentile(NPAR[i],50)), fontsize=9)
+                    ax.text(np.percentile(NPAR[i],84), np.max(yy)*1.02, '%.2f'%(np.percentile(NPAR[i],84)), fontsize=9)
+                except:
+                    print('Failed at i,x=',i,x)
+
+                ax.set_xlim(x1min, x1max)
+                ax.set_yticklabels([])
+                if i == K-1:
+                    ax.set_xlabel('%s'%(Par[i]), fontsize=12)
+                if i < K-1:
+                    ax.set_xticklabels([])
+
+        # Scatter and contour
+        for i, x in enumerate(Par):
+            for j, y in enumerate(Par):
+                if i > j:
+                    ax = axes[i, j]
+                    ax.scatter(NPAR[j], NPAR[i], c='b', s=1, marker='.', alpha=0.01)
+                    ax.set_xlabel('%s'%(Par[j]), fontsize=12)
+
+                    if kk == mmax-1:
+                        try:
+                            Xcont, Ycont, Zcont = density_estimation(NPAR[j], NPAR[i])
+                            mZ = np.max(Zcont)
+                            ax.contour(Xcont, Ycont, Zcont, levels=[0.68*mZ, 0.95*mZ, 0.99*mZ], linewidths=[0.8,0.5,0.3], colors='orange')
+                        except:
+                            print('Error occurs when density estimation. Maybe because some params are fixed.')
+                            pass
+
+                    x1min, x1max = NPARmin[j], NPARmax[j]
+                    y1min, y1max = NPARmin[i], NPARmax[i]
+                    ax.set_xlim(x1min, x1max)
+                    ax.set_ylim(y1min, y1max)
+
+                    if j==0:
+                        ax.set_ylabel('%s'%(Par[i]), fontsize=12)
+                    if j>0:
+                        ax.set_yticklabels([])
+                    if i<K-1:
+                        ax.set_xticklabels([])
+                    if i == 2:
+                        ax.yaxis.labelpad = 5.
+
+                if i < j:
+                    ax = axes[i, j]
+                    ax.set_xticklabels([])
+                    ax.set_yticklabels([])
+                    ax.set_frame_on(False)
+                    ax.set_xticks([])
+                    ax.set_yticks([])
+
+                if i == j:
+                    ax = axes[i, j]
+                    ax.set_yticklabels([])
+                    if i == K-1:
+                        ax.set_xlabel('%s'%(Par[i]), fontsize=12)
+                    if i < K-1:
+                        ax.set_xticklabels([])
+
+
+        if kk%10 == 0 and out_ind == 1:
+            fname = MB.DIR_OUT +  + '%d.png' % kk
+            print('Saving frame', fname)
+            plt.savefig(fname, dpi=200)
+            files.append(fname)
+
+    # For the last one
+    ax0.plot(xg0, fg0 * c / np.square(xg0) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='royalblue', label='')
+    ax0.plot(xg1, fg1 * c / np.square(xg1) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='g', label='')
+    ax0.plot(xg2, fg2 * c / np.square(xg2) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='#DF4E00', label='')
+
+    ax0.set_xlim(2200, 88000)
+    ax0.set_xscale('log')
+    ax0.set_ylim(-ymax * scl_yaxis, ymax)
+    ax0.set_xlabel('Observed wavelength ($\mathrm{\mu m}$)', fontsize=14)
+    ax0.set_ylabel('Flux ($10^{%d}\mathrm{erg}/\mathrm{s}/\mathrm{cm}^{2}/\mathrm{\AA}$)'%(np.log10(scale)),fontsize=12,labelpad=-2)
+    ax1.set_xlabel('$t_\mathrm{lookback}$ (Gyr)', fontsize=12)
+    ax1.set_ylabel('$\dot{M_*}/M_\odot$yr$^{-1}$', fontsize=12)
+    ax1.set_xlim(np.min(age)*0.8, Txmax)
+    ax1.set_ylim(0, SFmax)
+    ax1.set_xscale('log')
+    ax2.set_xlabel('$t_\mathrm{lookback}$ (Gyr)', fontsize=12)
+    ax2.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
+    ax2.set_xlim(np.min(age)*0.8, Txmax)
+    if round(np.min(Z),2) == round(np.max(Z),2):
+        ax2.set_ylim(-0.8, 0.5)
+    else:
+        ax2.set_ylim(np.min(Z)-0.05, np.max(Z)+0.05)
+    ax2.set_xscale('log')
+    #ax2.yaxis.labelpad = -5
+
+    ax1t = ax1.twiny()
+    ax2t = ax2.twiny()
+    ax1t.set_xlim(0.008, Txmax)
+    ax1t.set_xscale('log')
+
+    ax1t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
+    ax1t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
+    #ax1t.set_xticklabels(zredl[:])
+    #ax1t.set_xticks(Tzz[:])
+
+    ax1t.tick_params(axis='x', labelcolor='k')
+    ax1t.xaxis.set_ticks_position('none')
+    ax1.plot(Tzz, Tzz*0+SFmax, marker='|', color='k', ms=3, linestyle='None')
+
+    ax2t.set_xlim(0.008, Txmax)
+    ax2t.set_xscale('log')
+    #ax2t.set_xticklabels(zredl[:])
+    #ax2t.set_xticks(Tzz[:])
+    ax2t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
+    ax2t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
+
+    ax2t.tick_params(axis='x', labelcolor='k')
+    ax2t.xaxis.set_ticks_position('none')
+    ax2.plot(Tzz, Tzz*0+0.5, marker='|', color='k', ms=3, linestyle='None')
+
+    # Filters
+    ind_remove = np.where((wht<=0) | (ey<=0))[0]
+    if f_plot_filter:
+        ax1 = plot_filter(MB, ax1, ymax, scl=scl_yaxis, ind_remove=ind_remove)
+        xx = np.arange(2200,100000,100)
+        ax0.plot(xx, xx * 0, linestyle='--', lw=0.5, color='k')
+
+    plt.savefig(MB.DIR_OUT + 'param_' + ID + '_corner.png', dpi=150)
+
+
+def plot_corner_physparam_cumulative_frame(ID, Zall=np.arange(-1.2,0.4249,0.05), age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0], \
+    tau0=[0.1,0.2,0.3], fig=None, dust_model=0, out_ind=0, snlimbb=1.0, DIR_OUT='./', NRbb_lim=10000):
+    '''
+    Creat "cumulative" png for gif image.
+    
+    If you like to creat temporal png for gif image.
+
+    Parameters
+    ----------
+    snlimbb : float
+        SN limit to show flux or up lim in SED.
+    '''
+    col = ['violet', 'indigo', 'b', 'lightblue', 'lightgreen', 'g', 'orange', 'coral', 'r', 'darkred']#, 'k']
+    nage = np.arange(0,len(age),1)
+    fnc  = Func(ID, PA, Zall, age, dust_model=dust_model) # Set up the number of Age/ZZ
+    bfnc = Basic(Zall)
+
+    ###########################
+    # Open result file
+    ###########################
+    # Open ascii file and stock to array.
+    lib     = fnc.open_spec_fits(ID, PA, fall=0)
+    lib_all = fnc.open_spec_fits(ID, PA, fall=1)
+
+    file = 'summary_' + ID + '.fits'
+    hdul = fits.open(file) # open a FITS file
+
+    # Redshift MC
+    zp50  = hdul[1].data['zmc'][1]
+    zp16  = hdul[1].data['zmc'][0]
+    zp84  = hdul[1].data['zmc'][2]
+
+    M50 = hdul[1].data['ms'][1]
+    M16 = hdul[1].data['ms'][0]
+    M84 = hdul[1].data['ms'][2]
+    print('Total stellar mass is %.2e'%(M50))
+
+    A50 = np.zeros(len(age), dtype='float')
+    A16 = np.zeros(len(age), dtype='float')
+    A84 = np.zeros(len(age), dtype='float')
+    for aa in range(len(age)):
+        A50[aa] = hdul[1].data['A'+str(aa)][1]
+        A16[aa] = hdul[1].data['A'+str(aa)][0]
+        A84[aa] = hdul[1].data['A'+str(aa)][2]
+
+    Asum  = np.sum(A50)
+    aa   = 0
+    Av50 = hdul[1].data['Av'+str(aa)][1]
+    Av16 = hdul[1].data['Av'+str(aa)][0]
+    Av84 = hdul[1].data['Av'+str(aa)][2]
+    Z50 = np.zeros(len(age), dtype='float')
+    Z16 = np.zeros(len(age), dtype='float')
+    Z84 = np.zeros(len(age), dtype='float')
+    NZbest = np.zeros(len(age), dtype='int')
+    for aa in range(len(age)):
+        Z50[aa] = hdul[1].data['Z'+str(aa)][1]
+        Z16[aa] = hdul[1].data['Z'+str(aa)][0]
+        Z84[aa] = hdul[1].data['Z'+str(aa)][2]
+        NZbest[aa]= bfnc.Z2NZ(Z50[aa])
+
+
+    ZZ50 = np.sum(Z50*A50)/np.sum(A50)  # Light weighted Z.
+    chi   = hdul[1].data['chi'][0]
+    chin  = hdul[1].data['chi'][1]
+    fitc  = chin
+    Cz0   = hdul[0].header['Cz0']
+    Cz1   = hdul[0].header['Cz1']
+    Cz2   = hdul[0].header['Cz2']
+    zbes  = hdul[0].header['z']
+    zscl = (1.+zbes)
+
+    # Repeat no.
+    nplot = 1000
+    #DIR_OUT = '/astro/udfcen3/Takahiro/sedfitter/corner/' + ID + '_corner/'
+    try:
+        os.makedirs(DIR_OUT)
+    except:
+        pass
+
+    # plot Configuration
+    K = 4 # No of params.
+    Par = ['$\log M_*/M_\odot$', '$\log T$/Gyr', '$A_V$/mag', '$\log Z / Z_\odot$']
+    factor = 2.0           # size of one side of one panel
+    lbdim = 0.5 * factor   # size of left/bottom margin
+    trdim = 0.2 * factor   # size of top/right margin
+    whspace = 0.02         # w/hspace size
+    plotdim = factor * K + factor * (K - 1.) * whspace
+    dim = lbdim + plotdim + trdim
+    sclfig = 0.7
+
+    # Create a new figure if one wasn't provided.
+    if fig is None:
+        fig, axes = plt.subplots(K, K, figsize=(dim*sclfig, dim*sclfig))
+    else:
+        try:
+            axes = np.array(fig.axes).reshape((K, K))
+        except:
+            raise ValueError("Provided figure has {0} axes, but data has "
+                             "dimensions K={1}".format(len(fig.axes), K))
+    # Format the figure.
+    lb = lbdim / dim
+    tr = (lbdim + plotdim) / dim
+    fig.subplots_adjust(left=lb* 1.06, bottom=lb*.9, right=tr, top=tr*.99,
+                        wspace=whspace, hspace=whspace)
+
+    # For spec plot
+    ax0 = fig.add_axes([0.62,0.61,0.37,0.33])
+
+    ###############################
+    # Data taken from
+    ###############################
+    DIR_TMP = './templates/'
+    dat = np.loadtxt(DIR_TMP + 'spec_obs_' + ID + '.cat', comments='#')
+    NR = dat[:, 0]
+    x  = dat[:, 1]
+    fy00 = dat[:, 2]
+    ey00 = dat[:, 3]
+
+    con0 = (NR<1000) #& (fy/ey>SNlim)
+    xg0  = x[con0]
+    fg0  = fy00[con0] * Cz0
+    eg0  = ey00[con0] * Cz0
+    con1 = (NR>=1000) & (NR<2000) #& (fy/ey>SNlim)
+    xg1  = x[con1]
+    fg1  = fy00[con1] * Cz1
+    eg1  = ey00[con1] * Cz1
+    con2 = (NR>=2000) & (NR<NRbb_lim) #& (fy/ey>SNlim)
+    xg2  = x[con2]
+    fg2  = fy00[con2] * Cz2
+    eg2  = ey00[con2] * Cz2
+
+    con_bb = (NR>=NRbb_lim)#& (fy/ey>SNlim)
+    xg_bb  = x[con_bb]
+    fg_bb  = fy00[con_bb]
+    eg_bb  = ey00[con_bb]
+
+    fy01 = np.append(fg0,fg1)
+    ey01 = np.append(eg0,eg1)
+    fy02 = np.append(fy01,fg2)
+    ey02 = np.append(ey01,eg2)
+
+    fy = np.append(fy02,fg_bb)
+    ey = np.append(ey02,eg_bb)
+    wht=1./np.square(ey)
+
+    dat = np.loadtxt(DIR_TMP + 'bb_obs_' + ID + '.cat', comments='#')
+    NRbb = dat[:,0]
+    xbb  = dat[:,1]
+    fybb = dat[:,2]
+    eybb = dat[:,3]
+    exbb = dat[:,4]
+    snbb = fybb/eybb
+
+    conspec = (NR<NRbb_lim) #& (fy/ey>1)
+    #ax0.plot(xg0, fg0 * c / np.square(xg0) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='royalblue', label='')
+    #ax0.plot(xg1, fg1 * c / np.square(xg1) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='#DF4E00', label='')
+    conbb = (fybb/eybb>snlimbb)
+    ax0.errorbar(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, yerr=eybb[conbb]*c/np.square(xbb[conbb])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
+    ax0.plot(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, '.r', ms=10, linestyle='', linewidth=0, zorder=4)
+
+    conbbe = (fybb/eybb<snlimbb)
+    ax0.plot(xbb[conbbe], eybb[conbbe] * c / np.square(xbb[conbbe]) / d, 'vr', ms=10, linestyle='', linewidth=0, zorder=4)
+
+    ymax = np.max(fybb[conbb] * c / np.square(xbb[conbb]) / d) * 1.10
+    ax0.set_xlabel('Observed wavelength ($\mathrm{\mu m}$)', fontsize=14)
+    ax0.set_ylabel('Flux ($\mathrm{erg}/\mathrm{s}/\mathrm{cm}^{2}/\mathrm{\AA}$)', fontsize=13)
+    ax0.set_xlim(2200, 88000)
+    #ax1.set_xlim(12500, 16000)
+    ax0.set_xscale('log')
+    ax0.set_ylim(-0.05, ymax)
+
+    DIR_TMP = './templates/'
+    ####################
+    # MCMC corner plot.
+    ####################
+    file = 'chain_' + ID + '_corner.cpkl'
+    niter = 0
+    data = loadcpkl(os.path.join('./'+file))
+
+    try:
+        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
+        burnin = data['burnin']
+        nmc    = data['niter']
+        nwalk  = data['nwalkers']
+        Nburn  = burnin #*20
+        samples = data['chain'][:]
+    except:
+        if verbose: print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
+
+    f0     = fits.open(DIR_TMP + 'ms_' + ID + '.fits')
+    sedpar = f0[1]
+
+    import matplotlib
+    import matplotlib.cm as cm
+    getcmap   = matplotlib.cm.get_cmap('jet')
+    nc        = np.arange(0, nmc, 1)
+    col = getcmap((nc-0)/(nmc-0))
+
+    #for kk in range(0,nmc,1):
+    Ntmp = np.zeros(nplot, dtype='float')
+    lmtmp= np.zeros(nplot, dtype='float')
+    Avtmp= np.zeros(nplot, dtype='float')
+    Ztmp = np.zeros(nplot, dtype='float')
+    Ttmp = np.zeros(nplot, dtype='float')
+    ACtmp= np.zeros(nplot, dtype='float')
+
+    files = [] # For movie
+    for kk in range(0,nplot,1):
+
+        #nr = kk # np.random.randint(len(samples))
+        nr = np.random.randint(len(samples))
+        Avtmp[kk] = samples['Av'][nr]
+        #Asum = 0
+        #for ss in range(len(age)):
+        #Asum += np.sum(samples['A'+str(ss)][nr])
+        II0   = nage #[0,1,2,3] # Number for templates
+        for ss in range(len(age)):
+            ii = int(len(II0) - ss - 1) # from old to young templates.
+            AA_tmp = samples['A'+str(ii)][nr]
+            try:
+                ZZ_tmp = samples['Z'+str(ii)][nr]
+            except:
+                ZZ_tmp = samples['Z0'][nr]
+            nZtmp      = bfnc.Z2NZ(ZZ_tmp)
+            mslist     = sedpar.data['ML_'+str(nZtmp)][ii]
+            lmtmp[kk] += AA_tmp * mslist
+            Ztmp[kk]  += (10 ** ZZ_tmp) * AA_tmp * mslist
+            Ttmp[kk]  += age[ii] * AA_tmp * mslist
+            ACtmp[kk] += AA_tmp * mslist
+
+            # SED
+            flim = 0.05
+            if ss == 0:
+                y0, x0   = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
+                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
+                ysump = y0p #* 1e18
+                ysum  = y0  #* 1e18
+                if AA_tmp/Asum > flim:
+                    ax0.plot(x0, y0 * c/ np.square(x0) / d, '--', lw=0.1, color=col[ii], zorder=-1, label='', alpha=0.1)
+            else:
+                y0_r, x0_tmp = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
+                y0p, x0p  = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
+                ysump += y0p  #* 1e18
+                ysum  += y0_r #* 1e18
+                if AA_tmp/Asum > flim:
+                    ax0.plot(x0, y0_r * c/ np.square(x0) / d, '--', lw=0.1, color=col[ii], zorder=-1, label='', alpha=0.1)
+        # Total
+        ax0.plot(x0, ysum * c/ np.square(x0) / d, '-', lw=0.1, color='gray', zorder=-1, label='', alpha=0.1)
+        ax0.set_xlim(2200, 88000)
+        ax0.set_xscale('log')
+        ax0.set_ylim(0., ymax)
+
+        # Convert into log
+        Ztmp[kk] /= ACtmp[kk]
+        Ttmp[kk] /= ACtmp[kk]
+        Ntmp[kk]  = kk
+
+        lmtmp[kk] = np.log10(lmtmp[kk])
+        Ztmp[kk]  = np.log10(Ztmp[kk])
+        Ttmp[kk]  = np.log10(Ttmp[kk])
+
+
+        NPAR = [lmtmp[:kk+1], Ttmp[:kk+1], Avtmp[:kk+1], Ztmp[:kk+1]]
+        NPARmin = [np.log10(M16)-0.1, -0.4, Av16-0.1, -0.5]
+        NPARmax = [np.log10(M84)+0.1, 0.5, Av84+0.1, 0.5]
+
+        #for kk in range(0,nplot,1):
+        if kk == nplot-1:
+            # Histogram
+            for i, x in enumerate(Par):
+                ax = axes[i, i]
+                x1min, x1max = NPARmin[i], NPARmax[i]
+                nbin = 50
+                binwidth1 = (x1max-x1min)/nbin
+                bins1 = np.arange(x1min, x1max + binwidth1, binwidth1)
+                ax.hist(NPAR[i], bins=bins1, orientation='vertical', color='b', histtype='stepfilled', alpha=0.6)
+                ax.set_xlim(x1min, x1max)
+                ax.set_yticklabels([])
+                if i == K-1:
+                    ax.set_xlabel('%s'%(Par[i]), fontsize=12)
+                if i < K-1:
+                    ax.set_xticklabels([])
+
+        # Scatter and contour
+        for i, x in enumerate(Par):
+            for j, y in enumerate(Par):
+                if i > j:
+                    ax = axes[i, j]
+                    ax.scatter(NPAR[j], NPAR[i], c='b', s=1, marker='o', alpha=0.01)
+                    ax.set_xlabel('%s'%(Par[j]), fontsize=12)
+
+                    x1min, x1max = NPARmin[j], NPARmax[j]
+                    y1min, y1max = NPARmin[i], NPARmax[i]
+                    ax.set_xlim(x1min, x1max)
+                    ax.set_ylim(y1min, y1max)
+
+                    if j==0:
+                        ax.set_ylabel('%s'%(Par[i]), fontsize=12)
+                    if j>0:
+                        ax.set_yticklabels([])
+                    if i<K-1:
+                        ax.set_xticklabels([])
+
+                if i < j:
+                    ax = axes[i, j]
+                    ax.set_xticklabels([])
+                    ax.set_yticklabels([])
+                    ax.set_frame_on(False)
+                    ax.set_xticks([])
+                    ax.set_yticks([])
+
+                if i == j:
+                    ax = axes[i, j]
+                    ax.set_yticklabels([])
+                    if i == K-1:
+                        ax.set_xlabel('%s'%(Par[i]), fontsize=12)
+                    if i < K-1:
+                        ax.set_xticklabels([])
+
+
+        if kk%10 == 0 and out_ind == 1:
+            fname = DIR_OUT + '%d.png' % kk
+            print('Saving frame', fname)
+            plt.savefig(fname, dpi=200)
+            files.append(fname)
+
+        #plt.savefig(DIR_OUT + '%d.pdf'%(kk))
+
+    plt.savefig(DIR_OUT + 'param_' + ID + '_corner.png', dpi=200)
+    plt.close()
+
+
+def write_lines(ID, zbes, R_grs=45, dw=4, umag=1.0, DIR_OUT='./'):
+    '''
+    '''
+    dlw = R_grs * dw # Can affect the SFR.
+    ldw = 7
+
+    ###################################
+    # To add lines in the plot,
+    # ,manually edit the following file
+    # so as Fcont50 have >0.
+    ###################################
+    flw = open(DIR_OUT + ID + '_lines_fit.txt', 'w')
+    flw.write('# LW flux_line eflux_line flux_cont EW eEW L_line eL_line\n')
+    flw.write('# (AA) (Flam_1e-18) (Flam_1e-18) (Flam_1e-18) (AA) (AA) (erg/s) (erg/s)\n')
+    flw.write('# Error in EW is 1sigma, by pm eflux_line.\n')
+    flw.write('# If EW=-99, it means gaussian fit failed.\n')
+    flw.write('# and flux is the sum of excess at WL pm %.1f AA.\n'%(dlw))
+    flw.write('# Magnification is corrected; mu=%.3f\n'%(umag))
+    try:
+        fl = np.loadtxt(DIR_OUT + 'table_' + ID + '_lines.txt', comments='#')
+        LW      = fl[:,2]
+        Fcont50 = fl[:,3]
+        Fline50 = fl[:,6]
+        for ii in range(len(LW)):
+            if Fcont50[ii] > 0:
+                WL = LW[ii] * (1.+zbes)
+                if ii == 7:
+                    contmp = (x > WL - dlw) & (x < WL + dlw*1.5)
+                else:
+                    contmp = (x > WL - dlw) & (x < WL + dlw)
+                FLW[ii] = 1
+
+                xx   = x[contmp]
+                yy   = (fy - ysum_cut)[contmp]
+                eyy  = ey[contmp]
+                yy2  = (ysum_cut)[contmp]
+
+                xyzip = zip(xx,yy,eyy,yy2)
+                xyzip = sorted(xyzip)
+
+                xxs  = np.array([p1 for p1,p2,p3,p4 in xyzip])
+                yys  = np.array([p2 for p1,p2,p3,p4 in xyzip])
+                eyys = np.array([p3 for p1,p2,p3,p4 in xyzip])
+                yy2s = np.array([p4 for p1,p2,p3,p4 in xyzip])
+
+                flux = np.zeros(len(xxs), dtype='float')
+                efl  = np.zeros(len(xxs), dtype='float')
+                for ff in range(len(xxs)):
+                    flux[ff] = yy2s[ff]/np.square(xxs[ff]) * c/d
+                    efl[ff]  = np.square(eyys[ff]/np.square(xxs[ff]) * c/d)
+
+                fmed = np.median(flux) # Median of continuum, model flux
+                esum = np.sqrt(simps(efl, xxs))
+
+                try:
+                    popt,pcov = curve_fit(gaus,xxs,yys,p0=[Fline50[ii],WL,10],sigma=eyys)
+                    xxss = xxs/zscl
+
+                    if ii == 7:
+                        popt,pcov = curve_fit(gaus,xxs,yys,p0=[Fline50[ii],WL+20,10],sigma=eyys)
+                        xxss = xxs/zscl
+
+                    if f_grsm:
+                        ax2t.plot(xxs/zscl, (gaus(xxs,*popt)+yy2s) * c/np.square(xxs)/d, '#4682b4', linestyle='-', linewidth=1, alpha=0.8, zorder=20)
+
+                    I1 = simps((gaus(xxs,*popt)) * c/np.square(xxs)/d, xxs)
+                    I2 = I1 - simps((gaus(xxs,*popt)) * c/np.square(xxs)/d, xxs)
+                    fline = I1
+
+                    Flum = fline*Cons*1e-18 # luminosity in erg/s.
+                    elum = esum *Cons*1e-18 # luminosity in erg/s.
+                    SFR  = Flum * 6.58*1e-42
+                    print('SFR is', SFR/umag)
+                    EW_tmp   = simps( ((gaus(xxs,*popt)) * c/np.square(xxs)/d)/yy2s, xxs)
+                    EW_tmp_u = simps( ((gaus(xxs,*popt) + eyys/np.sqrt(len(xxs))) * c/np.square(xxs)/d)/yy2s, xxs)
+
+                    if ii == 7:
+                        contmp2 = (xxs/zscl>4320.) & (xxs/zscl<4380.)
+                        popt,pcov = curve_fit(gaus,xxs[contmp2], yys[contmp2], p0=[Fline50[ii],WL,10], sigma=eyys[contmp2])
+
+                        I1 = simps((gaus(xxs[contmp2],*popt)) * c/np.square(xxs[contmp2])/d, xxs[contmp2])
+                        I2 = I1 - simps((gaus(xxs[contmp2],*popt)) * c/np.square(xxs[contmp2])/d, xxs[contmp2])
+                        fline = I1
+
+                        Flum = fline*Cons*1e-18 # luminosity in erg/s.
+                        elum = esum *Cons*1e-18 # luminosity in erg/s.
+                        SFR  = Flum * 6.58*1e-42
+                        print('SFR, update, is', SFR/umag)
+                        EW_tmp   = simps( ((gaus(xxs[contmp2],*popt)) * c/np.square(xxs[contmp2])/d)/yy2s[contmp2], xxs[contmp2])
+                        EW_tmp_u = simps( ((gaus(xxs[contmp2],*popt) + eyys[contmp2]/np.sqrt(len(xxs[contmp2]))) * c/np.square(xxs[contmp2])/d)/yy2s[contmp2], xxs[contmp2])
+
+                    flw.write('%d %.2f %.2f %.2f %.2f %.2f %.2e %.2e %.2f\n'%(LW[ii],fline/umag, esum/umag, fmed/umag, EW_tmp,(EW_tmp_u-EW_tmp), Flum*1e-18/umag, elum*1e-18/umag, SFR/umag))
+
+                except Exception:
+                    fsum = np.zeros(len(xxs))
+                    for ff in range(len(fsum)):
+                        fsum[ff] = (yys[ff]+yy2s[ff])/np.square(xxs[ff])
+
+                    fline = np.sum(fsum) / d*c
+                    flw.write('%d %.2f %.2f %.2f %d %d %d %d %d\n'%(LW[ii],fline,esum,fmed, -99, 0, -99, 0, 0))
+                    pass
+
+    except:
+        pass
+    flw.close()
+
+
+def plot_corner_physparam_frame(ID, PA, Zall=np.arange(-1.2,0.4249,0.05), age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0], \
+    tau0=[0.1,0.2,0.3], fig=None, dust_model=0):
+    '''
+    If you like to
+    Creat temporal png for gif image.
+    '''
+    col = ['violet', 'indigo', 'b', 'lightblue', 'lightgreen', 'g', 'orange', 'coral', 'r', 'darkred']#, 'k']
+
+    nage = np.arange(0,len(age),1)
+    fnc  = Func(ID, PA, Zall, age, dust_model=dust_model) # Set up the number of Age/ZZ
+    bfnc = Basic(Zall)
+
+    ###########################
+    # Open result file
+    ###########################
+    # Open ascii file and stock to array.
+    lib     = fnc.open_spec_fits(fall=0, tau0=tau0)
+    lib_all = fnc.open_spec_fits(fall=1, tau0=tau0)
+
+    file = 'summary_' + ID + '.fits'
+    hdul = fits.open(file) # open a FITS file
+
+    # Redshift MC
+    zp50  = hdul[1].data['zmc'][1]
+    zp16  = hdul[1].data['zmc'][0]
+    zp84  = hdul[1].data['zmc'][2]
+
+    M50 = hdul[1].data['ms'][1]
+    M16 = hdul[1].data['ms'][0]
+    M84 = hdul[1].data['ms'][2]
+    print('Total stellar mass is %.2e'%(M50))
+
+    A50 = np.zeros(len(age), dtype='float')
+    A16 = np.zeros(len(age), dtype='float')
+    A84 = np.zeros(len(age), dtype='float')
+    for aa in range(len(age)):
+        A50[aa] = hdul[1].data['A'+str(aa)][1]
+        A16[aa] = hdul[1].data['A'+str(aa)][0]
+        A84[aa] = hdul[1].data['A'+str(aa)][2]
+
+    Asum  = np.sum(A50)
+    aa   = 0
+    Av50 = hdul[1].data['Av'+str(aa)][1]
+    Av16 = hdul[1].data['Av'+str(aa)][0]
+    Av84 = hdul[1].data['Av'+str(aa)][2]
+    Z50 = np.zeros(len(age), dtype='float')
+    Z16 = np.zeros(len(age), dtype='float')
+    Z84 = np.zeros(len(age), dtype='float')
+    NZbest = np.zeros(len(age), dtype='int')
+    for aa in range(len(age)):
+        Z50[aa] = hdul[1].data['Z'+str(aa)][1]
+        Z16[aa] = hdul[1].data['Z'+str(aa)][0]
+        Z84[aa] = hdul[1].data['Z'+str(aa)][2]
+        NZbest[aa]= bfnc.Z2NZ(Z50[aa])
+
+
+    ZZ50 = np.sum(Z50*A50)/np.sum(A50)  # Light weighted Z.
+    chi   = hdul[1].data['chi'][0]
+    chin  = hdul[1].data['chi'][1]
+    fitc  = chin
+    Cz0   = hdul[0].header['Cz0']
+    Cz1   = hdul[0].header['Cz1']
+    zbes  = hdul[0].header['z']
+    zscl = (1.+zbes)
+
+
+    # Repeat no.
+    nplot = 1000
+
+    DIR_OUT = '/astro/udfcen3/Takahiro/sedfitter/corner/' + ID + '_corner/'
+    try:
+        os.makedirs(DIR_OUT)
+    except:
+        pass
+
+    # plot Configuration
+    K = 4 # No of params.
+    Par = ['$\log M_*/M_\odot$', '$\log T_*$/Gyr', '$A_V$/mag', '$\log Z_* / Z_\odot$']
+    factor = 2.0           # size of one side of one panel
+    lbdim = 0.5 * factor   # size of left/bottom margin
+    trdim = 0.2 * factor   # size of top/right margin
+    whspace = 0.02         # w/hspace size
+    plotdim = factor * K + factor * (K - 1.) * whspace
+    dim = lbdim + plotdim + trdim
+    sclfig = 0.7
+
+    # Create a new figure if one wasn't provided.
+    ###############################
+    # Data taken from
+    ###############################
+    DIR_TMP = './templates/'
+    dat = np.loadtxt(DIR_TMP + 'spec_obs_' + ID + '.cat', comments='#')
+    NR = dat[:, 0]
+    x  = dat[:, 1]
+    fy00 = dat[:, 2]
+    ey00 = dat[:, 3]
+
+    con0 = (NR<1000) #& (fy/ey>SNlim)
+    xg0  = x[con0]
+    fg0  = fy00[con0] * Cz0
+    eg0  = ey00[con0] * Cz0
+    con1 = (NR>=1000) & (NR<10000) #& (fy/ey>SNlim)
+    xg1  = x[con1]
+    fg1  = fy00[con1] * Cz1
+    eg1  = ey00[con1] * Cz1
+    con2 = (NR>=10000)#& (fy/ey>SNlim)
+    xg2  = x[con2]
+    fg2  = fy00[con2]
+    eg2  = ey00[con2]
+    fy01 = np.append(fg0,fg1)
+    fy   = np.append(fy01,fg2)
+    ey01 = np.append(eg0,eg1)
+    ey   = np.append(ey01,eg2)
+    wht=1./np.square(ey)
+
+    dat = np.loadtxt(DIR_TMP + 'bb_obs_' + ID + '.cat', comments='#')
+    NRbb = dat[:, 0]
+    xbb  = dat[:, 1]
+    fybb = dat[:, 2]
+    eybb = dat[:, 3]
+    exbb = dat[:, 4]
+    snbb = fybb/eybb
+
+    conspec = (NR<10000) #& (fy/ey>1)
+    #ax0.plot(xg0, fg0 * c / np.square(xg0) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='royalblue', label='')
+    #ax0.plot(xg1, fg1 * c / np.square(xg1) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='#DF4E00', label='')
+    conbb = (fybb/eybb>1)
+
+    DIR_TMP = './templates/'
+    ####################
+    # MCMC corner plot.
+    ####################
+    file = 'chain_' + ID + '_corner.cpkl'
+    niter = 0
+    data = loadcpkl(os.path.join('./'+file))
+
+    try:
+        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
+        burnin = data['burnin']
+        nmc    = data['niter']
+        nwalk  = data['nwalkers']
+        Nburn  = burnin #*20
+        samples = data['chain'][:]
+    except:
+        if verbose: print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
+
+    f0     = fits.open(DIR_TMP + 'ms_' + ID + '.fits')
+    sedpar = f0[1]
+
+    import matplotlib
+    import matplotlib.cm as cm
+    getcmap   = matplotlib.cm.get_cmap('jet')
+    nc        = np.arange(0, nmc, 1)
+    col = getcmap((nc-0)/(nmc-0))
+
+    #for kk in range(0,nmc,1):
+    Ntmp = np.zeros(nplot, dtype='float')
+    lmtmp= np.zeros(nplot, dtype='float')
+    Avtmp= np.zeros(nplot, dtype='float')
+    Ztmp = np.zeros(nplot, dtype='float')
+    Ttmp = np.zeros(nplot, dtype='float')
+    ACtmp= np.zeros(nplot, dtype='float')
+
+
+    # Time bin
+    Txmax = 4 # Max x value
+    Tuni = MB.cosmo.age(zbes).value
+    Tuni0 = (Tuni - age[:])
+    delT  = np.zeros(len(age),dtype='float')
+    delTl = np.zeros(len(age),dtype='float')
+    delTu = np.zeros(len(age),dtype='float')
+    for aa in range(len(age)):
+        if aa == 0:
+            delTl[aa] = age[aa]
+            delTu[aa] = (age[aa+1]-age[aa])/2.
+            delT[aa]  = delTu[aa] + delTl[aa]
+        elif Tuni < age[aa]:
+            delTl[aa] = (age[aa]-age[aa-1])/2.
+            delTu[aa] = 10.
+            delT[aa]  = delTu[aa] + delTl[aa]
+        elif aa == len(age)-1:
+            delTl[aa] = (age[aa]-age[aa-1])/2.
+            delTu[aa] = Tuni - age[aa]
+            delT[aa]  = delTu[aa] + delTl[aa]
+        else:
+            delTl[aa] = (age[aa]-age[aa-1])/2.
+            delTu[aa] = (age[aa+1]-age[aa])/2.
+            delT[aa]  = delTu[aa] + delTl[aa]
+
+    delT[:]  *= 1e9 # Gyr to yr
+    delTl[:] *= 1e9 # Gyr to yr
+    delTu[:] *= 1e9 # Gyr to yr
+
+    ######
+    files = [] # For movie
+    SFmax = 0
+    Tsmin = 0
+    Tsmax = 0
+    Zsmin = 0
+    Zsmax = 0
+    AMtmp = 0
+    AMtmp16 = 0
+    AMtmp84 = 0
+    for ii in range(len(age)):
+        ZZ_tmp = Z50[ii] #samples['Z'+str(ii)][100]
+        ZZ_tmp16 = Z16[ii] #samples['Z'+str(ii)][100]
+        ZZ_tmp84 = Z84[ii] #samples['Z'+str(ii)][100]
+        AA_tmp = np.max(samples['A'+str(ii)][:])
+        AA_tmp84 = np.percentile(samples['A'+str(ii)][:],95)
+        AA_tmp16 = np.percentile(samples['A'+str(ii)][:],5)
+        #AA_tmp84 = A84[ii]
+        #AA_tmp16 = A16[ii]
+        nZtmp  = bfnc.Z2NZ(ZZ_tmp)
+        mslist = sedpar.data['ML_'+str(nZtmp)][ii]
+        AMtmp16 += mslist*AA_tmp16
+        AMtmp84 += mslist*AA_tmp84
+
+        Tsmax += age[ii] * AA_tmp84 * mslist
+        Tsmin += age[ii] * AA_tmp16 * mslist
+
+        Zsmax += 10**ZZ_tmp84 * AA_tmp84 * mslist
+        Zsmin += 10**ZZ_tmp16 * AA_tmp16 * mslist
+
+        SFtmp  = AA_tmp * mslist / delT[ii]
+        if SFtmp > SFmax:
+            SFmax = SFtmp
+
+    #NPARmin = [np.log10(M16)-0.1, -0.4, 0, -0.6]
+    #NPARmax = [np.log10(M84)+0.1, 0.5, 2., 0.5]
+    NPARmin = [np.log10(M16)-0.1, np.log10(Tsmin/AMtmp16)-0.1, Av16-0.1, np.log10(Zsmin/AMtmp16)-0.2]
+    NPARmax = [np.log10(M84)+0.1, np.log10(Tsmax/AMtmp84)+0.2, Av84+0.1, np.log10(Zsmax/AMtmp84)+0.2]
+
+    for kk in range(0,nplot,1):
+        if kk%10 == 0:
+            #print('New plot; %d'%kk)
+            fig, axes = plt.subplots(K, K, figsize=(dim*sclfig*2, dim*sclfig))
+            # Format the figure.
+            lb = lbdim / dim
+            tr = (lbdim + plotdim) / dim
+            #fig.subplots_adjust(left=lb*1.06, bottom=lb*.9, right=tr, top=tr*.99,
+            fig.subplots_adjust(left=0.5, bottom=lb*.9, right=tr, top=tr*.99,
+                                wspace=whspace, hspace=whspace)
+
+            # For spec plot
+            ax0 = fig.add_axes([0.05,0.73,0.37,0.23])
+            ax1 = fig.add_axes([0.05,0.40,0.37,0.23])
+            ax2 = fig.add_axes([0.05,0.07,0.37,0.23])
+
+            ax0.errorbar(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, yerr=eybb[conbb]*c/np.square(xbb[conbb])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
+            ax0.plot(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, '.r', ms=10, linestyle='', linewidth=0, zorder=4)#, label='Obs.(BB)')
+            ax0.plot(xg0, fg0 * c / np.square(xg0) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='royalblue', label='')
+            ax0.plot(xg1, fg1 * c / np.square(xg1) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='#DF4E00', label='')
+
+
+        #nr = kk # np.random.randint(len(samples))
+        nr = np.random.randint(len(samples))
+        Avtmp[kk] = samples['Av'][nr]
+        #Asum = 0
+        #for ss in range(len(age)):
+        #Asum += np.sum(samples['A'+str(ss)][nr])
+        ZMM = np.zeros((len(age)), dtype='float') # Mass weighted Z.
+        ZM  = np.zeros((len(age)), dtype='float') # Light weighted T.
+        ZC  = np.zeros((len(age)), dtype='float') # Light weighted T.
+        SF  = np.zeros((len(age)), dtype='float') # SFR
+        AM  = np.zeros((len(age)), dtype='float') # Light weighted T.
+
+
+        II0   = nage #[0,1,2,3] # Number for templates
+        for ss in range(len(age)):
+            ii = int(len(II0) - ss - 1) # from old to young templates.
+            AA_tmp = samples['A'+str(ii)][nr]
+            try:
+                ZZ_tmp = samples['Z'+str(ii)][nr]
+            except:
+                ZZ_tmp = samples['Z0'][nr]
+
+            nZtmp      = bfnc.Z2NZ(ZZ_tmp)
+            mslist     = sedpar.data['ML_'+str(nZtmp)][ii]
+            lmtmp[kk] += AA_tmp * mslist
+            Ztmp[kk]  += (10 ** ZZ_tmp) * AA_tmp * mslist
+            Ttmp[kk]  += age[ii] * AA_tmp * mslist
+            ACtmp[kk] += AA_tmp * mslist
+
+            AM[ii] = AA_tmp * mslist
+            SF[ii] = AA_tmp * mslist / delT[ii]
+            ZM[ii] = ZZ_tmp # AAtmp[aa] * mslist[aa]
+            ZMM[ii]= (10 ** ZZ_tmp) * AA_tmp * mslist
+
+            # SED
+            flim = 0.05
+            if ss == 0:
+                y0, x0   = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
+                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
+                ysump = y0p #* 1e18
+                ysum  = y0  #* 1e18
+                if AA_tmp/Asum > flim:
+                    ax0.plot(x0, y0 * c/ np.square(x0) / d, '--', lw=1, color=col[ii], zorder=-1, label='', alpha=0.5)
+                #ax1.plot(age[ii], SF[ii], marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
+                #ax1.errorbar(age[ii], SF[ii], xerr=[[delTl[ii]/1e9], [delTu[ii]/1e9]], ms=10, marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
+                xx1 = np.arange(age[ii]-delTl[ii]/1e9, age[ii]+delTu[ii]/1e9, 0.01)
+                ax1.fill_between(xx1, xx1*0, xx1*0+SF[ii], lw=1, facecolor=col[ii], zorder=1, label='', alpha=0.5)
+                #ax2.plot(age[ii], ZM[ii], marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
+            else:
+                y0_r, x0_tmp = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
+                y0p, x0p     = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
+                ysump += y0p  #* 1e18
+                ysum  += y0_r #* 1e18
+                if AA_tmp/Asum > flim:
+                    ax0.plot(x0, y0_r * c/ np.square(x0) / d, '--', lw=1, color=col[ii], zorder=-1, label='', alpha=0.5)
+                #ax1.plot(age[ii], SF[ii], marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
+                #ax1.errorbar(age[ii], SF[ii], xerr=[[delTl[ii]/1e9], [delTu[ii]/1e9]], ms=10, marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
+                xx1 = np.arange(age[ii]-delTl[ii]/1e9, age[ii]+delTu[ii]/1e9, 0.01)
+                ax1.fill_between(xx1, xx1*0, xx1*0+SF[ii], lw=1, facecolor=col[ii], zorder=1, label='', alpha=0.5)
+                #ax2.plot(age[ii], ZM[ii], marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
+
+        for ss in range(len(age)):
+            #ii = int(len(II0) - ss - 1) # from old to young templates.
+            ii = ss # from old to young templates.
+            AC = np.sum(AM[ss:])
+            ZC[ss] = np.log10(np.sum(ZMM[ss:])/AC)
+            #ax2.errorbar(age[ii], ZC[ii], xerr=[[delTl[ii]/1e9], [delTu[ii]/1e9]], ms=10*(SF[ii]/np.sum(SF[:]))+1, marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
+            ax2.errorbar(age[ii], ZC[ii], xerr=[[delTl[ii]/1e9], [delTu[ii]/1e9]], ms=10*(AC/np.sum(AM[:]))+1, marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
+
+        # Total
+        ymax = np.max(fybb[conbb] * c / np.square(xbb[conbb]) / d) * 1.10
+        ax0.plot(x0, ysum * c/ np.square(x0) / d, '-', lw=1., color='gray', zorder=-1, label='', alpha=0.8)
+        ax0.set_xlim(2200, 88000)
+        ax0.set_xscale('log')
+        ax0.set_ylim(0., ymax)
+        ax0.set_xlabel('Observed wavelength ($\mathrm{\mu m}$)', fontsize=14)
+        ax0.set_ylabel('Flux ($\mathrm{erg}/\mathrm{s}/\mathrm{cm}^{2}/\mathrm{\AA}$)', fontsize=13)
+
+
+        ax1.plot(age[:], SF[:], marker='', linestyle='-', lw=1, color='k', zorder=-1, label='', alpha=0.5)
+        ax1.set_xlabel('$t$ (Gyr)', fontsize=12)
+        ax1.set_ylabel('$\log \dot{M_*}/M_\odot$yr$^{-1}$', fontsize=12)
+        ax1.set_xlim(0.008, Txmax)
+        ax1.set_ylim(0, SFmax)
+        ax1.set_xscale('log')
+
+
+        ax2.plot(age[:], ZC[:], marker='', linestyle='-', lw=1, color='k', zorder=-1, label='', alpha=0.5)
+        ax2.set_xlabel('$t$ (Gyr)', fontsize=12)
+        ax2.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
+        ax2.set_xlim(0.008, Txmax)
+        #ax2.set_ylim(NPARmin[3], NPARmax[3])
+        ax2.set_ylim(-0.6, 0.5)
+        ax2.set_xscale('log')
+        #ax2.yaxis.labelpad = -5
+
+        # For redshift
+        if zbes<2:
+            zred  = [zbes, 2, 3, 6]
+            #zredl = ['$z$', 2, 3, 6]
+            zredl = ['$z$', 2, 3, 6]
+        elif zbes<2.5:
+            zred  = [zbes, 2.5, 3, 6]
+            zredl = ['$z$', 2.5, 3, 6]
+        elif zbes<3.:
+            zred  = [zbes, 3, 6]
+            zredl = ['$z$', 3, 6]
+        elif zbes<6:
+            zred  = [zbes, 6]
+            zredl = ['$z$', 6]
+
+        Tzz   = np.zeros(len(zred), dtype='float')
+        for zz in range(len(zred)):
+            Tzz[zz] = (Tuni - MB.cosmo.age(zbes).value)
+            if Tzz[zz] < 0.01:
+                Tzz[zz] = 0.01
+
+
+        ax1t = ax1.twiny()
+        ax2t = ax2.twiny()
+        ax1t.set_xlim(0.008, Txmax)
+        ax1t.set_xscale('log')
+        ax1t.set_xticklabels(zredl[:])
+        ax1t.set_xticks(Tzz[:])
+        ax1t.tick_params(axis='x', labelcolor='k')
+        ax1t.xaxis.set_ticks_position('none')
+        ax1.plot(Tzz, Tzz*0+SFmax, marker='|', color='k', ms=3, linestyle='None')
+
+        ax2t.set_xlim(0.008, Txmax)
+        ax2t.set_xscale('log')
+        ax2t.set_xticklabels(zredl[:])
+        ax2t.set_xticks(Tzz[:])
+        ax2t.tick_params(axis='x', labelcolor='k')
+        ax2t.xaxis.set_ticks_position('none')
+        ax2.plot(Tzz, Tzz*0+0.5, marker='|', color='k', ms=3, linestyle='None')
+
+        # Convert into log
+        Ztmp[kk] /= ACtmp[kk]
+        Ttmp[kk] /= ACtmp[kk]
+        Ntmp[kk]  = kk
+
+        lmtmp[kk] = np.log10(lmtmp[kk])
+        Ztmp[kk]  = np.log10(Ztmp[kk])
+        Ttmp[kk]  = np.log10(Ttmp[kk])
+
+        NPAR    = [lmtmp[kk], Ttmp[kk], Avtmp[kk], Ztmp[kk]]
+
+        # Scatter and contour
+        for i, x in enumerate(Par):
+            for j, y in enumerate(Par):
+                #print(i,j,Par[j], Par[i])
+                if i > j:
+                    ax = axes[i, j]
+                    ax.scatter(NPAR[j], NPAR[i], c='b', s=10, marker='o', alpha=0.5)
+                    ax.set_xlabel('%s'%(Par[j]), fontsize=12)
+
+                    #x1min, x1max = np.min(NPAR[j]), np.max(NPAR[j])
+                    #y1min, y1max = np.min(NPAR[i]), np.max(NPAR[i])
+                    x1min, x1max = NPARmin[j], NPARmax[j]
+                    y1min, y1max = NPARmin[i], NPARmax[i]
+                    ax.set_xlim(x1min, x1max)
+                    ax.set_ylim(y1min, y1max)
+
+                    if j==0:
+                        ax.set_ylabel('%s'%(Par[i]), fontsize=12)
+                    if j>0:
+                        ax.set_yticklabels([])
+                    if i<K-1:
+                        ax.set_xticklabels([])
+                    if i == 2:
+                        ax.yaxis.labelpad = 5.
+
+                if i < j:
+                    ax = axes[i, j]
+                    ax.set_xticklabels([])
+                    ax.set_yticklabels([])
+                    ax.set_frame_on(False)
+                    ax.set_xticks([])
+                    ax.set_yticks([])
+
+                if i == j:
+                    ax = axes[i, j]
+                    ax.set_xticklabels([])
+                    ax.set_yticklabels([])
+                    ax.set_frame_on(False)
+                    ax.set_xticks([])
+                    ax.set_yticks([])
+
+                    '''
+                    ax.set_yticklabels([])
+                    if i == K-1:
+                        ax.set_xlabel('%s'%(Par[i]), fontsize=12)
+                    if i < K-1:
+                        ax.set_xticklabels([])
+                    '''
+
+        if kk%10 == 0:
+            fname = DIR_OUT + '%d.png' % (kk)
+            #fname = DIR_OUT + '%d.png' % (kk+1)
+            print('Saving frame', fname)
+            plt.savefig(fname, dpi=150)
+            #files.append(fname)
+            plt.close()
diff --git a/build/lib/gsf/plot_sfh.py b/build/lib/gsf/plot_sfh.py
new file mode 100644
index 0000000..026c23e
--- /dev/null
+++ b/build/lib/gsf/plot_sfh.py
@@ -0,0 +1,2004 @@
+import numpy as np
+import sys
+import asdf
+import matplotlib.pyplot as plt
+from numpy import log10
+from scipy.integrate import simps
+import os
+import time
+from matplotlib.ticker import FormatStrFormatter
+import matplotlib.ticker as ticker
+
+from astropy.io import fits
+
+# Custom modules
+from .function import *
+from .function_class import Func
+from .basic_func import Basic
+from .function_igm import *
+
+
+def plot_sfh(MB, flim=0.01, lsfrl=-3, mmax=1000, Txmin=0.08, Txmax=4, lmmin=5, fil_path='./FILT/',
+    dust_model=0, f_SFMS=False, f_symbol=True, verbose=False, f_silence=True, DIR_TMP=None,
+    f_log_sfh=True, dpi=250, TMIN=0.0001, tau_lim=0.01, skip_zhist=False, tset_SFR_SED=0.1, f_axis_force=True):
+    '''
+    Purpose
+    -------
+    Star formation history plot.
+
+    Parametes
+    ---------
+    flim : float
+        Lower limit for plotting an age bin.
+    lsfrl : float
+        Lower limit for SFR, in logMsun/yr
+    f_SFMS : bool
+        If true, plot SFR of the main sequence of a ginen stellar mass at each lookback time.
+    tset_SFR_SED : float
+        in Gyr. Time scale over which SFR estimate is averaged.
+    '''
+    if f_silence:
+        import matplotlib
+        matplotlib.use("Agg")
+
+    print('\n### Running plot_sfh ###\n')
+    fnc = MB.fnc
+    bfnc = MB.bfnc
+    ID = MB.ID
+    Z = MB.Zall
+    age = MB.age
+    nage = MB.nage
+    tau0 = MB.tau0
+    age = np.asarray(age)
+
+    try:
+        if not MB.ZFIX == None:
+            skip_zhist = True
+    except:
+        pass
+
+    if Txmin > np.min(age):
+        Txmin = np.min(age) * 0.8
+
+    NUM_COLORS = len(age)
+    cm = plt.get_cmap('gist_rainbow_r')
+    col = np.atleast_2d([cm(1.*i/NUM_COLORS) for i in range(NUM_COLORS)])
+
+    ################
+    # RF colors.
+    home = os.path.expanduser('~')
+    c = MB.c
+    chimax = 1.
+    m0set = MB.m0set
+    Mpc_cm = MB.Mpc_cm
+    d = MB.d
+
+    #############
+    # Plot.
+    #############
+    if f_log_sfh:
+        fig = plt.figure(figsize=(8,2.8))
+        fig.subplots_adjust(top=0.88, bottom=0.18, left=0.07, right=0.99, hspace=0.15, wspace=0.3)
+    else:
+        fig = plt.figure(figsize=(8.2,2.8))
+        fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
+
+    if skip_zhist:
+        if f_log_sfh:
+            fig = plt.figure(figsize=(5.5,2.8))
+            fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
+        else:
+            fig = plt.figure(figsize=(6.2,2.8))
+            fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
+        ax1 = fig.add_subplot(121)
+        ax2 = fig.add_subplot(122)
+    else:
+        ax1 = fig.add_subplot(131)
+        ax2 = fig.add_subplot(132)
+        ax4 = fig.add_subplot(133)
+        ax4t = ax4.twiny()
+
+    ax1t = ax1.twiny()
+    ax2t = ax2.twiny()
+
+    ##################
+    # Fitting Results
+    ##################
+    SNlim = 3 # avobe which SN line is shown.
+
+    ###########################
+    # Open result file
+    ###########################
+    file = MB.DIR_OUT + 'summary_' + ID + '.fits'
+    hdul = fits.open(file) # open a FITS file
+    try:
+        zbes = hdul[0].header['zmc']
+    except:
+        zbes = hdul[0].header['z']
+    chinu= hdul[1].data['chi']
+
+    try:
+        RA   = hdul[0].header['RA']
+        DEC  = hdul[0].header['DEC']
+    except:
+        RA  = 0
+        DEC = 0
+    try:
+        SN = hdul[0].header['SN']
+    except:
+        ###########################
+        # Get SN of Spectra
+        ###########################
+        file = os.path.join(DIR_TMP, 'spec_obs_' + ID + '.cat')
+        fds  = np.loadtxt(file, comments='#')
+        nrs  = fds[:,0]
+        lams = fds[:,1]
+        fsp  = fds[:,2]
+        esp  = fds[:,3]
+
+        consp = (nrs<10000) & (lams/(1.+zbes)>3600) & (lams/(1.+zbes)<4200)
+        if len((fsp/esp)[consp]>10):
+            SN = np.median((fsp/esp)[consp])
+        else:
+            SN = 1
+
+    Asum = 0
+    A50 = np.arange(len(age), dtype='float')
+    for aa in range(len(A50)):
+        A50[aa] = 10**hdul[1].data['A'+str(aa)][1]
+        Asum += A50[aa]
+
+    ####################
+    # For cosmology
+    ####################
+    DL = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm # Luminositydistance in cm
+    Cons = (4.*np.pi*DL**2/(1.+zbes))
+
+    Tuni = MB.cosmo.age(zbes).value #, use_flat=True, **cosmo)
+    Tuni0 = (Tuni - age[:])
+
+    delT  = np.zeros(len(age),dtype='float')
+    delTl = np.zeros(len(age),dtype='float')
+    delTu = np.zeros(len(age),dtype='float')
+
+    if len(age) == 1:
+        for aa in range(len(age)):
+            try:
+                tau_ssp = float(MB.inputs['TAU_SSP'])
+            except:
+                tau_ssp = tau_lim
+            delTl[aa] = tau_ssp/2.
+            delTu[aa] = tau_ssp/2.
+            if age[aa] < tau_lim:
+                # This is because fsps has the minimum tau = tau_lim
+                delT[aa] = tau_lim
+            else:
+                delT[aa] = delTu[aa] + delTl[aa]
+    else: # This is only true when CSP...
+        for aa in range(len(age)):
+            if aa == 0:
+                delTl[aa] = age[aa]
+                delTu[aa] = (age[aa+1]-age[aa])/2.
+                delT[aa] = delTu[aa] + delTl[aa]
+            elif Tuni < age[aa]:
+                delTl[aa] = (age[aa]-age[aa-1])/2.
+                delTu[aa] = Tuni-age[aa] #delTl[aa] #10.
+                delT[aa]  = delTu[aa] + delTl[aa]
+            elif aa == len(age)-1:
+                delTl[aa] = (age[aa]-age[aa-1])/2.
+                delTu[aa] = Tuni - age[aa]
+                delT[aa]  = delTu[aa] + delTl[aa]
+            else:
+                delTl[aa] = (age[aa]-age[aa-1])/2.
+                delTu[aa] = (age[aa+1]-age[aa])/2.
+                if age[aa]+delTu[aa]>Tuni:
+                    delTu[aa] = Tuni-age[aa]
+                delT[aa] = delTu[aa] + delTl[aa]
+
+            if delTu[aa]<0:
+                delTu[aa] = 1e3
+
+    con_delt = (delT<=0)
+    delT[con_delt] = 1e10
+    delT[:] *= 1e9 # Gyr to yr
+    delTl[:] *= 1e9 # Gyr to yr
+    delTu[:] *= 1e9 # Gyr to yr
+
+    ##############################
+    # Load Pickle
+    ##############################
+    samplepath = MB.DIR_OUT 
+    pfile = 'chain_' + ID + '_corner.cpkl'
+
+    niter = 0
+    data = loadcpkl(os.path.join(samplepath+'/'+pfile))
+    try:
+        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
+        burnin = data['burnin']
+        nmc    = data['niter']
+        nwalk  = data['nwalkers']
+        Nburn  = burnin
+        samples = data['chain'][:]
+    except:
+        print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
+        return -1
+
+    ######################
+    # Mass-to-Light ratio.
+    ######################
+    AM = np.zeros((len(age), mmax), dtype='float') # Mass in each bin.
+    AC = np.zeros((len(age), mmax), dtype='float') -99 # Cumulative mass in each bin.
+    AL = np.zeros((len(age), mmax), dtype='float') # Cumulative light in each bin.
+    ZM = np.zeros((len(age), mmax), dtype='float') # Z.
+    ZC = np.zeros((len(age), mmax), dtype='float') -99 # Cumulative Z.
+    ZL = np.zeros((len(age), mmax), dtype='float') -99 # Light weighted cumulative Z.
+    TC = np.zeros((len(age), mmax), dtype='float') # Mass weighted T.
+    TL = np.zeros((len(age), mmax), dtype='float') # Light weighted T.
+    ZMM= np.zeros((len(age), mmax), dtype='float') # Mass weighted Z.
+    ZML= np.zeros((len(age), mmax), dtype='float') # Light weighted Z.
+    SF = np.zeros((len(age), mmax), dtype='float') # SFR
+    Av = np.zeros(mmax, dtype='float') # SFR
+
+
+    # ##############################
+    # Add simulated scatter in quad
+    # if files are available.
+    # ##############################
+    try:
+        f_zev = int(MB.inputs['ZEVOL'])
+    except:
+        f_zev = 1
+
+    eZ_mean = 0
+    try:
+        meanfile = './sim_SFH_mean.cat'
+        dfile = np.loadtxt(meanfile, comments='#')
+        eA = dfile[:,2]
+        eZ = dfile[:,4]
+        eAv= np.mean(dfile[:,6])
+        if f_zev == 0:
+            eZ_mean = np.mean(eZ[:])
+            eZ[:] = age * 0 #+ eZ_mean
+        else:
+            try:
+                f_zev = int(prihdr['ZEVOL'])
+                if f_zev == 0:
+                    eZ_mean = np.mean(eZ[:])
+                    eZ = age * 0
+            except:
+                pass
+    except:
+        if verbose:
+            print('No simulation file (%s).\nError may be underestimated.' % meanfile)
+        eA = age * 0
+        eZ = age * 0
+        eAv = 0
+
+    mm = 0
+
+    #####################
+    # Get SED based SFR
+    #####################
+    f_SFRSED_plot = False
+    SFR_SED = np.zeros(mmax,dtype='float')
+
+    # ASDF;
+    af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
+    af0 = asdf.open(MB.DIR_TMP + 'spec_all.asdf')
+    sedpar = af['ML'] # For M/L
+    sedpar0 = af0['ML'] # For mass loss frac.
+
+    AAtmp = np.zeros(len(age), dtype='float')
+    ZZtmp = np.zeros(len(age), dtype='float')
+    mslist= np.zeros(len(age), dtype='float')
+
+    for mm in range(mmax):
+        delt_tot = 0
+        mtmp  = np.random.randint(len(samples))# + Nburn
+
+        try:
+            Av_tmp = samples['Av'][mtmp]
+        except:
+            Av_tmp = MB.AVFIX
+
+        Avrand = np.random.uniform(-eAv, eAv)
+        if Av_tmp + Avrand<0:
+            Av[mm] = 0
+        else:
+            Av[mm] = Av_tmp + Avrand
+
+        for aa in range(len(age)):
+            try:
+                # This is in log.
+                AAtmp[aa] = samples['A'+str(aa)][mtmp]
+            except:
+                AAtmp[aa] = -10
+                pass
+
+            try:
+                ZZtmp[aa] = samples['Z'+str(aa)][mtmp]
+            except:
+                try:
+                    ZZtmp[aa] = samples['Z0'][mtmp]
+                except:
+                    ZZtmp[aa] = MB.ZFIX
+
+            nZtmp = bfnc.Z2NZ(ZZtmp[aa])
+            mslist[aa] = sedpar['ML_'+str(nZtmp)][aa]
+            Arand = np.random.uniform(-eA[aa],eA[aa])
+            Zrand = np.random.uniform(-eZ[aa],eZ[aa])
+            f_m_sur = sedpar0['frac_mass_survive_%d'%nZtmp][aa]
+
+            if True:# Age limit here?
+                # quantity in log scale;
+                AM[aa, mm] = AAtmp[aa] + np.log10(mslist[aa]) + Arand 
+                AL[aa, mm] = AM[aa,mm] - np.log10(mslist[aa])
+                SF[aa, mm] = AAtmp[aa] + np.log10(mslist[aa] / delT[aa] / f_m_sur) + Arand # / ml
+                ZM[aa, mm] = ZZtmp[aa] + Zrand
+                ZMM[aa, mm]= ZZtmp[aa] + AAtmp[aa] + np.log10(mslist[aa]) + Zrand
+                ZML[aa, mm]= ZMM[aa,mm] - np.log10(mslist[aa])
+            if SF[aa, mm] < -10:
+                SF[aa, mm] = np.nan
+
+            # SFR from SED. This will be converted in log later;
+            if age[aa]<=tset_SFR_SED:
+                SFR_SED[mm] += 10**SF[aa, mm] * delT[aa]
+                delt_tot += delT[aa]
+
+        SFR_SED[mm] /= delt_tot
+        if SFR_SED[mm] > 0:
+            SFR_SED[mm] = np.log10(SFR_SED[mm])
+        else:
+            SFR_SED[mm] = -99
+
+        for aa in range(len(age)):
+
+            if Tuni0[aa]<0:
+                continue
+
+            if np.sum(10**AM[aa:,mm])>0:
+                AC[aa, mm] = np.log10(np.sum(10**AM[aa:,mm]))
+                ZC[aa, mm] = np.log10(np.sum(10**ZMM[aa:,mm])/10**AC[aa, mm])
+            if np.sum(10**AL[aa:,mm])>0:
+                ZL[aa, mm] = np.log10(np.sum(10**ZML[aa:,mm])/np.sum(10**AL[aa:,mm]))
+            if f_zev == 0: # To avoid random fluctuation in A.
+                ZC[aa,mm] = ZM[aa,mm]
+
+            ACs = 0
+            ALs = 0
+            for bb in range(aa, len(age), 1):
+
+                if Tuni0[bb]<0:
+                    continue
+
+                tmpAA = 10**np.random.uniform(-eA[bb],eA[bb])
+                tmpTT = np.random.uniform(-delT[bb]/1e9/2.,delT[bb]/1e9/2.)
+
+                TC[aa, mm] += (age[bb]+tmpTT) * 10**AAtmp[bb] * mslist[bb] * tmpAA
+                TL[aa, mm] += (age[bb]+tmpTT) * 10**AAtmp[bb] * tmpAA
+                ACs += 10**AAtmp[bb] * mslist[bb] * tmpAA
+                ALs += 10**AAtmp[bb] * tmpAA
+
+            TC[aa, mm] /= ACs
+            TL[aa, mm] /= ALs
+
+            if TC[aa, mm]>0:
+                TC[aa, mm] = np.log10(TC[aa, mm])
+            else:
+                TC[aa, mm] = np.nan
+
+            if TL[aa, mm]>0:
+                TL[aa, mm] = np.log10(TL[aa, mm])
+            else:
+                TL[aa, mm] = np.nan
+
+        # Do stuff...
+        time.sleep(0.01)
+        # Update Progress Bar
+        printProgressBar(mm, mmax, prefix = 'Progress:', suffix = 'Complete', length = 40)
+
+    Avtmp = np.percentile(Av[:],[16,50,84])
+
+    #############
+    # Plot
+    #############
+    AMp = np.zeros((len(age),3), dtype='float')
+    ACp = np.zeros((len(age),3), dtype='float')
+    ZMp = np.zeros((len(age),3), dtype='float')
+    ZCp = np.zeros((len(age),3), dtype='float')
+    ZLp = np.zeros((len(age),3), dtype='float')
+    SFp = np.zeros((len(age),3), dtype='float')
+    for aa in range(len(age)):
+       AMp[aa,:] = np.nanpercentile(AM[aa,:], [16,50,84])
+       ACp[aa,:] = np.nanpercentile(AC[aa,:], [16,50,84])
+       ZMp[aa,:] = np.nanpercentile(ZM[aa,:], [16,50,84])
+       ZCp[aa,:] = np.nanpercentile(ZC[aa,:], [16,50,84])
+       ZLp[aa,:] = np.nanpercentile(ZL[aa,:], [16,50,84])
+       SFp[aa,:] = np.nanpercentile(SF[aa,:], [16,50,84])
+
+    SFR_SED_med = np.nanpercentile(SFR_SED[:],[16,50,84])
+    if f_SFRSED_plot:
+        ax1.errorbar(delt_tot/2./1e9, SFR_SED_med[1], xerr=[[delt_tot/2./1e9],[delt_tot/2./1e9]], \
+        yerr=[[SFR_SED_med[1]-SFR_SED_med[0]],[SFR_SED_med[2]-SFR_SED_med[1]]], \
+        linestyle='', color='orange', lw=1., marker='*',ms=8,zorder=-2)
+
+    ###################
+    msize = np.zeros(len(age), dtype='float')
+    for aa in range(len(age)):
+        if A50[aa]/Asum>flim: # if >1%
+            msize[aa] = 200 * A50[aa]/Asum
+
+    conA = (msize>=0)
+    if f_log_sfh:
+        ax1.fill_between(age[conA], SFp[:,0][conA], SFp[:,2][conA], linestyle='-', color='k', alpha=0.5, zorder=-1)
+        ax1.errorbar(age, SFp[:,1], linestyle='-', color='k', marker='', zorder=-1, lw=.5)
+    else:
+        ax1.fill_between(age[conA], 10**SFp[:,0][conA], 10**SFp[:,2][conA], linestyle='-', color='k', alpha=0.5, zorder=-1)
+        ax1.errorbar(age, 10**SFp[:,1], linestyle='-', color='k', marker='', zorder=-1, lw=.5)
+
+    if f_symbol:
+        tbnd = 0.0001
+        for aa in range(len(age)):
+            agebin = np.arange(age[aa]-delTl[aa]/1e9, age[aa]+delTu[aa]/1e9, delTu[aa]/1e10)
+            tbnd = age[aa]+delT[aa]/2./1e9
+            
+            if f_log_sfh:
+                ax1.errorbar(age[aa], SFp[aa,1], xerr=[[delTl[aa]/1e9], [delTu[aa]/1e9]], \
+                    yerr=[[SFp[aa,1]-SFp[aa,0]], [SFp[aa,2]-SFp[aa,1]]], linestyle='', color=col[aa], marker='', zorder=1, lw=1.)
+                if msize[aa]>0:
+                    ax1.scatter(age[aa], SFp[aa,1], marker='.', color=col[aa], edgecolor='k', s=msize[aa], zorder=1)
+            else:
+                ax1.errorbar(age[aa], 10**SFp[aa,1], xerr=[[delTl[aa]/1e9], [delTu[aa]/1e9]], \
+                    yerr=[[10**SFp[aa,1]-10**SFp[aa,0]], [10**SFp[aa,2]-10**SFp[aa,1]]], linestyle='', color=col[aa], marker='.', zorder=1, lw=1.)
+                if msize[aa]>0:
+                    ax1.scatter(age[aa], 10**SFp[aa,1], marker='.', color=col[aa], edgecolor='k', s=msize[aa], zorder=1)
+
+    #############
+    # Get SFMS in log10;
+    #############
+    IMF = int(MB.inputs['NIMF'])
+    SFMS_16 = get_SFMS(zbes,age,10**ACp[:,0],IMF=IMF)
+    SFMS_50 = get_SFMS(zbes,age,10**ACp[:,1],IMF=IMF)
+    SFMS_84 = get_SFMS(zbes,age,10**ACp[:,2],IMF=IMF)
+
+    #try:
+    if False:
+        f_rejuv,t_quench,t_rejuv = check_rejuv(age,SFp[:,:],ACp[:,:],SFMS_50)
+    else:
+        print('Failed to call rejuvenation module.')
+        f_rejuv,t_quench,t_rejuv = 0,0,0
+
+    # Plot MS?
+    if f_SFMS:
+        if f_log_sfh:
+            ax1.fill_between(age[conA], SFMS_50[conA]-0.2, SFMS_50[conA]+0.2, linestyle='-', color='b', alpha=0.3, zorder=-2)
+            ax1.plot(age[conA], SFMS_50[conA], linestyle='--', color='k', alpha=0.5, zorder=-2)
+        else:
+            ax1.fill_between(age[conA], 10**(SFMS_50[conA]-0.2), 10**(SFMS_50[conA]+0.2), linestyle='-', color='b', alpha=0.3, zorder=-2)
+            ax1.plot(age[conA], 10**SFMS_50[conA], linestyle='--', color='k', alpha=0.5, zorder=-2)
+
+    #
+    # Mass in each bin
+    #
+    ax2label = ''
+    ax2.fill_between(age[conA], ACp[:,0][conA], ACp[:,2][conA], linestyle='-', color='k', alpha=0.5)
+    ax2.errorbar(age[conA], ACp[:,1][conA], xerr=[delTl[:][conA]/1e9,delTu[:][conA]/1e9],
+        yerr=[ACp[:,1][conA]-ACp[:,0][conA],ACp[:,2][conA]-ACp[:,1][conA]], linestyle='-', color='k', lw=0.5, label=ax2label, zorder=1)
+
+    if f_symbol:
+        tbnd = 0.0001
+        mtmp = 0
+        for ii in range(len(age)):
+            aa = len(age) -1 - ii
+            agebin = np.arange(0, age[aa], delTu[aa]/1e10)
+            ax2.errorbar(age[aa], ACp[aa,1], xerr=[[delTl[aa]/1e9],[delTu[aa]/1e9]],
+                yerr=[[ACp[aa,1]-ACp[aa,0]],[ACp[aa,2]-ACp[aa,1]]], linestyle='-', color=col[aa], lw=1, zorder=2)
+
+            tbnd = age[aa]+delT[aa]/2./1e9
+            mtmp = ACp[aa,1]
+            if msize[aa]>0:
+                ax2.scatter(age[aa], ACp[aa,1], marker='.', c=[col[aa]], edgecolor='k', s=msize[aa], zorder=2)
+
+
+    y2min = np.max([lmmin,np.min(ACp[:,0][conA])])
+    y2max = np.max(ACp[:,2][conA])+0.05
+    if np.abs(y2max-y2min) < 0.2:
+        y2min -= 0.2
+
+    #
+    # Total Metal
+    #
+    if not skip_zhist:
+        ax4.fill_between(age[conA], ZCp[:,0][conA], ZCp[:,2][conA], linestyle='-', color='k', alpha=0.5)
+        ax4.errorbar(age[conA], ZCp[:,1][conA], linestyle='-', color='k', lw=0.5, zorder=1)
+        
+        for ii in range(len(age)):
+            aa = len(age) -1 - ii
+            if msize[aa]>0:
+                ax4.errorbar(age[aa], ZCp[aa,1], xerr=[[delTl[aa]/1e9],[delTu[aa]/1e9]], yerr=[[ZCp[aa,1]-ZCp[aa,0]],[ZCp[aa,2]-ZCp[aa,1]]], linestyle='-', color=col[aa], lw=1, zorder=1)
+                ax4.scatter(age[aa], ZCp[aa,1], marker='.', c=[col[aa]], edgecolor='k', s=msize[aa], zorder=2)
+
+
+    #############
+    # Axis
+    #############
+    # For redshift
+    if zbes<4:
+        if zbes<2:
+            zred  = [zbes, 2, 3, 6]
+            zredl = ['$z_\mathrm{obs.}$', 2, 3, 6]
+        elif zbes<2.5:
+            zred  = [zbes, 2.5, 3, 6]
+            zredl = ['$z_\mathrm{obs.}$', 2.5, 3, 6]
+        elif zbes<3.:
+            zred  = [zbes, 3, 6]
+            zredl = ['$z_\mathrm{obs.}$', 3, 6]
+        else:
+            zred  = [zbes, 6]
+            zredl = ['$z_\mathrm{obs.}$', 6]
+    elif zbes<6:
+        zred  = [zbes, 5, 6, 9]
+        zredl = ['$z_\mathrm{obs.}$', 5, 6, 9]
+    elif zbes<12:
+        zred  = [zbes, 12]
+        zredl = ['$z_\mathrm{obs.}$', 12]
+    else:
+        zred  = [zbes, 20]
+        zredl = ['$z_\mathrm{obs.}$', 20]
+
+    Tzz = np.zeros(len(zred), dtype='float')
+    for zz in range(len(zred)):
+        Tzz[zz] = (Tuni - MB.cosmo.age(zred[zz]).value)
+        if Tzz[zz] < Txmin:
+            Tzz[zz] = Txmin
+    
+    lsfru = 2.8
+    if np.max(SFp[:,2])>2.8:
+        lsfru = np.max(SFp[:,2])+0.1
+    if np.min(SFp[:,2])>lsfrl:
+        lsfrl = np.min(SFp[:,2])+0.1
+
+    if f_log_sfh:
+        if f_axis_force:
+            ax1.set_ylim(lsfrl, lsfru)
+        ax1.set_ylabel('$\log \dot{M}_*/M_\odot$yr$^{-1}$', fontsize=12)
+        #ax1.plot(Tzz, Tzz*0+lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
+    else:
+        ax1.set_ylim(0, 10**lsfru)
+        ax1.set_ylabel('$\dot{M}_*/M_\odot$yr$^{-1}$', fontsize=12)
+        #ax1.plot(Tzz, Tzz*0+10**lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
+
+    if Txmax < np.max(age):
+        Txmax = np.max(age)
+    ax1.set_xlim(Txmin, Txmax)
+    ax1.set_xscale('log')
+
+    ax2.set_ylabel('$\log M_*/M_\odot$', fontsize=12)
+    ax2.set_xlim(Txmin, Txmax)
+    if f_axis_force:
+        ax2.set_ylim(y2min, y2max)
+    ax2.set_xscale('log')
+    ax2.text(np.min(age*1.05), y2min + 0.07*(y2max-y2min), 'ID: %s\n$z_\mathrm{obs.}:%.2f$\n$\log M_\mathrm{*}/M_\odot:%.2f$\n$\log Z_\mathrm{*}/Z_\odot:%.2f$\n$\log T_\mathrm{*}$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$'\
+        %(ID, zbes, ACp[0,1], ZCp[0,1], np.nanmedian(TC[0,:]), Avtmp[1]), fontsize=9, bbox=dict(facecolor='w', alpha=0.7), zorder=10)
+
+    #
+    # Brief Summary
+    #
+    # Writing SED param in a fits file;
+    # Header
+    prihdr = fits.Header()
+    prihdr['ID'] = ID
+    prihdr['z'] = zbes
+    prihdr['RA'] = RA
+    prihdr['DEC'] = DEC
+    # Add rejuv properties;
+    prihdr['f_rejuv'] = f_rejuv
+    prihdr['t_quen'] = t_quench
+    prihdr['t_rejuv'] = t_rejuv
+    # SFR
+    prihdr['tset_SFR'] = tset_SFR_SED
+    # Version;
+    import gsf
+    prihdr['version'] = gsf.__version__
+
+    percs = [16,50,84]
+    zmc = hdul[1].data['zmc']
+    ACP = [ACp[0,0], ACp[0,1], ACp[0,2]]
+    ZCP = [ZCp[0,0], ZCp[0,1], ZCp[0,2]]
+    ZLP = [ZLp[0,0], ZLp[0,1], ZLp[0,2]]
+    con = (~np.isnan(TC[0,:]))
+    TMW = [np.percentile(TC[0,:][con],16), np.percentile(TC[0,:][con],50), np.percentile(TC[0,:][con],84)]
+    con = (~np.isnan(TL[0,:]))
+    TLW = [np.percentile(TL[0,:][con],16), np.percentile(TL[0,:][con],50), np.percentile(TL[0,:][con],84)]
+    for ii in range(len(percs)):
+        prihdr['zmc_%d'%percs[ii]] = ('%.3f'%zmc[ii],'redshift')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH Mstel_%d'%percs[ii]] = ('%.3f'%ACP[ii], 'Stellar mass, logMsun')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH SFR_%d'%percs[ii]] = ('%.3f'%SFR_SED_med[ii], 'SFR, logMsun/yr')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH Z_MW_%d'%percs[ii]] = ('%.3f'%ZCP[ii], 'Mass-weighted metallicity, logZsun')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH Z_LW_%d'%percs[ii]] = ('%.3f'%ZLP[ii], 'Light-weighted metallicity, logZsun')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH T_MW_%d'%percs[ii]] = ('%.3f'%TMW[ii], 'Mass-weighted age, logGyr')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH T_LW_%d'%percs[ii]] = ('%.3f'%TLW[ii], 'Light-weighted age, logGyr')
+    for ii in range(len(percs)):
+        prihdr['AV_%d'%percs[ii]] = ('%.3f'%Avtmp[ii], 'Dust attenuation, mag')
+    prihdu = fits.PrimaryHDU(header=prihdr)
+
+    # For SFH plot;
+    t0 = Tuni - age[:]
+    col02 = []
+    col50 = fits.Column(name='time', format='E', unit='Gyr', array=age[:])
+    col02.append(col50)
+    col50 = fits.Column(name='time_l', format='E', unit='Gyr', array=age[:]-delTl[:]/1e9)
+    col02.append(col50)
+    col50 = fits.Column(name='time_u', format='E', unit='Gyr', array=age[:]+delTl[:]/1e9)
+    col02.append(col50)
+    col50 = fits.Column(name='SFR16', format='E', unit='logMsun/yr', array=SFp[:,0])
+    col02.append(col50)
+    col50 = fits.Column(name='SFR50', format='E', unit='logMsun/yr', array=SFp[:,1])
+    col02.append(col50)
+    col50 = fits.Column(name='SFR84', format='E', unit='logMsun/yr', array=SFp[:,2])
+    col02.append(col50)
+    col50 = fits.Column(name='Mstel16', format='E', unit='logMsun', array=ACp[:,0])
+    col02.append(col50)
+    col50 = fits.Column(name='Mstel50', format='E', unit='logMsun', array=ACp[:,1])
+    col02.append(col50)
+    col50 = fits.Column(name='Mstel84', format='E', unit='logMsun', array=ACp[:,2])
+    col02.append(col50)
+    col50 = fits.Column(name='Z16', format='E', unit='logZsun', array=ZCp[:,0])
+    col02.append(col50)
+    col50 = fits.Column(name='Z50', format='E', unit='logZsun', array=ZCp[:,1])
+    col02.append(col50)
+    col50 = fits.Column(name='Z84', format='E', unit='logZsun', array=ZCp[:,2])
+    col02.append(col50)
+    
+    colms = fits.ColDefs(col02)
+    dathdu = fits.BinTableHDU.from_columns(colms)
+    hdu = fits.HDUList([prihdu, dathdu])
+    file_sfh = MB.DIR_OUT + 'SFH_' + ID + '.fits'
+    hdu.writeto(file_sfh, overwrite=True)
+
+    # ASDF;
+    from astropy import units as u
+
+    # ASDF;
+    tree_sfh = {
+        'id': ID,
+        'redshift': '%.3f'%zbes,
+        'nimf': '%s'%(IMF),
+        'version_gsf': gsf.__version__
+    }
+    tree_sfh['sfh'] = {}
+
+    for ii in range(len(percs)):
+        tree_sfh['zmc_%d'%percs[ii]] = '%.3f'%zmc[ii]
+    for ii in range(len(percs)):
+        tree_sfh['Mstel_%d'%percs[ii]] = '%.3f'%(10**ACP[ii]) * u.Msun
+    for ii in range(len(percs)):
+        tree_sfh['SFR_%d'%percs[ii]] = '%.3f'%(10**SFR_SED_med[ii]) * u.Msun / u.yr
+    for ii in range(len(percs)):
+        tree_sfh['Z_MW_%d'%percs[ii]] = '%.3f'%ZCP[ii]
+    for ii in range(len(percs)):
+        tree_sfh['Z_LW_%d'%percs[ii]] = '%.3f'%ZLP[ii]
+    for ii in range(len(percs)):
+        tree_sfh['T_MW_%d'%percs[ii]] = '%.3f'%(10**TMW[ii]) * u.Gyr
+    for ii in range(len(percs)):
+        tree_sfh['T_LW_%d'%percs[ii]] = '%.3f'%(10**TLW[ii]) * u.Gyr
+    for ii in range(len(percs)):
+        tree_sfh['AV_%d'%percs[ii]] = '%.3f'%Avtmp[ii]*u.mag
+
+    tree_sfh['sfh'].update({'time': age * u.Gyr})
+    tree_sfh['sfh'].update({'time_l': (age[:]-delTl[:]/1e9) * u.Gyr})
+    tree_sfh['sfh'].update({'time_u': (age[:]+delTl[:]/1e9) * u.Gyr})
+    tree_sfh['sfh'].update({'SFR16': 10**SFp[:,0] * u.Msun / u.yr})
+    tree_sfh['sfh'].update({'SFR50': 10**SFp[:,1] * u.Msun / u.yr})
+    tree_sfh['sfh'].update({'SFR84': 10**SFp[:,2] * u.Msun / u.yr})
+    tree_sfh['sfh'].update({'Mstel16': 10**ACp[:,0] * u.Msun})
+    tree_sfh['sfh'].update({'Mstel50': 10**ACp[:,1] * u.Msun})
+    tree_sfh['sfh'].update({'Mstel84': 10**ACp[:,2] * u.Msun})
+    tree_sfh['sfh'].update({'logZ16': ZCp[:,0]})
+    tree_sfh['sfh'].update({'logZ50': ZCp[:,1]})
+    tree_sfh['sfh'].update({'logZ84': ZCp[:,2]})
+
+    af = asdf.AsdfFile(tree_sfh)
+    af.write_to(os.path.join(MB.DIR_OUT, 'gsf_sfh_%s.asdf'%(ID)), all_array_compression='zlib')
+
+    # Attach to MB;
+    MB.sfh_tlook = age
+    MB.sfh_tlookl= delTl[:][conA]/1e9
+    MB.sfh_tlooku= delTu[:][conA]/1e9
+    MB.sfh_sfr16 = SFp[:,0]
+    MB.sfh_sfr50 = SFp[:,1]
+    MB.sfh_sfr84 = SFp[:,2]
+    MB.sfh_mfr16 = ACp[:,0]
+    MB.sfh_mfr50 = ACp[:,1]
+    MB.sfh_mfr84 = ACp[:,2]
+    MB.sfh_zfr16 = ZCp[:,0]
+    MB.sfh_zfr50 = ZCp[:,1]
+    MB.sfh_zfr84 = ZCp[:,2]
+
+    # SFH
+    zzall = np.arange(1.,12,0.01)
+    Tall = MB.cosmo.age(zzall).value
+
+    dely2 = 0.1
+    while (y2max-y2min)/dely2>7:
+        dely2 *= 2.
+
+    y2ticks = np.arange(y2min, y2max, dely2)
+    ax2.set_yticks(y2ticks)
+    ax2.set_yticklabels(np.arange(y2min, y2max, dely2), minor=False)
+
+    ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+
+    if not skip_zhist:
+
+        delZl = 0.4 # only for label. not delZ of Zall.
+        delZZl = delZl / 4
+
+        y3min, y3max = np.min(Z), np.max(Z)+delZZl
+        ax4.set_xlim(Txmin, Txmax)
+        ax4.set_ylim(y3min, y3max)
+        ax4.set_xscale('log')
+
+        if f_axis_force:
+            Zticks = np.arange(MB.Zall.min(), MB.Zall.max()+delZZl, delZl)
+            ax4.set_yticks(Zticks)
+            Zlabels = []
+            for Ztmp in Zticks:
+                Zlabels.append('%.1f'%(Ztmp))
+
+        ax4.yaxis.labelpad = -2
+        ax4.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
+        ax4.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
+        ax4t.set_xscale('log')
+
+        ax4t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
+        ax4t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
+        
+        ax4t.tick_params(axis='x', labelcolor='k')
+        ax4t.xaxis.set_ticks_position('none')
+        ax4t.plot(Tzz, Tzz*0+y3max+(y3max-y3min)*.00, marker='|', color='k', ms=3, linestyle='None')
+        ax4t.set_xlim(Txmin, Txmax)
+
+    ax1.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
+    ax2.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
+
+    # This has to come before set_xticks;
+    ax1t.set_xscale('log')
+    ax2t.set_xscale('log')
+
+    ax1t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
+    ax1t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
+    ax1t.tick_params(axis='x', labelcolor='k')
+    ax1t.xaxis.set_ticks_position('none')
+    ax1t.plot(Tzz, Tzz*0+lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
+
+    ax2t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
+    ax2t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
+    ax2t.tick_params(axis='x', labelcolor='k')
+    ax2t.xaxis.set_ticks_position('none')
+    ax2t.plot(Tzz, Tzz*0+y2max+(y2max-y2min)*.00, marker='|', color='k', ms=3, linestyle='None')
+
+    # This has to come after set_xticks;
+    ax1t.set_xlim(Txmin, Txmax)
+    ax2t.set_xlim(Txmin, Txmax)
+
+    # Save
+    fig.savefig(MB.DIR_OUT + 'SFH_' + ID + '_pcl.png', dpi=dpi)
+
+
+def sfr_tau(t0, tau0, Z=0.0, sfh=0, tt=np.arange(0,13,0.1), Mtot=1.,
+    sfrllim=1e-20):
+    '''
+    Parameters
+    ----------
+    sfh : int
+        1:exponential, 4:delayed exp, 5:, 6:lognormal
+    ML : float
+        Total Mass.
+    tt : float
+        Lookback time, in Gyr
+    tau0: float
+        in Gyr
+    t0 : float
+        age, in Gyr
+
+    Returns
+    -------
+    SFR : 
+        in Msun/yr
+    MFR :
+        in Msun
+
+    '''
+    yy = np.zeros(len(tt), dtype='float') 
+    yyms = np.zeros(len(tt), dtype='float')
+    con = (tt<=t0)
+    if sfh == 1:
+        yy[con] = np.exp((tt[con]-t0)/tau0)
+    elif sfh == 4:
+        yy[con] = (t0-tt[con]) * np.exp((tt[con]-t0)/tau0)
+    elif sfh == 6: # lognorm
+        con = (tt>0)
+        yy[con] = 1. / np.sqrt(2*np.pi*tau0**2) * np.exp(-(np.log(tt[con])-np.log(t0))**2/(2*tau0**2)) / tt[con]
+
+    # Total mass calculation;
+    #deltt = (tt[1] - tt[0]) #* 1e9
+    yyms[:] = np.cumsum(yy[::-1])[::-1] #* deltt * 1e9 # in Msun
+    
+    # Normalization;
+    deltt = tt[1] - tt[0]
+    C = Mtot/np.max(yyms)
+    yyms *= C
+    yy *= C / deltt / 1e9 # in Msun/yr
+
+    yy[~con] = sfrllim #1e-20
+    yyms[~con] = sfrllim #1e-20
+    return tt, yy, yyms
+
+
+def plot_sfh_tau(MB, flim=0.01, lsfrl=-1, mmax=1000, Txmin=0.08, Txmax=4, lmmin=8.5, fil_path='./FILT/',
+    dust_model=0, f_SFMS=False, f_symbol=True, verbose=False, f_silence=True, DIR_TMP=None,
+    f_log_sfh=True, dpi=250, TMIN=0.0001, tau_lim=0.01, skip_zhist=True, tset_SFR_SED=0.1):
+    '''
+    Purpose
+    -------
+    Star formation history plot.
+
+    Parameters
+    ----------
+    flim : float
+        Lower limit for plotting an age bin.
+    lsfrl : float
+        Lower limit for SFR, in logMsun/yr
+    f_SFMS : bool
+        If true, plot SFR of the main sequence of a ginen stellar mass at each lookback time.
+    tset_SFR_SED : float
+        in Gyr. Time scale over which SFR estimate is averaged.
+    '''
+    if f_silence:
+        import matplotlib
+        matplotlib.use("Agg")
+    else:
+        import matplotlib
+
+    print('\n### Running plot_sfh_tau ###\n')
+
+    fnc = MB.fnc
+    bfnc = MB.bfnc
+    ID = MB.ID
+    Z = MB.Zall
+    age = MB.age
+    nage = MB.nage
+    tau0 = MB.tau0
+    ageparam = MB.ageparam
+
+    try:
+        if not MB.ZFIX == None:
+            skip_zhist = True
+    except:
+        pass
+
+    NUM_COLORS = len(age)
+    cm = plt.get_cmap('gist_rainbow_r')
+    col = np.atleast_2d([cm(1.*i/NUM_COLORS) for i in range(NUM_COLORS)])
+
+    ################
+    # RF colors.
+    home = os.path.expanduser('~')
+    c = MB.c
+    chimax = 1.
+    m0set = MB.m0set
+    Mpc_cm = MB.Mpc_cm
+    d = MB.d
+
+    #############
+    # Plot.
+    #############
+    if f_log_sfh:
+        fig = plt.figure(figsize=(8,2.8))
+        fig.subplots_adjust(top=0.88, bottom=0.18, left=0.07, right=0.99, hspace=0.15, wspace=0.3)
+    else:
+        fig = plt.figure(figsize=(8.2,2.8))
+        fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
+
+    if skip_zhist:
+        if f_log_sfh:
+            fig = plt.figure(figsize=(5.5,2.8))
+            fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
+        else:
+            fig = plt.figure(figsize=(6.2,2.8))
+            fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
+        ax1 = fig.add_subplot(121)
+        ax2 = fig.add_subplot(122)
+    else:
+        ax1 = fig.add_subplot(131)
+        ax2 = fig.add_subplot(132)
+        ax4 = fig.add_subplot(133)
+        ax4t = ax4.twiny()
+
+    ax1t = ax1.twiny()
+    ax2t = ax2.twiny()
+
+    ##################
+    # Fitting Results
+    ##################
+    SNlim = 3 # avobe which SN line is shown.
+
+
+    ###########################
+    # Open result file
+    ###########################
+    file = MB.DIR_OUT + 'summary_' + ID + '.fits'
+    hdul = fits.open(file) # open a FITS file
+    try:
+        zbes = hdul[0].header['zmc']
+    except:
+        zbes = hdul[0].header['z']
+    chinu= hdul[1].data['chi']
+
+    try:
+        RA   = hdul[0].header['RA']
+        DEC  = hdul[0].header['DEC']
+    except:
+        RA  = 0
+        DEC = 0
+    try:
+        SN = hdul[0].header['SN']
+    except:
+        ###########################
+        # Get SN of Spectra
+        ###########################
+        file = 'templates/spec_obs_' + ID + '.cat'
+        fds  = np.loadtxt(file, comments='#')
+        nrs  = fds[:,0]
+        lams = fds[:,1]
+        fsp  = fds[:,2]
+        esp  = fds[:,3]
+
+        consp = (nrs<10000) & (lams/(1.+zbes)>3600) & (lams/(1.+zbes)<4200)
+        if len((fsp/esp)[consp]>10):
+            SN = np.median((fsp/esp)[consp])
+        else:
+            SN = 1
+
+    Asum = 0
+    A50 = np.arange(len(age), dtype='float')
+    for aa in range(len(A50)):
+        A50[aa] = 10**hdul[1].data['A'+str(aa)][1]
+        Asum += A50[aa]
+
+    ####################
+    # For cosmology
+    ####################
+    DL = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm # Luminositydistance in cm
+    Cons = (4.*np.pi*DL**2/(1.+zbes))
+
+    Tuni = MB.cosmo.age(zbes).value #, use_flat=True, **cosmo)
+    Tuni0 = (Tuni - age[:])
+
+    delT  = np.zeros(len(age),dtype='float')
+    delTl = np.zeros(len(age),dtype='float')
+    delTu = np.zeros(len(age),dtype='float')
+
+    if len(age) == 1:
+    #if tau0[0] < 0: # SSP;
+        for aa in range(len(age)):
+            try:
+                tau_ssp = float(MB.inputs['TAU_SSP'])
+            except:
+                tau_ssp = tau_lim
+            delTl[aa] = tau_ssp/2
+            delTu[aa] = tau_ssp/2
+            if age[aa] < tau_lim:
+                delT[aa] = tau_lim
+            else:
+                delT[aa] = delTu[aa] + delTl[aa]
+    else: # This is only true when CSP...
+        for aa in range(len(age)):
+            if aa == 0:
+                delTl[aa] = age[aa]
+                delTu[aa] = (age[aa+1]-age[aa])/2.
+                delT[aa]  = delTu[aa] + delTl[aa]
+            elif Tuni < age[aa]:
+                delTl[aa] = (age[aa]-age[aa-1])/2.
+                delTu[aa] = Tuni-age[aa] #delTl[aa] #10.
+                delT[aa]  = delTu[aa] + delTl[aa]
+            elif aa == len(age)-1:
+                delTl[aa] = (age[aa]-age[aa-1])/2.
+                delTu[aa] = Tuni - age[aa]
+                delT[aa]  = delTu[aa] + delTl[aa]
+            else:
+                delTl[aa] = (age[aa]-age[aa-1])/2.
+                delTu[aa] = (age[aa+1]-age[aa])/2.
+                if age[aa]+delTu[aa]>Tuni:
+                    delTu[aa] = Tuni-age[aa]
+                delT[aa] = delTu[aa] + delTl[aa]
+
+    con_delt = (delT<=0)
+    delT[con_delt] = 1e10
+    delT[:] *= 1e9 # Gyr to yr
+    delTl[:] *= 1e9 # Gyr to yr
+    delTu[:] *= 1e9 # Gyr to yr
+
+    ##############################
+    # Load Pickle
+    ##############################
+    samplepath = MB.DIR_OUT 
+    pfile = 'chain_' + ID + '_corner.cpkl'
+
+    niter = 0
+    data = loadcpkl(os.path.join(samplepath+'/'+pfile))
+    try:
+        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
+        burnin = data['burnin']
+        nmc    = data['niter']
+        nwalk  = data['nwalkers']
+        Nburn  = burnin #* nwalk/10/2 # I think this takes 3/4 of samples
+        #if nmc>1000:
+        #    Nburn  = 500
+        samples = data['chain'][:]
+    except:
+        print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
+        return -1
+
+    ######################
+    # Mass-to-Light ratio.
+    ######################
+    AM = np.zeros((len(age), mmax), dtype='float') # Mass in each bin.
+    AC = np.zeros((len(age), mmax), dtype='float') -99 # Cumulative mass in each bin.
+    AL = np.zeros((len(age), mmax), dtype='float') # Cumulative light in each bin.
+    ZM = np.zeros((len(age), mmax), dtype='float') # Z.
+    ZC = np.zeros((len(age), mmax), dtype='float') -99 # Cumulative Z.
+    ZL = np.zeros((len(age), mmax), dtype='float') -99 # Light weighted cumulative Z.
+    TC = np.zeros((len(age), mmax), dtype='float') # Mass weighted T.
+    TL = np.zeros((len(age), mmax), dtype='float') # Light weighted T.
+    ZMM = np.zeros((len(age), mmax), dtype='float') # Mass weighted Z.
+    ZML= np.zeros((len(age), mmax), dtype='float') # Light weighted Z.
+    SF = np.zeros((len(age), mmax), dtype='float') # SFR
+    Av = np.zeros(mmax, dtype='float') # SFR
+
+
+    # ##############################
+    # Add simulated scatter in quad
+    # if files are available.
+    # ##############################
+    f_zev = int(MB.inputs['ZEVOL'])
+
+    eZ_mean = 0
+
+    #####################
+    # Get SED based SFR
+    #####################
+    f_SFRSED_plot = False
+    SFR_SED = np.zeros(mmax,dtype='float')
+
+    # ASDF;
+    af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
+    af0 = asdf.open(MB.DIR_TMP + 'spec_all.asdf')
+    sedpar = af['ML'] # For M/L
+    sedpar0 = af0['ML'] # For mass loss frac.
+
+    ttmin = 0.001
+    tt = np.arange(ttmin,Tuni+0.5,ttmin/10)
+    xSF = np.zeros((len(tt), mmax), dtype='float') # SFR
+    ySF = np.zeros((len(tt), mmax), dtype='float') # SFR
+    yMS = np.zeros((len(tt), mmax), dtype='float') # MFR
+    ySF_each = np.zeros((MB.npeak, len(tt), mmax), dtype='float') # SFR
+    yMS_each = np.zeros((MB.npeak, len(tt), mmax), dtype='float') # MFR
+
+    ZZmc = np.zeros((MB.npeak, mmax), dtype='float') 
+    TTmc = np.zeros((MB.npeak, mmax), dtype='float') 
+    TAmc = np.zeros((MB.npeak, mmax), dtype='float') 
+
+    if Txmin > np.min(tt):
+        Txmin = np.min(tt) * 0.8
+
+    mm = 0
+    plot_each = True
+    while mm<mmax:
+        mtmp = np.random.randint(len(samples))# + Nburn
+        if MB.nAV != 0:
+            Av_tmp = samples['Av'][mtmp]
+        else:
+            Av_tmp = MB.AVFIX
+
+        for aa in range(MB.npeak):
+            AAtmp = samples['A%d'%aa][mtmp]
+            ltautmp = samples['TAU%d'%aa][mtmp]
+            lagetmp = samples['AGE%d'%aa][mtmp]
+            if aa == 0 or MB.ZEVOL:
+                try:
+                    ZZtmp = samples['Z%d'%aa][mtmp]
+                except:
+                    ZZtmp = MB.ZFIX
+
+            ZZmc[aa,mm] = ZZtmp
+            TAmc[aa,mm] = lagetmp
+            TTmc[aa,mm] = ltautmp
+
+            nZtmp,nttmp,natmp = bfnc.Z2NZ(ZZtmp, ltautmp, lagetmp)
+            mslist = sedpar['ML_'+str(nZtmp)+'_'+str(nttmp)][natmp]
+
+            xSF[:,mm], ySF_each[aa,:,mm], yMS_each[aa,:,mm] = sfr_tau(10**lagetmp, 10**ltautmp, ZZtmp, sfh=MB.SFH_FORM, tt=tt, Mtot=10**AAtmp*mslist)
+            ySF[:,mm] += ySF_each[aa,:,mm]
+            yMS[:,mm] += yMS_each[aa,:,mm]
+
+            # SFR from SED. This will be converted in log later;
+            con_sfr = (xSF[:,mm] <= tset_SFR_SED)
+            SFR_SED[mm] += np.mean(ySF_each[aa,:,mm][con_sfr])
+
+
+        Av[mm] = Av_tmp
+        if plot_each:
+            ax1.plot(xSF[:,mm], np.log10(ySF[:,mm]), linestyle='-', color='k', alpha=0.01, zorder=-1, lw=0.5)
+            ax2.plot(xSF[:,mm], np.log10(yMS[:,mm]), linestyle='-', color='k', alpha=0.01, zorder=-1, lw=0.5)
+
+        if SFR_SED[mm] > 0:
+            SFR_SED[mm] = np.log10(SFR_SED[mm])
+        else:
+            SFR_SED[mm] = -99
+
+
+        mm += 1
+
+    Avtmp = np.percentile(Av[:],[16,50,84])
+
+    #############
+    # Plot
+    #############
+    xSFp = np.zeros((len(tt),3), dtype='float')
+    ySFp = np.zeros((len(tt),3), dtype='float')
+    yMSp = np.zeros((len(tt),3), dtype='float')
+    ySFp_each = np.zeros((MB.npeak, len(tt), 3), dtype='float')
+    yMSp_each = np.zeros((MB.npeak, len(tt), 3), dtype='float')
+    for ii in range(len(tt)):
+        xSFp[ii,:] = np.percentile(xSF[ii,:], [16,50,84])
+        ySFp[ii,:] = np.percentile(ySF[ii,:], [16,50,84])
+        yMSp[ii,:] = np.percentile(yMS[ii,:], [16,50,84])
+        for aa in range(MB.npeak):
+            ySFp_each[aa,ii,:] = np.percentile(ySF_each[aa,ii,:], [16,50,84])
+            yMSp_each[aa,ii,:] = np.percentile(yMS_each[aa,ii,:], [16,50,84])
+
+    for aa in range(MB.npeak):
+        ax1.plot(xSFp[:,1], np.log10(ySFp_each[aa,:,1]), linestyle='-', color=col[aa], alpha=1., zorder=-1, lw=0.5)
+        ax2.plot(xSFp[:,1], np.log10(ySFp_each[aa,:,1]), linestyle='-', color=col[aa], alpha=1., zorder=-1, lw=0.5)
+
+    ax1.plot(xSFp[:,1], np.log10(ySFp[:,1]), linestyle='-', color='k', alpha=1., zorder=-1, lw=0.5)
+    ax2.plot(xSFp[:,1], np.log10(yMSp[:,1]), linestyle='-', color='k', alpha=1., zorder=-1, lw=0.5)
+
+    ACp = np.zeros((len(tt),3),'float')
+    SFp = np.zeros((len(tt),3),'float')
+    ACp[:] = np.log10(yMSp[:,:])
+    SFp[:] = np.log10(ySFp[:,:])
+
+    SFR_SED_med = np.percentile(SFR_SED[:],[16,50,84])
+
+    ###################
+    msize = np.zeros(len(age), dtype='float')
+    # Metal
+    ZCp = np.zeros((MB.npeak,3),'float')
+    TCp = np.zeros((MB.npeak,3),'float')
+    TTp = np.zeros((MB.npeak,3),'float')
+    for aa in range(len(age)):
+        if A50[aa]/Asum>flim: # if >1%
+            msize[aa] = 200 * A50[aa]/Asum
+
+        ZCp[aa,:] = np.percentile(ZZmc[aa,:], [16,50,84])
+        TCp[aa,:] = np.percentile(TTmc[aa,:], [16,50,84])
+        TTp[aa,:] = np.percentile(TAmc[aa,:], [16,50,84])
+
+    if False:
+        conA = (msize>=0)
+        if f_log_sfh:
+            ax1.fill_between(age[conA], SFp[:,0][conA], SFp[:,2][conA], linestyle='-', color='k', alpha=0.5, zorder=-1)
+            ax1.errorbar(age, SFp[:,1], linestyle='-', color='k', marker='', zorder=-1, lw=.5)
+        else:
+            ax1.fill_between(age[conA], 10**SFp[:,0][conA], 10**SFp[:,2][conA], linestyle='-', color='k', alpha=0.5, zorder=-1)
+            ax1.errorbar(age, 10**SFp[:,1], linestyle='-', color='k', marker='', zorder=-1, lw=.5)
+
+    #############
+    # Get SFMS in log10;
+    #############
+    IMF = int(MB.inputs['NIMF'])
+    SFMS_16 = get_SFMS(zbes,tt,10**ACp[:,0],IMF=IMF)
+    SFMS_50 = get_SFMS(zbes,tt,10**ACp[:,1],IMF=IMF)
+    SFMS_84 = get_SFMS(zbes,tt,10**ACp[:,2],IMF=IMF)
+
+    #try:
+    if False:
+        f_rejuv,t_quench,t_rejuv = check_rejuv(age,SFp[:,:],ACp[:,:],SFMS_50)
+    else:
+        print('Failed to call rejuvenation module.')
+        f_rejuv,t_quench,t_rejuv = 0,0,0
+
+    # Plot MS?
+    conA = ()
+    if f_SFMS:
+        if f_log_sfh:
+            ax1.fill_between(tt[conA], SFMS_50[conA]-0.2, SFMS_50[conA]+0.2, linestyle='-', color='b', alpha=0.3, zorder=-2)
+            ax1.plot(tt[conA], SFMS_50[conA], linestyle='--', color='k', alpha=0.5, zorder=-2)
+
+    # Plot limit;
+    y2min = np.max([lmmin,np.min(np.log10(yMSp[:,1]))])
+    y2max = np.max(np.log10(yMSp[:,1]))+0.05
+    if np.abs(y2max-y2min) < 0.2:
+        y2min -= 0.2
+
+    # Total Metal
+    if not skip_zhist:
+        ax4.fill_between(age[conA], ZCp[:,0][conA], ZCp[:,2][conA], linestyle='-', color='k', alpha=0.5)
+        ax4.errorbar(age[conA], ZCp[:,1][conA], linestyle='-', color='k', lw=0.5, zorder=1)
+        
+        for ii in range(len(age)):
+            aa = len(age) -1 - ii
+            if msize[aa]>0:
+                ax4.errorbar(age[aa], ZCp[aa,1], xerr=[[delTl[aa]/1e9],[delTu[aa]/1e9]], yerr=[[ZCp[aa,1]-ZCp[aa,0]],[ZCp[aa,2]-ZCp[aa,1]]], linestyle='-', color=col[aa], lw=1, zorder=1)
+                ax4.scatter(age[aa], ZCp[aa,1], marker='.', c=[col[aa]], edgecolor='k', s=msize[aa], zorder=2)
+
+
+    #############
+    # Axis
+    #############
+    # For redshift
+    if zbes<4:
+        if zbes<2:
+            zred  = [zbes, 2, 3, 6]
+            zredl = ['$z_\mathrm{obs.}$', 2, 3, 6]
+        elif zbes<2.5:
+            zred  = [zbes, 2.5, 3, 6]
+            zredl = ['$z_\mathrm{obs.}$', 2.5, 3, 6]
+        elif zbes<3.:
+            zred  = [zbes, 3, 6]
+            zredl = ['$z_\mathrm{obs.}$', 3, 6]
+        else:
+            zred  = [zbes, 6]
+            zredl = ['$z_\mathrm{obs.}$', 6]
+    elif zbes<6:
+        zred  = [zbes, 5, 6, 9]
+        zredl = ['$z_\mathrm{obs.}$', 5, 6, 9]
+    elif zbes<12:
+        zred  = [zbes, 12]
+        zredl = ['$z_\mathrm{obs.}$', 12]
+    else:
+        zred  = [zbes, 20]
+        zredl = ['$z_\mathrm{obs.}$', 20]
+
+    Tzz = np.zeros(len(zred), dtype='float')
+    for zz in range(len(zred)):
+        Tzz[zz] = (Tuni - MB.cosmo.age(zred[zz]).value)
+        if Tzz[zz] < Txmin:
+            Tzz[zz] = Txmin
+    
+    lsfru = 2.8
+    if np.max(SFp[:,2])>2.8:
+        lsfru = np.max(SFp[:,2])+0.1
+
+    if f_log_sfh:
+        ax1.set_ylim(lsfrl, lsfru)
+        ax1.set_ylabel('$\log \dot{M}_*/M_\odot$yr$^{-1}$', fontsize=12)
+        #ax1.plot(Tzz, Tzz*0+lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
+    else:
+        ax1.set_ylim(0, 10**lsfru)
+        ax1.set_ylabel('$\dot{M}_*/M_\odot$yr$^{-1}$', fontsize=12)
+        #ax1.plot(Tzz, Tzz*0+10**lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
+
+    ax1.set_xlim(Txmin, Txmax)
+    ax1.set_xscale('log')
+
+    ax2.set_ylabel('$\log M_*/M_\odot$', fontsize=12)
+    ax2.set_xlim(Txmin, Txmax)
+    ax2.set_ylim(y2min, y2max)
+    ax2.set_xscale('log')
+    ax2.text(np.min(age*1.05), y2min + 0.07*(y2max-y2min), 'ID: %s\n$z_\mathrm{obs.}:%.2f$\n$\log M_\mathrm{*}/M_\odot:%.2f$\n$\log Z_\mathrm{*}/Z_\odot:%.2f$\n$\log T_\mathrm{*}$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$'\
+        %(ID, zbes, ACp[0,1], ZCp[0,1], np.nanmedian(TC[0,:]), Avtmp[1]), fontsize=9, bbox=dict(facecolor='w', alpha=0.7), zorder=10)
+
+    #
+    # Brief Summary
+    #
+    # Writing SED param in a fits file;
+    # Header
+    prihdr = fits.Header()
+    prihdr['ID'] = ID
+    prihdr['z'] = zbes
+    prihdr['RA'] = RA
+    prihdr['DEC'] = DEC
+    # Add rejuv properties;
+    prihdr['f_rejuv'] = f_rejuv
+    prihdr['t_quen'] = t_quench
+    prihdr['t_rejuv'] = t_rejuv
+    # SFR
+    prihdr['tset_SFR'] = tset_SFR_SED
+    # SFH
+    prihdr['SFH_FORM'] = MB.SFH_FORM
+    # Version;
+    import gsf
+    prihdr['version'] = gsf.__version__
+
+    percs = [16,50,84]
+    zmc = hdul[1].data['zmc']
+    ACP = [ACp[0,0], ACp[0,1], ACp[0,2]]
+    ZCP = [ZCp[0,0], ZCp[0,1], ZCp[0,2]]
+    ZLP = ZCP #[ZLp[0,0], ZLp[0,1], ZLp[0,2]]
+    TLW = TTp[0,:]
+    TMW = TTp[0,:]
+    TAW = TCp[0,:]
+    for ii in range(len(percs)):
+        prihdr['zmc_%d'%percs[ii]] = ('%.3f'%zmc[ii],'redshift')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH Mstel_%d'%percs[ii]] = ('%.3f'%ACP[ii], 'Stellar mass, logMsun')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH SFR_%d'%percs[ii]] = ('%.3f'%SFR_SED_med[ii], 'SFR, logMsun/yr')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH Z_MW_%d'%percs[ii]] = ('%.3f'%ZCP[ii], 'Mass-weighted metallicity, logZsun')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH Z_LW_%d'%percs[ii]] = ('%.3f'%ZLP[ii], 'Light-weighted metallicity, logZsun')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH T_MW_%d'%percs[ii]] = ('%.3f'%TMW[ii], 'Mass-weighted age, logGyr')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH T_LW_%d'%percs[ii]] = ('%.3f'%TLW[ii], 'Light-weighted age, logGyr')
+    for ii in range(len(percs)):
+        prihdr['HIERARCH TAU_%d'%percs[ii]] = ('%.3f'%TAW[ii], 'Tau, logGyr')
+    for ii in range(len(percs)):
+        prihdr['AV_%d'%percs[ii]] = ('%.3f'%Avtmp[ii], 'Dust attenuation, mag')
+    prihdu = fits.PrimaryHDU(header=prihdr)
+
+    # For SFH plot;
+    t0 = Tuni - age[:]
+    col02 = []
+    col50 = fits.Column(name='time', format='E', unit='Gyr', array=xSFp[:,1])
+    col02.append(col50)
+    col50 = fits.Column(name='time_l', format='E', unit='Gyr', array=xSFp[:,0])
+    col02.append(col50)
+    col50 = fits.Column(name='time_u', format='E', unit='Gyr', array=xSFp[:,2])
+    col02.append(col50)
+    col50 = fits.Column(name='SFR16', format='E', unit='logMsun/yr', array=SFp[:,0])
+    col02.append(col50)
+    col50 = fits.Column(name='SFR50', format='E', unit='logMsun/yr', array=SFp[:,1])
+    col02.append(col50)
+    col50 = fits.Column(name='SFR84', format='E', unit='logMsun/yr', array=SFp[:,2])
+    col02.append(col50)
+    col50 = fits.Column(name='Mstel16', format='E', unit='logMsun', array=ACp[:,0])
+    col02.append(col50)
+    col50 = fits.Column(name='Mstel50', format='E', unit='logMsun', array=ACp[:,1])
+    col02.append(col50)
+    col50 = fits.Column(name='Mstel84', format='E', unit='logMsun', array=ACp[:,2])
+    col02.append(col50)
+    col50 = fits.Column(name='Z16', format='E', unit='logZsun', array=ZCp[:,0])
+    col02.append(col50)
+    col50 = fits.Column(name='Z50', format='E', unit='logZsun', array=ZCp[:,1])
+    col02.append(col50)
+    col50 = fits.Column(name='Z84', format='E', unit='logZsun', array=ZCp[:,2])
+    col02.append(col50)
+    
+    colms = fits.ColDefs(col02)
+    dathdu = fits.BinTableHDU.from_columns(colms)
+    hdu = fits.HDUList([prihdu, dathdu])
+    file_sfh = MB.DIR_OUT + 'SFH_' + ID + '.fits'
+    hdu.writeto(file_sfh, overwrite=True)
+
+    # Attach to MB;
+    MB.sfh_tlook = age
+    MB.sfh_tlookl= delTl[:][conA]/1e9
+    MB.sfh_tlooku= delTu[:][conA]/1e9
+    MB.sfh_sfr16 = SFp[:,0]
+    MB.sfh_sfr50 = SFp[:,1]
+    MB.sfh_sfr84 = SFp[:,2]
+    MB.sfh_mfr16 = ACp[:,0]
+    MB.sfh_mfr50 = ACp[:,1]
+    MB.sfh_mfr84 = ACp[:,2]
+    MB.sfh_zfr16 = ZCp[:,0]
+    MB.sfh_zfr50 = ZCp[:,1]
+    MB.sfh_zfr84 = ZCp[:,2]
+
+    # SFH
+    zzall = np.arange(1.,12,0.01)
+    Tall = MB.cosmo.age(zzall).value # , use_flat=True, **cosmo)
+
+    dely2 = 0.1
+    while (y2max-y2min)/dely2>7:
+        dely2 *= 2.
+
+    y2ticks = np.arange(y2min, y2max, dely2)
+    ax2.set_yticks(y2ticks)
+    ax2.set_yticklabels(np.arange(y2min, y2max, dely2), minor=False)
+
+    ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+
+    if not skip_zhist:
+        y3min, y3max = np.min([np.min(Z),-0.8]), np.max([np.max(Z),0.4])
+        ax4.set_xlim(Txmin, Txmax)
+        ax4.set_ylim(y3min-0.05, y3max)
+        ax4.set_xscale('log')
+        ax4.set_yticks([-0.8, -0.4, 0., 0.4])
+        ax4.set_yticklabels(['-0.8', '-0.4', '0', '0.4'])
+        
+        #ax4.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
+        #ax3.yaxis.labelpad = -2
+        ax4.yaxis.labelpad = -2
+        ax4.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
+        ax4.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
+        ax4t.set_xscale('log')
+
+        ax4t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
+        ax4t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
+        ax4t.tick_params(axis='x', labelcolor='k')
+        ax4t.xaxis.set_ticks_position('none')
+        ax4t.plot(Tzz, Tzz*0+y3max+(y3max-y3min)*.00, marker='|', color='k', ms=3, linestyle='None')
+        ax4t.set_xlim(Txmin, Txmax)
+
+    ax1.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
+    ax2.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
+
+    # This has to come before set_xticks;
+    ax1t.set_xscale('log')
+    ax2t.set_xscale('log')
+
+    ax1t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
+    ax1t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
+    ax1t.tick_params(axis='x', labelcolor='k')
+    ax1t.xaxis.set_ticks_position('none')
+    ax1t.plot(Tzz, Tzz*0+lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
+
+    ax2t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
+    ax2t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
+    ax2t.tick_params(axis='x', labelcolor='k')
+    ax2t.xaxis.set_ticks_position('none')
+    ax2t.plot(Tzz, Tzz*0+y2max+(y2max-y2min)*.00, marker='|', color='k', ms=3, linestyle='None')
+
+    # This has to come after set_xticks;
+    ax1t.set_xlim(Txmin, Txmax)
+    ax2t.set_xlim(Txmin, Txmax)
+
+    # Save
+    fig.savefig(MB.DIR_OUT + 'SFH_' + ID + '_pcl.png', dpi=dpi)
+
+
+def get_evolv(MB, ID, Z=np.arange(-1.2,0.4249,0.05), age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0], f_comp=0, fil_path='./FILT/', \
+    inputs=None, dust_model=0, DIR_TMP='./templates/', delt_sfh=0.01):
+    '''
+    Purpose
+    -------
+    Reprocess output files to get spectra, UV color, and SFH at higher resolution.
+
+    Parameters
+    ----------
+    delt_sfh : float
+        delta t of input SFH in Gyr.
+    
+    Returns
+    -------
+    SED as function of age, based on SF and Z histories;
+    '''
+
+    print('This function may take a while as it runs fsps.')
+    flim  = 0.01
+    lsfrl = -1 # log SFR low limit
+    mmax  = 1000
+    Txmax = 4 # Max x value
+    lmmin = 10.3
+
+    nage = np.arange(0,len(age),1)
+    fnc  = Func(ID, Z, nage, dust_model=dust_model) # Set up the number of Age/ZZ
+    bfnc = Basic(Z)
+    age = np.asarray(age)
+
+    ################
+    # RF colors.
+    import os.path
+    home = os.path.expanduser('~')
+    c      = MB.c #3.e18 # A/s
+    m0set  = MB.m0set #25.0
+    chimax = 1.
+    d      = 10**(73.6/2.5) * 1e-18 # From [ergs/s/cm2/A] to [ergs/s/cm2/Hz]
+
+    ###########################
+    # Open result file
+    ###########################
+    file = 'summary_' + ID + '.fits'
+    hdul = fits.open(file) # open a FITS file
+    zbes = hdul[0].header['z']
+    chinu= hdul[1].data['chi']
+
+    uv= hdul[1].data['uv']
+    vj= hdul[1].data['vj']
+
+    RA   = 0
+    DEC  = 0
+    rek  = 0
+    erekl= 0
+    ereku= 0
+    mu = 1.0
+    nn = 0
+    qq = 0
+    enn = 0
+    eqq = 0
+    try:
+        RA   = hdul[0].header['RA']
+        DEC  = hdul[0].header['DEC']
+    except:
+        RA  = 0
+        DEC = 0
+
+    try:
+        SN = hdul[0].header['SN']
+    except:
+        ###########################
+        # Get SN of Spectra
+        ###########################
+        file = 'templates/spec_obs_' + ID + '.cat'
+        fds  = np.loadtxt(file, comments='#')
+        nrs  = fds[:,0]
+        lams = fds[:,1]
+        fsp  = fds[:,2]
+        esp  = fds[:,3]
+
+        consp = (nrs<10000) & (lams/(1.+zbes)>3600) & (lams/(1.+zbes)<4200)
+        if len((fsp/esp)[consp]>10):
+            SN = np.median((fsp/esp)[consp])
+        else:
+            SN = 1
+
+
+    Asum = 0
+    A50 = np.arange(len(age), dtype='float')
+    for aa in range(len(A50)):
+        A50[aa] = hdul[1].data['A'+str(aa)][1]
+        Asum += A50[aa]
+
+    ####################
+    # For cosmology
+    ####################
+    DL = MB.cosmo.luminosity_distance(zbes).value * MB.Mpc_cm # Luminositydistance in cm
+    Cons = (4.*np.pi*DL**2/(1.+zbes))
+
+    Tuni = MB.cosmo.age(zbes).value
+    Tuni0 = (Tuni - age[:])
+
+    delT  = np.zeros(len(age),dtype='float')
+    delTl = np.zeros(len(age),dtype='float')
+    delTu = np.zeros(len(age),dtype='float')
+    for aa in range(len(age)):
+        if aa == 0:
+            delTl[aa] = age[aa]
+            delTu[aa] = (age[aa+1]-age[aa])/2.
+            delT[aa]  = delTu[aa] + delTl[aa]
+        elif Tuni < age[aa]:
+            delTl[aa] = (age[aa]-age[aa-1])/2.
+            delTu[aa] = delTl[aa] #10.
+            delT[aa]  = delTu[aa] + delTl[aa]
+        elif aa == len(age)-1:
+            delTl[aa] = (age[aa]-age[aa-1])/2.
+            delTu[aa] = Tuni - age[aa]
+            delT[aa]  = delTu[aa] + delTl[aa]
+        else:
+            delTl[aa] = (age[aa]-age[aa-1])/2.
+            delTu[aa] = (age[aa+1]-age[aa])/2.
+            delT[aa]  = delTu[aa] + delTl[aa]
+
+    delT[:]  *= 1e9 # Gyr to yr
+    delTl[:] *= 1e9 # Gyr to yr
+    delTu[:] *= 1e9 # Gyr to yr
+    ##############################
+    # Load Pickle
+    ##############################
+    samplepath = './'
+    pfile = 'chain_' + ID + '_corner.cpkl'
+
+    niter = 0
+    data = loadcpkl(os.path.join(samplepath+'/'+pfile))
+    try:
+        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
+        burnin = data['burnin']
+        nmc    = data['niter']
+        nwalk  = data['nwalkers']
+        Nburn  = burnin #* nwalk/10/2 # I think this takes 3/4 of samples
+        #if nmc>1000:
+        #    Nburn  = 500
+        samples = data['chain'][:]
+    except:
+        print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
+        return -1
+
+    ######################
+    # Mass-to-Light ratio.
+    ######################
+    AM = np.zeros((len(age), mmax), dtype='float') # Mass in each bin.
+    AC = np.zeros((len(age), mmax), dtype='float') # Cumulative mass in each bin.
+    AL = np.zeros((len(age), mmax), dtype='float') # Cumulative light in each bin.
+    ZM = np.zeros((len(age), mmax), dtype='float') # Z.
+    ZC = np.zeros((len(age), mmax), dtype='float') # Cumulative Z.
+    ZL = np.zeros((len(age), mmax), dtype='float') # Light weighted cumulative Z.
+    TC = np.zeros((len(age), mmax), dtype='float') # Mass weighted T.
+    TL = np.zeros((len(age), mmax), dtype='float') # Light weighted T.
+    ZMM= np.zeros((len(age), mmax), dtype='float') # Mass weighted Z.
+    ZML= np.zeros((len(age), mmax), dtype='float') # Light weighted Z.
+    SF = np.zeros((len(age), mmax), dtype='float') # SFR
+    Av = np.zeros(mmax, dtype='float') # SFR
+
+    # ##############################
+    # Add simulated scatter in quad
+    # if files are available.
+    # ##############################
+    try:
+        f_zev = int(MB.inputs['ZEVOL'])
+    except:
+        f_zev = 1
+
+    eZ_mean = 0
+    try:
+        meanfile = './sim_SFH_mean.cat'
+        dfile    = np.loadtxt(meanfile, comments='#')
+        eA = dfile[:,2]
+        eZ = dfile[:,4]
+        eAv= np.mean(dfile[:,6])
+        if f_zev == 0:
+            eZ_mean = np.mean(eZ[:])
+            eZ[:]   = age * 0 #+ eZ_mean
+        else:
+            try:
+                f_zev = int(prihdr['ZEVOL'])
+                if f_zev == 0:
+                    eZ_mean = np.mean(eZ[:])
+                    eZ = age * 0
+            except:
+                pass
+    except:
+        print('No simulation file (%s).\nError may be underestimated.' % meanfile)
+        eA = age * 0
+        eZ = age * 0
+        eAv= 0
+
+    mm = 0
+    for mm in range(mmax):
+        mtmp  = np.random.randint(len(samples))# + Nburn
+        AAtmp = np.zeros(len(age), dtype='float')
+        ZZtmp = np.zeros(len(age), dtype='float')
+        mslist= np.zeros(len(age), dtype='float')
+
+        Av_tmp = samples['Av'][mtmp]
+
+        f0 = fits.open(DIR_TMP + 'ms_' + ID + '.fits')
+        sedpar = f0[1]
+        f1 = fits.open(DIR_TMP + 'ms.fits')
+        mloss = f1[1].data
+
+        Avrand = np.random.uniform(-eAv, eAv)
+        if Av_tmp + Avrand<0:
+            Av[mm] = 0
+        else:
+            Av[mm] = Av_tmp + Avrand
+
+        for aa in range(len(age)):
+            AAtmp[aa] = samples['A'+str(aa)][mtmp]/mu
+            try:
+                ZZtmp[aa] = samples['Z'+str(aa)][mtmp]
+            except:
+                ZZtmp[aa] = samples['Z0'][mtmp]
+
+            nZtmp      = bfnc.Z2NZ(ZZtmp[aa])
+            mslist[aa] = sedpar.data['ML_'+str(nZtmp)][aa]
+
+            ml = mloss['ms_'+str(nZtmp)][aa]
+
+            Arand = np.random.uniform(-eA[aa],eA[aa])
+            Zrand = np.random.uniform(-eZ[aa],eZ[aa])
+            AM[aa, mm] = AAtmp[aa] * mslist[aa] * 10**Arand
+            AL[aa, mm] = AM[aa, mm] / mslist[aa]
+            SF[aa, mm] = AAtmp[aa] * mslist[aa] / delT[aa] / ml * 10**Arand
+            ZM[aa, mm] = ZZtmp[aa] + Zrand
+            ZMM[aa, mm]= (10 ** ZZtmp[aa]) * AAtmp[aa] * mslist[aa] * 10**Zrand
+            ZML[aa, mm]= ZMM[aa, mm] / mslist[aa]
+
+        for aa in range(len(age)):
+            AC[aa, mm] = np.sum(AM[aa:, mm])
+            ZC[aa, mm] = np.log10(np.sum(ZMM[aa:, mm])/AC[aa, mm])
+            ZL[aa, mm] = np.log10(np.sum(ZML[aa:, mm])/np.sum(AL[aa:, mm]))
+            if f_zev == 0: # To avoid random fluctuation in A.
+                ZC[aa, mm] = ZM[aa, mm]
+
+            ACs = 0
+            ALs = 0
+            for bb in range(aa, len(age), 1):
+                tmpAA       = 10**np.random.uniform(-eA[bb],eA[bb])
+                tmpTT       = np.random.uniform(-delT[bb]/1e9,delT[bb]/1e9)
+                TC[aa, mm] += (age[bb]+tmpTT) * AAtmp[bb] * mslist[bb] * tmpAA
+                TL[aa, mm] += (age[bb]+tmpTT) * AAtmp[bb] * tmpAA
+                ACs        += AAtmp[bb] * mslist[bb] * tmpAA
+                ALs        += AAtmp[bb] * tmpAA
+
+            TC[aa, mm] /= ACs
+            TL[aa, mm] /= ALs
+
+    Avtmp  = np.percentile(Av[:],[16,50,84])
+
+    #############
+    # Plot
+    #############
+    AMp = np.zeros((len(age),3), dtype='float')
+    ACp = np.zeros((len(age),3), dtype='float')
+    ZMp = np.zeros((len(age),3), dtype='float')
+    ZCp = np.zeros((len(age),3), dtype='float')
+    SFp = np.zeros((len(age),3), dtype='float')
+    for aa in range(len(age)):
+       AMp[aa,:] = np.percentile(AM[aa,:], [16,50,84])
+       ACp[aa,:] = np.percentile(AC[aa,:], [16,50,84])
+       ZMp[aa,:] = np.percentile(ZM[aa,:], [16,50,84])
+       ZCp[aa,:] = np.percentile(ZC[aa,:], [16,50,84])
+       SFp[aa,:] = np.percentile(SF[aa,:], [16,50,84])
+
+    ###################
+    msize = np.zeros(len(age), dtype='float')
+    for aa in range(len(age)):
+        if A50[aa]/Asum>flim: # if >1%
+            msize[aa] = 150 * A50[aa]/Asum
+
+    conA = (msize>=0)
+    # Make template;
+    tbegin = np.min(Tuni-age)
+    tuniv_hr = np.arange(tbegin,Tuni,delt_sfh) # in Gyr
+    sfh_hr_in= np.interp(tuniv_hr,(Tuni-age)[::-1],SFp[:,1][::-1])
+    zh_hr_in = np.interp(tuniv_hr,(Tuni-age)[::-1],ZCp[:,1][::-1])
+
+    # FSPS
+    con_sfh = (tuniv_hr>0)
+    import fsps
+    nimf = int(inputs['NIMF'])
+    try:
+        fneb = int(inputs['ADD_NEBULAE'])
+    except:
+        fneb = 0
+
+    if fneb == 1:
+        print('Metallicity is set to logZ/Zsun=%.2f'%(np.max(zh_hr_in)))
+        sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, logzsol=np.max(zh_hr_in), sfh=3, dust_type=2, dust2=0.0, add_neb_emission=True)
+        sp.set_tabular_sfh(tuniv_hr[con_sfh], sfh_hr_in[con_sfh])
+    else:
+        sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=3, imf_type=nimf, sfh=3, dust_type=2, dust2=0.0, add_neb_emission=False)
+        sp.set_tabular_sfh(tuniv_hr[con_sfh], sfh_hr_in[con_sfh], Z=10**zh_hr_in[con_sfh])
+
+    col01 = []
+    t_get = tuniv_hr[con_sfh]
+
+    for ss in range(len(t_get)):
+        wave0, flux0 = sp.get_spectrum(tage=t_get[ss], peraa=True)
+        if ss == 0:
+            spec_mul_nu_conv = np.zeros((len(t_get),len(wave0)),dtype='float')
+
+        print('Template %d is processed.'%(ss))
+        wavetmp  = wave0*(1.+zbes)
+        spec_mul_nu = flamtonu(wavetmp, flux0) # Conversion from Flambda to Fnu.
+        Lsun = MB.Lsun #3.839 * 1e33 #erg s-1
+        stmp_common = 1e10 # 1 tmp is in 1e10Lsun
+
+        spec_mul_nu_conv[ss,:] = spec_mul_nu[:]
+        spec_mul_nu_conv[ss,:] *= Lsun/(4.*np.pi*DL**2/(1.+zbes))
+        Ls = 10**sp.log_lbol
+        spec_mul_nu_conv[ss,:] *= (1./Ls)*stmp_common # in unit of erg/s/Hz/cm2/ms[ss].
+
+        consave = (wavetmp/(1.+zbes)<20000) # AA
+        if ss == 0:
+            nd_ap  = np.arange(0,len(wave0),1)
+            col1   = fits.Column(name='wavelength', format='E', unit='AA', array=wavetmp[consave])#, disp='obs'
+            col2   = fits.Column(name='colnum', format='K', unit='', array=nd_ap[consave])
+            col00  = [col1, col2]
+            col3   = fits.Column(name='age', format='E', unit='Gyr', array=t_get)
+            col4   = fits.Column(name='sfh', format='E', unit='Msun/yr', array=sfh_hr_in[con_sfh])
+            col5   = fits.Column(name='zh', format='E', unit='Zsun', array=zh_hr_in[con_sfh])
+            col01  = [col3,col4,col5]
+
+        colspec_all = fits.Column(name='fspec_'+str(ss), format='E', unit='Fnu', array=spec_mul_nu_conv[ss,:][consave])#, disp='%s'%(t_get[ss])
+        col00.append(colspec_all)
+
+    coldefs_spec = fits.ColDefs(col00)
+    hdu = fits.BinTableHDU.from_columns(coldefs_spec)
+    hdu.writeto(DIR_TMP + 'obsspec_' + ID + '.fits', overwrite=True)
+
+    coldefs_spec = fits.ColDefs(col01)
+    hdu = fits.BinTableHDU.from_columns(coldefs_spec)
+    hdu.writeto(DIR_TMP + 'obshist_' + ID + '.fits', overwrite=True)
+
+
+def plot_evolv(MB, ID, Z=np.arange(-1.2,0.4249,0.05), age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0], f_comp=0, fil_path='./FILT/', \
+    inputs=None, dust_model=0, DIR_TMP='./templates/', delt_sfh = 0.01, nmc=300):
+    '''
+    Parameters
+    ----------
+    delt_sfh : float
+        delta t of input SFH in Gyr.
+
+    Returns
+    -------
+    SED as function of age, based on SF and Z histories.
+    '''
+
+    import os.path
+
+    ################
+    flim = 0.01
+    lsfrl = -1 # log SFR low limit
+    mmax  = 1000
+    Txmax = 4 # Max x value
+    lmmin = 10.3
+
+    nage = np.arange(0,len(age),1)
+    fnc  = Func(ID, Z, nage, dust_model=dust_model) # Set up the number of Age/ZZ
+    bfnc = Basic(Z)
+    age = np.asarray(age)
+
+    ################
+    # RF colors.
+    home = os.path.expanduser('~')
+    c      = 3.e18 # A/s
+    chimax = 1.
+    m0set   = 25.0
+    d      = 10**(73.6/2.5) * 1e-18 # From [ergs/s/cm2/A] to [ergs/s/cm2/Hz]
+
+    #############
+    # Plot.
+    #############
+    fig = plt.figure(figsize=(5,2.6))
+    fig.subplots_adjust(top=0.96, bottom=0.16, left=0.12, right=0.99, hspace=0.15, wspace=0.15)
+    #ax1 = fig.add_subplot(131)
+    #ax2 = fig.add_subplot(132)
+    #ax3 = fig.add_subplot(133)
+    ax2 = fig.add_subplot(121)
+    ax3 = fig.add_subplot(122)
+
+    ###########################
+    # Open result file
+    ###########################
+    file = 'summary_' + ID + '.fits'
+    hdul = fits.open(file) # open a FITS file
+    zbes = hdul[0].header['z']
+    chinu= hdul[1].data['chi']
+    uv= hdul[1].data['uv']
+    vj= hdul[1].data['vj']
+
+    try:
+        RA   = hdul[0].header['RA']
+        DEC  = hdul[0].header['DEC']
+    except:
+        RA  = 0
+        DEC = 0
+
+    try:
+        SN = hdul[0].header['SN']
+    except:
+        ###########################
+        # Get SN of Spectra
+        ###########################
+        file = 'templates/spec_obs_' + ID + '.cat'
+        fds  = np.loadtxt(file, comments='#')
+        nrs  = fds[:,0]
+        lams = fds[:,1]
+        fsp  = fds[:,2]
+        esp  = fds[:,3]
+
+        consp = (nrs<10000) & (lams/(1.+zbes)>3600) & (lams/(1.+zbes)<4200)
+        if len((fsp/esp)[consp]>10):
+            SN = np.median((fsp/esp)[consp])
+        else:
+            SN = 1
+
+    Asum = 0
+    A50 = np.arange(len(age), dtype='float')
+    for aa in range(len(A50)):
+        A50[aa] = hdul[1].data['A'+str(aa)][1]
+        Asum += A50[aa]
+
+    # Cosmo;
+    DL = MB.cosmo.luminosity_distance(zbes).value * MB.Mpc_cm # Luminositydistance in cm
+    Cons = (4.*np.pi*DL**2/(1.+zbes))
+    Tuni = MB.cosmo.age(zbes).value #, use_flat=True, **cosmo)
+    Tuni0 = (Tuni - age[:])
+
+    # Open summary;
+    file = 'summary_' + ID + '.fits'
+    fd   = fits.open(file)[1].data
+    #print(fits.open(file)[1].header)
+    Avtmp = fd['Av0']
+    uvtmp = fd['uv']
+    vjtmp = fd['vj']
+    #ax2.plot(vj[1],uv[1],color='gray',marker='s',ms=3)
+
+    # SFH
+    file = DIR_TMP + 'obshist_' + ID + '.fits'
+    fd   = fits.open(file)[1].data
+    age  = fd['age']
+    sfh  = fd['sfh']
+    zh   = fd['zh']
+
+    # Open FSPS temp;
+    file = DIR_TMP + 'obsspec_' + ID + '.fits'
+    fd   = fits.open(file)[1].data
+    wave = fd['wavelength']
+    nr   = fd['colnum']
+    uvall= age * 0 - 99
+    vjall= age * 0 - 99
+
+    delp = -10
+    flag = False
+    flag2= False
+    #for ii in range(1,len(age),10):
+    for ii in range(len(age)-1,-1,delp):
+        flux = fd['fspec_%d'%(ii)]
+        flux_att = madau_igm_abs(wave/(1.+zbes), flux, zbes)
+        flux_d, xxd, nrd = dust_calz(wave/(1.+zbes), flux_att, 0.0, nr)
+        #ax1.plot(xxd,flux_d,linestyle='-',lw=0.3+0.1*ii/len(age))
+        band0  = ['u','v','j']
+        lmconv,fconv = filconv(band0, xxd, flux_d, fil_path) # flux in fnu
+        uv = -2.5*np.log10(fconv[0]/fconv[1])
+        vj = -2.5*np.log10(fconv[1]/fconv[2])
+        uvall[ii] = uv
+        vjall[ii] = vj
+        #ax2.plot(vjall[ii],uvall[ii],marker='s',ms=5,linestyle='-',zorder=5)
+
+        if flag and not flag2:
+            flux_d, xxd, nrd = dust_calz(wave/(1.+zbes), flux_att, Avtmp[1], nr)
+            lmconv,fconv = filconv(band0, xxd, flux_d, fil_path) # flux in fnu
+            uv_av = -2.5*np.log10(fconv[0]/fconv[1])
+            vj_av = -2.5*np.log10(fconv[1]/fconv[2])
+            delvj, deluv = vj_av-vj, uv_av-uv
+            flag2 = True
+        flag = True
+
+    #import matplotlib.colormaps as cm
+    conuvj = (vjall>-90)&(uvall>-90)
+    ax2.plot(vjall[conuvj],uvall[conuvj],marker='s',markeredgecolor='k',color='none',ms=5,zorder=4)
+    ax2.scatter(vjall[conuvj],uvall[conuvj],marker='s',c=age[conuvj],cmap='jet',s=8,zorder=5)
+    ax2.plot(vjall[conuvj],uvall[conuvj],marker='',color='k',ms=3,linestyle='-',zorder=3)
+    ax3.plot(age,np.log10(sfh),color='b', linewidth=1, linestyle='-',label=r'$\log$SFR$/M_\odot$yr$^{-1}$')
+    ax3.plot(age,zh,color='r', linewidth=1, linestyle='-',label=r'$\log Z_*/Z_\odot$')
+
+    '''
+    ax1.set_xlim(1000,40000)
+    ax1.set_xscale('log')
+    ax1.set_ylim(1e-2,1e2)
+    ax1.set_yscale('log')
+    ax1.set_xlabel('Wavelength')
+    '''
+
+    ax2.set_xlim(-0.3,2.3)
+    ax2.set_ylim(-0.3,2.3)
+
+    ax2.set_xlabel('$V-J\,$/ mag')
+    ax2.set_ylabel('$U-V\,$/ mag')
+    ax3.set_xlabel('age / Gyr')
+    #ax3.set_ylabel('$\log$SFR$/M_\odot$yr$^{-1}$ or $\log Z_*/Z_\odot$')
+
+    ##
+    prog_path = '/Users/tmorishita/GitHub/gsf/gsf/example/misc/'
+    data_uvj = np.loadtxt(prog_path+'f2.cat',comments='#')
+    x=data_uvj[:,0]
+    y=data_uvj[:,1]
+    ax2.plot(x,y,color="gray",lw=1,ls="-")
+
+    data_uvj = np.loadtxt(prog_path+'g2.cat',comments='#')
+    x=data_uvj[:,0]
+    y=data_uvj[:,1]
+    ax2.plot(x,y,color="gray",lw=1,ls="-")
+
+    data_uvj = np.loadtxt(prog_path+'h2.cat',comments='#')
+    x=data_uvj[:,0]
+    y=data_uvj[:,1]
+    ax2.plot(x,y,color="gray",lw=1,ls="-")
+
+    try:
+        av=np.array([1.2,0.,delvj,deluv])
+        X,Y,U,V=zip(av)
+        ax2.quiver(X,Y,U,V,angles='xy',scale_units='xy',scale=1,linewidth=1,color="k")
+    except:
+        pass
+
+    ax2.text(-0.1,2.1,'Quiescent',fontsize=11,color='orangered')
+    ax2.text(1.3,-0.2,'Starforming',fontsize=11,color='royalblue')
+
+    ax3.legend(loc=3)
+    #plt.show()
+    plt.savefig('hist_' + ID + '.pdf')
diff --git a/build/lib/gsf/posterior_flexible.py b/build/lib/gsf/posterior_flexible.py
new file mode 100644
index 0000000..ea6a858
--- /dev/null
+++ b/build/lib/gsf/posterior_flexible.py
@@ -0,0 +1,303 @@
+import numpy as np
+import sys
+import scipy.integrate as integrate
+from scipy.integrate import cumtrapz
+from scipy import stats
+from numpy import exp as np_exp
+from numpy import log as np_log
+from scipy.special import erf
+
+
+class Post:
+    '''
+    Class function for MCMC
+    '''
+    def __init__(self, mainbody):
+        self.mb = mainbody
+        self.scale = 1
+        self.gauss_Mdyn = None
+        self.Na = len(self.mb.age)
+
+
+    def residual(self, pars, fy:float, ey:float, wht:float, f_fir:bool, out:bool=False, 
+        f_val:bool=False, f_penlize:bool=True, f_only_spec:bool=False):
+        '''
+        Parameters
+        ----------
+        out : 
+            model as second output. For lnprob func.
+        f_fir : bool
+            If dust component is on or off.
+
+        Returns
+        -------
+        residual of model and data.
+        '''
+        if f_val:
+            vals = pars
+        else:
+            vals = pars.valuesdict()
+
+        model, x1 = self.mb.fnc.get_template(vals)
+
+        if self.mb.f_dust:
+            model_dust, x1_dust = self.mb.fnc.tmp04_dust(vals)
+            n_optir = self.mb.n_optir #len(model)
+
+            # Add dust flux to opt/IR grid.
+            model[:] += model_dust[:n_optir]
+            # then append only FIR flux grid.
+            model = np.append(model,model_dust[n_optir:])
+            x1 = np.append(x1,x1_dust[n_optir:])
+
+        if self.mb.fneb:
+            n_optir = self.mb.n_optir
+            model_neb, x1_neb = self.mb.fnc.get_template(vals, f_neb=True)
+            model[:n_optir] += model_neb
+
+        if self.mb.ferr:
+            try:
+                logf = vals['logf']
+            except:
+                logf = -np.inf
+        else:
+            logf = -np.inf # temporary... (if f is param, then take from vals dictionary.)
+
+        if f_only_spec:
+            contmp = (self.mb.dict['NR']<10000)
+            fy = fy[contmp]
+            ey = ey[contmp]
+            wht = wht[contmp]
+            model = model[contmp]
+
+        sig = wht[:] * 0
+        con_res = (wht>0)
+        con_res_r = (wht==0)
+        sig[con_res] = ey[con_res]**2 + model[con_res]**2 * np_exp(logf)**2
+        sig[con_res_r] = wht[con_res_r] * 0 + np.inf
+
+        if fy is None:
+            print('Data is none')
+            resid = model
+        else:
+            resid = (model - fy) / np.sqrt(sig)
+
+        if self.mb.ferr and f_penlize:
+            # Penalize redisual;
+            tmp = (model - fy)**2 / sig + np_log(2*3.14*sig**2)
+            con_res = (tmp>0) & (~np.isinf(tmp))
+            resid[con_res] = np.sqrt(tmp[con_res])
+
+        if not out:
+            return resid # i.e. residual/sigma. Because is_weighted = True.
+        else:
+            return resid, model # i.e. residual/sigma. Because is_weighted = True.
+
+
+    def func_tmp(self, xint, eobs, fmodel):
+        '''
+        A function used for chi2 calculation for non-detection in lnprob.
+        '''
+        int_tmp = np_exp(-0.5 * ((xint-fmodel)/eobs)**2)
+        return int_tmp
+
+
+    def swap_pars(self, pars):
+        '''
+        '''
+        Amax = -99
+        aamax = 0
+        for aa in range(self.Na):
+            if pars['A%d'%aa]>Amax:
+                Amax = pars['A%d'%aa]
+                aamax = aa
+        if aamax>0:
+            Amax2 = pars['A%d'%(aamax-1)]
+            pars['A%d'%(aamax-1)] = Amax
+            pars['A%d'%aamax] = Amax2
+        return pars
+
+
+    def swap_pars_inv(self, pars):
+        '''
+        '''
+        Amax = -99
+        aamax = self.Na-1
+        for aa in range(self.Na):
+            if pars['A%d'%aa]>Amax:
+                Amax = pars['A%d'%aa]
+                aamax = aa
+        if aamax<self.Na-1:
+            Amax2 = pars['A%d'%(aamax+1)]
+            pars['A%d'%(aamax+1)] = Amax
+            pars['A%d'%aamax] = Amax2
+        return pars
+
+
+    def lnprob_emcee(self, pos, pars, fy:float, ey:float, wht:float, f_fir:bool, f_chind:bool=True, SNlim:float=1.0, f_scale:bool=False, 
+        lnpreject=-np.inf, f_like:bool=False, flat_prior:bool=False, gauss_prior:bool=True, f_val:bool=True, nsigma:float=1.0, out=None,
+        f_prior_sfh=False, alpha_sfh_prior=100, norder_sfh_prior=3):
+        '''
+        Parameters
+        ----------
+        f_chind : bool
+            If true, includes non-detection in likelihood calculation.
+        lnpreject : 
+            A replaced value when lnprob gets -inf value.
+        flat_prior : 
+            Assumes flat prior for Mdyn. Used only when MB.f_Mdyn==True.
+        gauss_prior : 
+            Assumes gaussian prior for Mdyn. Used only when MB.f_Mdyn==True.
+        pos : 
+            This is a critical parameter, to make use of EMCEE, while keeping pars a dictionary obtained by lmfit.
+            This is pos of sampler.run_mcmc(pos, self.nmc, progress=True).
+        out : 
+            Just for keywords.
+
+        Returns:
+        --------
+        If f_like, log Likelihood. Else, log Posterior prob.
+        '''
+        if f_val:
+            vals = pars
+        else:
+            vals = pars.valuesdict()
+        if self.mb.ferr == 1:
+            logf = vals['logf']
+        else:
+            logf = -np.inf
+
+        if False:
+            # Checking multiple peak model
+            if self.mb.SFH_FORM != -99 and self.mb.npeak>1:
+                for aa in range(0,self.mb.npeak-1,1):
+                    if vals['A'+str(aa)] > vals['A'+str(aa+1)]:
+                        return lnpreject
+
+        # Check range:
+        if not out==None:
+            ii = 0
+            for key in out.params:
+                if out.params[key].vary:
+                    cmin = out.params[key].min
+                    cmax = out.params[key].max
+                    if pos[ii]<cmin or pos[ii]>cmax or np.isnan(pos[ii]):
+                        return lnpreject
+                    vals[key].value = pos[ii]
+                    ii += 1
+
+        resid, model = self.residual(vals, fy, ey, wht, f_fir, out=True, f_val=f_val, f_penlize=False)
+
+        con_res = (model>=0) & (wht>0) & (fy>0) & (ey>0)
+        sig_con = np.sqrt(ey[con_res]**2 + model[con_res]**2 * np_exp(2 * logf))
+        chi_nd = 0.0
+
+        con_up = (ey>0) & (fy/ey<=SNlim)
+        if f_chind and len(fy[con_up])>0:
+            x_erf = (ey[con_up]/SNlim - model[con_up]) / (np.sqrt(2) * ey[con_up]/SNlim)
+            f_erf = erf(x_erf)
+            if np.min(f_erf) <= -1:
+                #return lnpreject
+                lnlike = -0.5 * (np.sum(resid[con_res]**2 + np_log(2 * 3.14 * sig_con**2)))
+            else:
+                chi_nd = np.sum( np_log(np.sqrt(np.pi / 2) * ey[con_up]/SNlim * (1 + f_erf)) )
+            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np_log(2 * 3.14 * sig_con**2)) - 2 * chi_nd)
+        else:
+            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np_log(2 * 3.14 * sig_con**2)))
+
+        # Scale likeligood; Do not make this happen yet.
+        if f_scale:
+            if self.scale == 1:
+                self.scale = np.abs(lnlike) * 0.001
+                print('scale is set to',self.scale)
+            lnlike += self.scale
+
+        if np.isinf(np.abs(lnlike)):
+            print('Error in lnlike')
+            return lnpreject
+
+        # If no prior, return log likeligood.
+        if f_like:
+            return lnlike
+
+        # Prior
+        respr = 0
+
+        if self.mb.f_Mdyn:
+            # Prior from dynamical mass:
+            if gauss_prior and self.gauss_Mdyn == None:
+                self.gauss_Mdyn = stats.norm(self.mb.logMdyn, self.mb.elogMdyn)
+            #logMtmp = self.get_mass(vals)
+            logMtmp = self.mb.logMtmp
+            #print(logMtmp)
+            if flat_prior:
+                if logMtmp > self.mb.logMdyn + self.mb.elogMdyn * nsigma:
+                    #pars = self.swap_pars(pars)
+                    #print(logMtmp, self.mb.logMdyn + self.mb.elogMdyn)
+                    return lnpreject
+                else:
+                    respr += 0
+            elif gauss_prior:
+                if logMtmp > self.mb.logMdyn + self.mb.elogMdyn * nsigma:
+                    #pars = self.swap_pars(pars)
+                    #return lnpreject
+                    pass
+                #elif logMtmp < self.mb.logMdyn - self.mb.elogMdyn * nsigma:
+                #    pars = self.swap_pars_inv(pars)
+                #    return lnpreject
+                p_gauss = self.gauss_Mdyn.pdf(logMtmp) #/ self.gauss_cnst
+                respr += np_log(p_gauss)
+
+        # Prior for redshift:
+        if self.mb.fzmc == 1:
+            zprior = self.mb.z_prior
+            prior = self.mb.p_prior
+
+            nzz = np.argmin(np.abs(zprior-vals['zmc']))
+            # For something unacceptable;
+            if nzz<0 or prior[nzz]<=0:
+                print('z Posterior unacceptable.')
+                return lnpreject
+            else:
+                respr += np_log(prior[nzz])
+
+        # Prior for SFH;
+        if f_prior_sfh:
+            respr += self.get_sfh_prior(vals, norder=norder_sfh_prior, alpha=alpha_sfh_prior)
+
+        lnposterior = lnlike + respr
+        if not np.isfinite(lnposterior):
+            print('Posterior unacceptable.')
+            return lnpreject
+
+        return lnposterior
+
+
+    def get_sfh_prior(self, vals, norder=3, alpha=100.0):
+        '''
+        Fit SFH with an n-order polynomial and reflect the residual to the prior.
+        Fit should be done in log-log space.
+        '''
+        t = np.log10(self.mb.age)
+        y = []
+        vary = []
+        for aa in range(self.Na):
+            y.append(vals['A%d'%aa])
+            vary.append(vals['A%d'%aa].vary)
+        y = np.asarray(y)
+
+        con = (vary)
+        z = np.polyfit(t[con], y[con], norder)
+        yfit = np.polyval(z,t[con])
+        yres = (y[con] - yfit)
+
+        if False:
+            import matplotlib.pyplot as plt
+            plt.plot(t,y)
+            plt.plot(t[con],yfit)
+            plt.show()
+            hoge
+
+        lnprior = -0.5 * (np.sum(yres**2) * alpha)
+
+        return lnprior
\ No newline at end of file
diff --git a/build/lib/gsf/posterior_nested.py b/build/lib/gsf/posterior_nested.py
new file mode 100644
index 0000000..3ba1761
--- /dev/null
+++ b/build/lib/gsf/posterior_nested.py
@@ -0,0 +1,239 @@
+import numpy as np
+import sys
+import scipy.integrate as integrate
+from scipy.integrate import cumtrapz
+from scipy import special,stats
+
+from .function import *
+
+class Post_nested:
+    '''
+    #####################
+    # Function for MCMC
+    #####################
+    '''
+    def __init__(self, mainbody, params=None):
+        self.mb = mainbody
+        self.params = params
+
+    def residual(self, pars, fy, ey, wht, f_fir=False, out=False):
+        '''
+        Parameters
+        ----------
+        out   : model as second output. For lnprob func.
+        f_fir : Bool. If dust component is on or off.
+
+        Returns
+        -------
+        residual of model and data.
+
+        '''
+
+        vals = pars #.valuesdict()
+        model, x1 = self.mb.fnc.get_template(vals)
+
+        if self.mb.f_dust:
+            model_dust, x1_dust = self.mb.fnc.tmp04_dust(vals)
+            n_optir = len(model)
+
+            # Add dust flux to opt/IR grid.
+            model[:] += model_dust[:n_optir]
+
+            # then append only FIR flux grid.
+            model = np.append(model,model_dust[n_optir:])
+            x1 = np.append(x1,x1_dust[n_optir:])
+
+        if self.mb.ferr:
+            try:
+                f = vals['f']
+            except:
+                f = 0
+        else:
+            f = 0 # temporary... (if f is param, then take from vals dictionary.)
+
+        #con_res = (model>0) & (wht>0) #& (ey>0)
+        sig = np.sqrt(1./wht + (f**2*model**2))
+
+        if fy is None:
+            print('Data is none')
+            resid = model #[con_res]
+        else:
+            resid = (model - fy) / sig
+
+        if not out:
+            return resid # i.e. residual/sigma. Because is_weighted = True.
+        else:
+            return resid, model # i.e. residual/sigma. Because is_weighted = True.
+
+
+    def get_dict(self, pars):
+
+        ii = 0
+        for key in self.params:
+            if self.params[key].vary:
+                self.params[key].value = pars[ii]
+                ii += 1
+        return self.params
+
+
+    def residual_nest(self, pars, fy, ey, wht, f_fir=False, out=False):
+        '''
+        Parameters
+        ----------
+        out   : model as second output. For lnprob func.
+        f_fir : Bool. If dust component is on or off.
+
+        Returns
+        -------
+        residual of model and data.
+
+        '''
+
+        vals = self.get_dict(pars)
+        model, x1 = self.mb.fnc.get_template(vals)
+
+        if self.mb.f_dust:
+            model_dust, x1_dust = self.mb.fnc.tmp04_dust(vals)
+            n_optir = len(model)
+
+            # Add dust flux to opt/IR grid.
+            model[:] += model_dust[:n_optir]
+            # then append only FIR flux grid.
+            model = np.append(model,model_dust[n_optir:])
+            x1 = np.append(x1,x1_dust[n_optir:])
+
+        if self.mb.ferr:
+            try:
+                f = vals['f']
+            except:
+                f = 0
+        else:
+            f = 0 # temporary... (if f is param, then take from vals dictionary.)
+
+        #con_res = (model>0) & (wht>0) #& (ey>0)
+        sig = np.sqrt(1./wht + (f**2*model**2))
+
+        if fy is None:
+            print('Data is none')
+            resid = model #[con_res]
+        else:
+            resid = (model - fy) / sig
+
+        if not out:
+            return resid # i.e. residual/sigma. Because is_weighted = True.
+        else:
+            return resid, model # i.e. residual/sigma. Because is_weighted = True.
+
+
+    def func_tmp(self, xint, eobs, fmodel):
+        '''
+        A function used for chi2 calculation for non-detection in lnprob.
+        '''
+        int_tmp = np.exp(-0.5 * ((xint-fmodel)/eobs)**2)
+        return int_tmp
+
+
+    def prior_transform(self, pars):
+        """
+        A function defining the tranform between the parameterisation in the unit hypercube
+        to the true parameters.
+
+        Args:
+            theta (tuple): a tuple containing the parameters.
+            
+        Returns:
+            tuple: a new tuple or array with the transformed parameters.
+        """
+
+        #prior = np.zeros(len(pars),'float')
+        if True:
+            ii = 0
+            for key in self.params:
+                if self.params[key].vary:
+                    cmin = self.params[key].min
+                    cmax = self.params[key].max
+                    cprim = pars[ii]
+                    pars[ii] = cprim*(cmax-cmin) + cmin
+                    ii += 1
+
+        '''
+        mmu = 0.     # mean of Gaussian prior on m
+        msigma = 10. # standard deviation of Gaussian prior on m
+        m = mmu + msigma*ndtri(mprime) # convert back to m
+        '''
+
+        return pars
+
+
+    def lnlike(self, pars, fy, ey, wht, f_fir, f_chind=True, SNlim=1.0, lnpreject=-np.inf):
+        '''
+        Returns
+        -------
+        log likelihood
+
+        Note:
+        =====
+        This is a copy from lnprob, with respr=0.
+
+        '''
+        vals = pars#.valuesdict()
+        if self.mb.ferr == 1:
+            f = vals['f']
+        else:
+            f = 0
+
+        resid, model = self.residual_nest(pars, fy, ey, wht, f_fir, out=True)
+        con_res = (model>=0) & (wht>0) & (fy>0) & (ey>0) # Instead of model>0; model>=0 is for Lyman limit where flux=0. This already exclude upper limit.
+        sig_con = np.sqrt(1./wht[con_res]+f**2*model[con_res]**2) # To avoid error message.
+        chi_nd = 0.0
+
+        con_up = (ey>0) & (fy/ey<=SNlim)
+        if f_chind and len(fy[con_up])>0:
+            x_erf = (ey[con_up]/SNlim - model[con_up]) / (np.sqrt(2) * ey[con_up]/SNlim)
+            f_erf = special.erf(x_erf)
+            if np.min(f_erf) <= -1:
+                return lnpreject
+            else:
+                chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey[con_up]/SNlim * (1 + f_erf)) )
+            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np.log(2 * 3.14 * sig_con**2)) - 2 * chi_nd)
+        else:
+            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np.log(2 * 3.14 * sig_con**2)))
+
+        return lnlike
+
+
+    def lnprob(self, pars, fy, ey, wht, f_fir, f_chind=True, SNlim=1.0, lnpreject=-np.inf):
+        '''
+        Returns:
+        ========
+        log posterior
+
+        '''
+
+        vals = pars #.valuesdict()
+        if self.mb.ferr == 1:
+            f = vals['f']
+        else:
+            f = 0
+
+        resid, model = self.residual(pars, fy, ey, wht, f_fir, out=True)
+        con_res = (model>=0) & (wht>0) & (fy>0) & (ey>0) # Instead of model>0; model>=0 is for Lyman limit where flux=0. This already exclude upper limit.
+        sig_con = np.sqrt(1./wht[con_res]+f**2*model[con_res]**2) # To avoid error message.
+        chi_nd = 0.0
+
+        con_up = (ey>0) & (fy/ey<=SNlim)
+        if f_chind and len(fy[con_up])>0:
+            x_erf = (ey[con_up]/SNlim - model[con_up]) / (np.sqrt(2) * ey[con_up]/SNlim)
+            f_erf = special.erf(x_erf)
+            if np.min(f_erf) <= -1:
+                return lnpreject
+            else:
+                chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey[con_up]/SNlim * (1 + f_erf)) )
+            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np.log(2 * 3.14 * sig_con**2)) - 2 * chi_nd)
+        else:
+            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np.log(2 * 3.14 * sig_con**2)))
+
+        respr = 0 # Flat prior...
+        lnposterior = lnlike + respr
+
+        return lnposterior
diff --git a/build/lib/gsf/writing.py b/build/lib/gsf/writing.py
new file mode 100644
index 0000000..0c93fc4
--- /dev/null
+++ b/build/lib/gsf/writing.py
@@ -0,0 +1,420 @@
+import numpy as np
+from astropy.io import fits
+from astropy import units as u
+import asdf
+import gsf
+from .function import filconv, calc_Dn4
+
+
+def get_param(self, res, fitc, tcalc=1., burnin=-1):
+    '''
+    Purpose
+    -------
+    Write a parameter file.
+
+    '''
+    print('##########################')
+    print('### Writing parameters ###')
+    print('##########################')
+    lib_all = self.lib_all
+
+    # Those are from redshiftfit;
+    zrecom = self.zgal
+    Czrec0 = self.Cz0
+    Czrec1 = self.Cz1
+    Czrec2 = self.Cz2
+
+    try:
+        z_cz = self.z_cz_prev
+        scl_cz0 = self.scl_cz0
+        scl_cz1 = self.scl_cz1
+        scl_cz2 = self.scl_cz2
+    except: # When redshiftfit is skipped.
+        z_cz = np.asarray([self.zgal,self.zgal,self.zgal])
+        scl_cz0 = np.asarray([self.Cz0,self.Cz0,self.Cz0])
+        scl_cz1 = np.asarray([self.Cz1,self.Cz1,self.Cz1])
+        scl_cz2 = np.asarray([self.Cz2,self.Cz2,self.Cz2])
+
+    tau0 = self.tau0
+    ID0 = self.ID
+    age = self.age
+    Zall = self.Zall
+
+    fnc  = self.fnc 
+    bfnc = self.bfnc 
+
+    DIR_TMP = self.DIR_TMP
+
+    fil_path = self.DIR_FILT
+    nmc  = self.nmc
+    ndim = self.ndim
+    nwalker = self.nwalk
+
+    #samples = res.chain[:, :, :].reshape((-1, ndim))
+    samples = res.flatchain
+    if burnin < 0:
+         burnin = int(samples.shape[0]/2.)
+
+    ##############################
+    # Best parameters
+    Amc = np.zeros((len(age),3), dtype='float')
+    Ab = np.zeros(len(age), dtype='float')
+    Zmc = np.zeros((len(age),3), dtype='float')
+    Zb = np.zeros(len(age), dtype='float')
+    NZbest = np.zeros(len(age), dtype='int')
+    AGEmc = np.zeros((len(age),3), dtype='float')
+    TAUmc = np.zeros((len(age),3), dtype='float')
+    if self.f_dust:
+        Mdustmc = np.zeros(3, dtype='float')
+        nTdustmc= np.zeros(3, dtype='float')
+        Tdustmc = np.zeros(3, dtype='float')
+    
+    # ASDF;
+    af = self.af
+    sedpar = af['ML']
+
+    ms = np.zeros(len(age), dtype='float')
+    try:
+        msmc0 = np.zeros(len(res.flatchain['A%d'%self.aamin[0]][burnin:]), dtype='float')
+    except:
+        msmc0 = np.zeros(len(res.flatchain['Av'][burnin:]), dtype='float')
+
+    for aa in range(len(age)):
+        try:
+            Ab[aa] = res.params['A'+str(aa)].value
+            Amc[aa,:] = np.percentile(res.flatchain['A'+str(aa)][burnin:], [16,50,84])
+        except:
+            Ab[aa] = -99
+            Amc[aa,:] = [-99,-99,-99]
+            pass
+        if aa == 0 or self.ZEVOL:
+            try:
+                Zb[aa] = res.params['Z'+str(aa)].value
+                Zmc[aa,:] = np.percentile(res.flatchain['Z'+str(aa)][burnin:], [16,50,84])
+            except:
+                ZFIX = self.ZFIX
+                Zb[aa] = ZFIX
+                Zmc[aa,:] = [ZFIX, ZFIX, ZFIX]
+        else:
+            Zb[aa] = Zb[0]
+            Zmc[aa,:] = Zmc[0,:]
+
+        if self.SFH_FORM == -99:
+            NZbest = bfnc.Z2NZ(Zb[aa])
+            ms[aa] = sedpar['ML_' +  str(NZbest)][aa]
+            try:
+                msmc0[:] += 10**res.flatchain['A' + str(aa)][burnin:] * ms[aa]
+            except:
+                pass
+        else:
+            taub = res.params['TAU'+str(aa)].value
+            ageb = res.params['AGE'+str(aa)].value
+            NZbest,NTAU,NAGE = bfnc.Z2NZ(Zb[aa],taub,ageb)
+            ms[aa] = sedpar['ML_%d_%d'%(NZbest, NTAU)][NAGE]
+            try:
+                msmc0[:] += 10**res.flatchain['A' + str(aa)][burnin:] * ms[aa]
+            except:
+                pass
+
+            # Do debug..
+            try:
+                AGEmc[aa,:] = np.percentile(res.flatchain['AGE'+str(aa)][burnin:], [16,50,84])
+            except:
+                AGEFIX = self.AGEFIX
+                AGEmc[aa,:] = [AGEFIX, AGEFIX, AGEFIX]
+
+            try:
+                TAUmc[aa,:] = np.percentile(res.flatchain['TAU'+str(aa)][burnin:], [16,50,84])
+            except:
+                TAUFIX = self.TAUFIX
+                TAUmc[aa,:] = [TAUFIX, TAUFIX, TAUFIX]
+
+    msmc = np.percentile(msmc0, [16,50,84])
+    try:
+        Avb = res.params['Av'].value
+        Avmc = np.percentile(res.flatchain['Av'][burnin:], [16,50,84])
+    except:
+        Avb = self.AVFIX
+        Avmc = [self.AVFIX,self.AVFIX,self.AVFIX]
+
+    AAvmc = [Avmc]
+    if self.fzmc:
+        zmc = np.percentile(res.flatchain['zmc'][burnin:], [16,50,84])
+    else:
+        zmc = z_cz
+
+    if self.ferr:
+        logf = np.percentile(res.flatchain['logf'][burnin:], [16,50,84])
+    else:
+        logf = [-99,-99,-99]
+
+    AA_tmp = np.zeros(len(age), dtype='float')
+    ZZ_tmp = np.zeros(len(age), dtype='float')
+    NZbest = np.zeros(len(age), dtype='int')
+
+    ############
+    # Get SN.
+    ############
+    nrs, lams, fsp, esp = self.data['spec_obs']['NR'], self.data['spec_obs']['x'], self.data['spec_obs']['fy'], self.data['spec_obs']['ey']
+
+    consp = (nrs<10000) & (lams/(1.+zrecom)>3600) & (lams/(1.+zrecom)<4200) & (esp>0)
+    NSN = len(fsp[consp])
+    if NSN>0:
+        SN = np.median((fsp/esp)[consp])
+    else:
+        SN = 0
+
+    ######################
+    # Write in Fits table.
+    ######################
+    # Header
+    prihdr = fits.Header()
+    prihdr['ID']     = ID0
+    prihdr['Cz0']    = Czrec0
+    prihdr['Cz1']    = Czrec1
+    prihdr['Cz2']    = Czrec2
+    prihdr['z']      = zrecom
+    prihdr['zmc']    = zmc[1]
+    prihdr['SN']     = SN
+    prihdr['nSN']    = NSN
+    prihdr['NDIM']   = ndim
+    prihdr['tcalc']  = (tcalc, 'in second')
+    prihdr['chi2']   = fitc[0]
+    prihdr['chi2nu'] = fitc[1]
+    prihdr['bic'] = res.bic
+    prihdr['nmc'] = nmc
+    prihdr['nwalker'] = nwalker
+    prihdr['version'] = gsf.__version__
+    prihdu = fits.PrimaryHDU(header=prihdr)
+
+    # Data
+    col01 = []
+    for aa in range(len(age)):
+        col50 = fits.Column(name='A'+str(aa), format='E', unit='', array=Amc[aa][:])
+        col01.append(col50)
+
+    for aa in range(len(AAvmc)):
+        col50 = fits.Column(name='Av'+str(aa), format='E', unit='mag', array=AAvmc[aa][:])
+        col01.append(col50)
+
+    for aa in range(len(Zmc)):
+        col50 = fits.Column(name='Z'+str(aa), format='E', unit='logZsun', array=Zmc[aa][:])
+        col01.append(col50)
+
+    for aa in range(len(AGEmc)):
+        col50 = fits.Column(name='AGE'+str(aa), format='E', unit='logGyr', array=AGEmc[aa][:])
+        col01.append(col50)
+        
+    for aa in range(len(TAUmc)):
+        col50 = fits.Column(name='TAU'+str(aa), format='E', unit='logGyr', array=TAUmc[aa][:])
+        col01.append(col50)
+        
+    if self.f_dust:
+        Mdust_per_temp = self.af['spec_dust']['Mdust']
+        if self.DT0 == self.DT1 or self.DT0 + self.dDT >= self.DT1:
+            nTdustmc[:] = [0,0,0]
+            Tdustmc[:] = [self.DT0,self.DT0,self.DT0]
+        else:
+            if not self.TDUSTFIX == None:
+                nTdustmc[:] = [self.NTDUST,self.NTDUST,self.NTDUST]
+            else:
+                nTdustmc[:] = np.percentile(res.flatchain['TDUST'][burnin:], [16,50,84])
+            Tdustmc[:] = self.DT0 + self.dDT * nTdustmc[:]
+
+        Mdustmc[:] = np.percentile(res.flatchain['MDUST'][burnin:], [16,50,84])
+        col50 = fits.Column(name='ADUST', format='E', unit='Msun', array=Mdustmc[:])
+        col01.append(col50)
+
+        # Then M2Light ratio;
+        Mdustmc[0] += np.log10(Mdust_per_temp[int(nTdustmc[0])])
+        Mdustmc[1] += np.log10(Mdust_per_temp[int(nTdustmc[1])])
+        Mdustmc[2] += np.log10(Mdust_per_temp[int(nTdustmc[2])])
+
+        col50 = fits.Column(name='MDUST', format='E', unit='Msun', array=Mdustmc[:])
+        col01.append(col50)
+        col50 = fits.Column(name='nTDUST', format='E', unit='', array=nTdustmc[:])
+        col01.append(col50)
+        col50 = fits.Column(name='TDUST', format='E', unit='K', array=Tdustmc[:])
+        col01.append(col50)
+
+    if self.fneb:
+        Anebmc = np.zeros(3, dtype=float)
+        logUmc = np.zeros(3, dtype=float)
+        Anebmc[:] = np.percentile(res.flatchain['Aneb'][burnin:], [16,50,84])
+        if not self.logUFIX == None:
+            logUmc[:] = [self.logUFIX,self.logUFIX,self.logUFIX]
+        else:
+            logUmc[:] = np.percentile(res.flatchain['logU'][burnin:], [16,50,84])
+        col50 = fits.Column(name='Aneb', format='E', unit='', array=Anebmc[:])
+        col01.append(col50)
+        col50 = fits.Column(name='logU', format='E', unit='', array=logUmc[:])
+        col01.append(col50)
+
+    # zmc
+    col50 = fits.Column(name='zmc', format='E', unit='', array=zmc[:])
+    col01.append(col50)
+
+    # Mass
+    col50 = fits.Column(name='ms', format='E', unit='Msun', array=msmc[:])
+    col01.append(col50)
+
+    # zmc
+    col50 = fits.Column(name='z_cz', format='E', unit='', array=z_cz[:])
+    col01.append(col50)
+
+    # Chi
+    col50 = fits.Column(name='chi', format='E', unit='', array=fitc[:])
+    col01.append(col50)
+
+    # C0 scale
+    col50 = fits.Column(name='Cscale0', format='E', unit='', array=scl_cz0[:])
+    col01.append(col50)
+
+    # C1 scale
+    col50 = fits.Column(name='Cscale1', format='E', unit='', array=scl_cz1[:])
+    col01.append(col50)
+
+    # C2 scale
+    col50 = fits.Column(name='Cscale2', format='E', unit='', array=scl_cz2[:])
+    col01.append(col50)
+
+    col50 = fits.Column(name='logf', format='E', unit='', array=logf)
+    col01.append(col50)
+
+    colms = fits.ColDefs(col01)
+    dathdu = fits.BinTableHDU.from_columns(colms)
+    hdu = fits.HDUList([prihdu, dathdu])
+    hdu.writeto(self.DIR_OUT + 'summary_' + ID0 + '.fits', overwrite=True)
+
+
+
+def get_index(mmax=300):
+    '''
+    Purpose
+    -------
+    Retrieve spectral indices from each realization.
+    '''
+
+    if nmc<mmax:
+        mmax = int(nmc/2.)
+
+    # RF color
+    uv = np.zeros(int(mmax), dtype='float')
+    bv = np.zeros(int(mmax), dtype='float')
+    vj = np.zeros(int(mmax), dtype='float')
+    zj = np.zeros(int(mmax), dtype='float')
+
+    # Lick indeces
+    Dn4  = np.zeros(int(mmax), dtype='float')
+    Mgb  = np.zeros(int(mmax), dtype='float')
+    Fe52 = np.zeros(int(mmax), dtype='float')
+    Fe53 = np.zeros(int(mmax), dtype='float')
+    Mg1  = np.zeros(int(mmax), dtype='float')
+    Mg2  = np.zeros(int(mmax), dtype='float')
+    G4300= np.zeros(int(mmax), dtype='float')
+    NaD  = np.zeros(int(mmax), dtype='float')
+    Hb   = np.zeros(int(mmax), dtype='float')
+
+    for mm in range(0,mmax,1):
+        rn = np.random.randint(len(res.flatchain['A0']))
+
+        for aa in range(len(age)):
+            fit_params['A'+str(aa)].value = res.flatchain['A%d'%(aa)][rn]
+            try:
+                fit_params['Z'+str(aa)].value = res.flatchain['Z%d'%(aa)][rn]
+            except:
+                pass
+        fit_params['Av'].value = res.flatchain['Av'][rn]
+
+        model2, xm_tmp = fnc.tmp04(fit_params, zrecom, lib_all)
+
+        # not necessary here.
+        if self.f_dust:
+            model2_dust, xm_tmp_dust = fnc.tmp04_dust(ID0, par_tmp, zrecom, lib_all, tau0=tau0)
+            model2 = np.append(model2,model2_dust)
+            xm_tmp = np.append(xm_tmp,xm_tmp_dust)
+
+        Dn4[mm]= calc_Dn4(xm_tmp, model2, zrecom) # Dust attenuation is not included?
+        lmrest = xm_tmp / (1. + zrecom)
+        band0  = ['u','b','v','j','sz']
+        try:
+            lmconv,fconv = filconv(band0, lmrest, model2, fil_path) # model2 in fnu
+            uv[mm] = -2.5*np.log10(fconv[0]/fconv[2])
+            bv[mm] = -2.5*np.log10(fconv[1]/fconv[2])
+            vj[mm] = -2.5*np.log10(fconv[2]/fconv[3])
+            zj[mm] = -2.5*np.log10(fconv[4]/fconv[3])
+        except:
+            uv[mm] = 0
+            bv[mm] = 0
+            vj[mm] = 0
+            zj[mm] = 0
+
+    conper = (Dn4>-99) #(Dn4>0)
+    Dnmc = np.percentile(Dn4[conper], [16,50,84])
+    uvmc = np.percentile(uv[conper], [16,50,84])
+    bvmc = np.percentile(bv[conper], [16,50,84])
+    vjmc = np.percentile(vj[conper], [16,50,84])
+    zjmc = np.percentile(zj[conper], [16,50,84])
+
+    Mgbmc  = np.percentile(Mgb[conper], [16,50,84])
+    Fe52mc = np.percentile(Fe52[conper], [16,50,84])
+    Fe53mc = np.percentile(Fe53[conper], [16,50,84])
+    G4300mc= np.percentile(G4300[conper], [16,50,84])
+    NaDmc  = np.percentile(NaD[conper], [16,50,84])
+    Hbmc   = np.percentile(Hb[conper], [16,50,84])
+    Mg1mc  = np.percentile(Mg1[conper], [16,50,84])
+    Mg2mc  = np.percentile(Mg2[conper], [16,50,84])
+
+
+    # Dn4000
+    col50 = fits.Column(name='Dn4', format='E', unit='', array=Dnmc[:])
+    col01.append(col50)
+
+
+    # U-V
+    col50 = fits.Column(name='uv', format='E', unit='mag', array=uvmc[:])
+    col01.append(col50)
+
+    # V-J
+    col50 = fits.Column(name='vj', format='E', unit='mag', array=vjmc[:])
+    col01.append(col50)
+
+    # B-V
+    col50 = fits.Column(name='bv', format='E', unit='mag', array=bvmc[:])
+    col01.append(col50)
+
+    # z-J
+    col50 = fits.Column(name='zj', format='E', unit='mag', array=zjmc[:])
+    col01.append(col50)
+
+    # Mgb
+    col50 = fits.Column(name='Mgb', format='E', unit='', array=Mgbmc[:])
+    col01.append(col50)
+
+    # Fe5270
+    col50 = fits.Column(name='Fe5270', format='E', unit='', array=Fe52mc[:])
+    col01.append(col50)
+
+    # Fe5335
+    col50 = fits.Column(name='Fe5335', format='E', unit='', array=Fe53mc[:])
+    col01.append(col50)
+
+    # G4300
+    col50 = fits.Column(name='G4300', format='E', unit='', array=G4300mc[:])
+    col01.append(col50)
+
+    # NaD
+    col50 = fits.Column(name='NaD', format='E', unit='', array=NaDmc[:])
+    col01.append(col50)
+
+    # Hb
+    col50 = fits.Column(name='Hb', format='E', unit='', array=Hbmc[:])
+    col01.append(col50)
+
+    # Mg1
+    col50 = fits.Column(name='Mg1', format='E', unit='', array=Mg1mc[:])
+    col01.append(col50)
+
+    # Mg2
+    col50 = fits.Column(name='Mg2', format='E', unit='', array=Mg2mc[:])
+    col01.append(col50)
diff --git a/build/lib/gsf/zfit.py b/build/lib/gsf/zfit.py
new file mode 100644
index 0000000..63f89fd
--- /dev/null
+++ b/build/lib/gsf/zfit.py
@@ -0,0 +1,190 @@
+import numpy as np
+import sys
+from lmfit import Model, Parameters, minimize, fit_report, Minimizer
+import scipy.interpolate as interpolate
+import matplotlib.pyplot as plt
+from .function import check_line_cz_man
+
+
+def lnprob_cz(pars, zprior, prior, zliml, zlimu, args, kwargs):
+    '''
+    '''
+    resid = residual_z(pars, *args, **kwargs) # i.e. (data - model) * wht
+    z = pars['z']
+    s_z = 1 #pars['f_cz']
+    resid *= 1/s_z
+    resid *= resid
+
+    if False:
+        nzz = np.argmin(np.abs(zprior-z))
+
+        # For something unacceptable;
+        if nzz<0 or zprior[nzz]<zliml or zprior[nzz]>zlimu or prior[nzz]<=0:
+            return -np.inf
+        else:
+            respr = np.log(prior[nzz])
+            resid += np.log(2 * np.pi * s_z**2)
+            return -0.5 * np.sum(resid) + respr
+    else:
+        resid += np.log(2 * np.pi * s_z**2)
+        return -0.5 * np.sum(resid)
+
+
+def residual_z(pars, xm_tmp, fm_tmp, xobs, fobs, eobs, NR, NRbb_lim=10000, include_photometry=True, f_line_check=False):
+    '''
+    '''
+    vals = pars.valuesdict()
+    z = vals['z']
+    Cz0s = vals['Cz0']
+    Cz1s = vals['Cz1']
+    Cz2s = vals['Cz2']
+
+    xm_s = xm_tmp * (1+z)
+    fint = interpolate.interp1d(xm_s, fm_tmp, kind='nearest', fill_value="extrapolate")
+    # @@@ Maybe for future consideration.
+    # fint = interpolate.interp1d(xm_s, fm_tmp, kind='linear', fill_value="extrapolate")
+    fm_s = fint(xobs)
+
+    con0 = (NR<1000)
+    fy0  = fobs[con0] * Cz0s
+    ey0  = eobs[con0] * Cz0s
+    con1 = (NR>=1000) & (NR<2000)
+    fy1  = fobs[con1] * Cz1s
+    ey1  = eobs[con1] * Cz1s
+    con2 = (NR>=2000) & (NR<NRbb_lim)
+    fy2  = fobs[con2] * Cz2s
+    ey2  = eobs[con2] * Cz2s
+    con_bb = (NR>=NRbb_lim) # BB
+    fy_bb  = fobs[con_bb]
+    ey_bb  = eobs[con_bb]
+
+    fy01 = np.append(fy0,fy1)
+    ey01 = np.append(ey0,ey1)
+    fy02 = np.append(fy01,fy2)
+    ey02 = np.append(ey01,ey2)
+
+    if include_photometry and len(fy_bb)>0:
+        fcon = np.append(fy02,fy_bb)
+        eycon = np.append(ey02,ey_bb)
+        wht = 1./np.square(eycon)
+    else:
+        fcon = fy02
+        eycon = ey02
+        wht = 1./np.square(eycon)
+
+    if f_line_check:
+        try:
+            wht2, ypoly = check_line_cz_man(fcon, xobs, wht, fm_s, z)
+        except:
+            wht2 = wht
+    else:
+        wht2 = wht
+        
+    if fobs is None:
+        print('Data is none')
+        return fm_s
+    else:
+        return (fm_s - fcon) * np.sqrt(wht2) # i.e. residual/sigma
+
+
+def check_redshift(fobs, eobs, xobs, fm_tmp, xm_tmp, zbest, zprior, prior, NR, zliml, zlimu, \
+    nmc_cz=100, nwalk_cz=10, nthin=5, f_line_check=False, f_vary=True, NRbb_lim=10000, include_photometry=True):
+    '''
+    Purpose
+    -------
+    Fit observed flux with a template to get redshift probability.
+
+    Parameters
+    ----------
+    zbest : 
+        Initial value for redshift.
+    zprior : 
+        Redshift grid for prior.
+    prior : 
+        Prior for redshift determination. E.g., Eazy z-probability.
+    zliml : 
+        Lowest redshift for fitting range.
+    zlimu : 
+        Highest redshift for fitting range.
+    f_vary : bool
+        If want to fix redshift.
+    fm_tmp :
+        Template spectrum at RF.
+    xm_tmp :
+        Template spectrum at RF.
+    fobs : 
+        Observed spectrum. (Already scaled with Cz0prev.)
+    eobs : 
+        Observed spectrum. (Already scaled with Cz0prev.)
+    xobs : 
+        Observed spectrum. (Already scaled with Cz0prev.)
+
+
+    Returns
+    -------
+    res_cz :
+
+    fitc_cz :
+
+    '''
+    if zliml == None or zlimu == None:
+        print('z range is not set for the z-fit function. Exiting.')
+        print('Specify `ZMCMIN` and `ZMCMIN` in your input file.')
+        sys.exit()
+
+    fit_par_cz = Parameters()
+    fit_par_cz.add('z', value=zbest, min=zliml, max=zlimu, vary=f_vary)
+    fit_par_cz.add('Cz0', value=1, min=0.5, max=1.5)
+    fit_par_cz.add('Cz1', value=1, min=0.5, max=1.5)
+    fit_par_cz.add('Cz2', value=1, min=0.5, max=1.5)
+
+    # Get Best fit
+    args_res = (xm_tmp, fm_tmp, xobs, fobs, eobs, NR)
+    kwargs_res = {'include_photometry':include_photometry, 'f_line_check':f_line_check, 'NRbb_lim':NRbb_lim}
+
+    out_cz  = minimize(residual_z, fit_par_cz, args=args_res, method='nelder')
+    keys = fit_report(out_cz).split('\n')
+    for key in keys:
+        if key[4:7] == 'chi':
+            skey = key.split(' ')
+            csq = float(skey[14])
+        if key[4:7] == 'red':
+            skey = key.split(' ')
+            rcsq = float(skey[7])
+
+    fitc_cz = [csq, rcsq] # Chi2, Reduced-chi2
+
+    mini_cz = Minimizer(lnprob_cz, out_cz.params, (zprior, prior, zliml, zlimu, args_res, kwargs_res))
+    res_cz = mini_cz.emcee(burn=int(nmc_cz/2), steps=nmc_cz, thin=nthin, nwalkers=nwalk_cz, params=out_cz.params, is_weighted=True)
+
+    return res_cz, fitc_cz
+
+
+def get_chi2(zz_prob, fy_cz, ey_cz, x_cz, fm_tmp, xm_tmp, file_zprob, rms_lim=1e4):
+    '''
+    zz_prob : float array
+        redshift array for fit.
+    fy_cz, ey_cz, x_cz : 
+        observed values.
+    fm_tmp, xm_tmp :
+        template.
+    file_zprob : str
+        output file
+    '''
+    mask = (ey_cz<rms_lim)
+    prob_cz = np.zeros(len(zz_prob), float)
+    fw = open(file_zprob, 'w')
+    fw.write('# z p(z)\n')
+    for ii in range(len(zz_prob)):
+        z = zz_prob[ii]
+        xm_s = xm_tmp * (1+z)
+        fint = interpolate.interp1d(xm_s, fm_tmp, kind='nearest', fill_value="extrapolate")
+        fm_s = fint(x_cz)
+
+        wht = 1./np.square(ey_cz)
+        # print(np.nansum((fm_s - fy_cz)**2 * (wht)))
+        lnprob_cz = -0.5 * np.nansum( np.square((fm_s - fy_cz)[mask] * np.sqrt(wht[mask])) ) # i.e. (residual/sigma)^2
+        prob_cz[ii] = np.exp(lnprob_cz)
+        fw.write('%.3f %.3e\n'%(z,prob_cz[ii]))
+        
+    fw.close()
diff --git a/dist/gsf-1.7.6.dev20+g1e4de8a.d20221210-py3.9.egg b/dist/gsf-1.7.6.dev20+g1e4de8a.d20221210-py3.9.egg
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diff --git a/docs/_build/doctrees/index.doctree b/docs/_build/doctrees/index.doctree
index a8a14090164edc0cc9b9c966772bf8e845b872af..bada2b091ae688c4fb6ead7adee2b3da75039101 100644
GIT binary patch
delta 654
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delta 602
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diff --git a/docs/_build/doctrees/parameters.doctree b/docs/_build/doctrees/parameters.doctree
index 2c0131be086e151451bc9a7ea941df269ba6ae7a..e995a1b0861ca30f40537aa642d1735dcb6738bd 100644
GIT binary patch
delta 3680
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diff --git a/docs/_build/doctrees/redshift_fitting.doctree b/docs/_build/doctrees/redshift_fitting.doctree
new file mode 100644
index 0000000000000000000000000000000000000000..e8b181dc70146caa8dfbf470fca6bb8adeaf27b4
GIT binary patch
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diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt
index 248725a..e5d6ad3 100644
--- a/docs/_build/html/_sources/index.rst.txt
+++ b/docs/_build/html/_sources/index.rst.txt
@@ -19,6 +19,7 @@ gsf's documentation
    fitting.rst
    output.rst
    prior.rst
+   redshift_fitting.rst
 
    auto/modules
 
diff --git a/docs/_build/html/_sources/parameters.rst.txt b/docs/_build/html/_sources/parameters.rst.txt
index 52f1070..cb5b189 100644
--- a/docs/_build/html/_sources/parameters.rst.txt
+++ b/docs/_build/html/_sources/parameters.rst.txt
@@ -173,6 +173,8 @@ An example config file can be generated by executing the following command (TBD)
      - 
      - 
 
+Read  :ref:`Redshift Fitting <redshift_fitting>` for a caveat about making redshift a free parameter.
+
 
 **Parameters specific for the target:**
 
diff --git a/docs/_build/html/_sources/redshift_fitting.rst.txt b/docs/_build/html/_sources/redshift_fitting.rst.txt
new file mode 100644
index 0000000..9cc1323
--- /dev/null
+++ b/docs/_build/html/_sources/redshift_fitting.rst.txt
@@ -0,0 +1,22 @@
+.. _redshift_fitting:
+
+Redshift Fitting
+================
+While gsf is now able to search the redshift grid during the fiting process, 
+it was not originally designed to efficiently estimate the posterior for redshift. 
+Users can turn this redshift fitting functionality by turning ``F_ZMC`` to 1, 
+with ``ZMCMIN`` and ``ZMCMAX`` properly specified too, in the input configuration file.
+
+To minimize the disk space usage, gsf generates templates only at ``ZMC`` specified in the input file. 
+How it works with redshift search is, during the fitting process, when gsf detects a large shift 
+(specifically, larger than the limiting value specified by ``deltaz_lim`` in `function_class`) in redshift from ``ZMC``,
+gsf calls the filter convolution function (`function.filconv`) to recalculate the model broadband flux for the given model, 
+to better reflect the current place of each walker in the parameter space. ``ZMC`` is also replaced to this new redshift at 
+this point. When the shift is small, gsf interpolates the model fluxes for the given set of filters 
+after applying the shift to wavelength. 
+
+This extra step allows more accurate parameter search for gsf with redshift as a variable, but significantly slows down the computing speed, by a factor up to 10. 
+This overhead is primarily caused by the number of data points in the model templates and filter curves. 
+To minimize the computation time, users may want to consder using sparse templates, by using ``--delwave`` argument (delta wave, in Aungstrome) when they run run_gsf.py 
+script, or by directly passing the same parameter to `maketemp` function.
+
diff --git a/docs/_build/html/auto/gsf.html b/docs/_build/html/auto/gsf.html
index 3228c32..642d197 100644
--- a/docs/_build/html/auto/gsf.html
+++ b/docs/_build/html/auto/gsf.html
@@ -50,6 +50,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../fitting.html">Fitting process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../output.html">Output files</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../prior.html">Prior</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../redshift_fitting.html">Redshift Fitting</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="modules.html">gsf</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">gsf package</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#submodules">Submodules</a></li>
@@ -751,8 +752,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.function_class.Func.get_template">
 <span class="sig-name descname"><span class="pre">get_template</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_nrd</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_apply_dust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_IGM</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deltaz_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_neb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EBVratio</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2.27</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.get_template" title="Permalink to this definition"></a></dt>
-<dd><p>Makes model template for a nebular emission.
-This is only for nubular template, with len(A) = 1. Thus, aa=0 during the code.</p>
+<dd><p>Makes model template for a given parameter set, <code class="docutils literal notranslate"><span class="pre">par</span></code>.</p>
 <dl class="simple">
 <dt>nprec<span class="classifier">int</span></dt><dd><p>Precision when redshift is refined.</p>
 </dd>
@@ -1000,7 +1000,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dt class="sig sig-object py" id="gsf.maketmp_filt.check_library">
 <span class="sig-prename descclassname"><span class="pre">gsf.maketmp_filt.</span></span><span class="sig-name descname"><span class="pre">check_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MB</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">af</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nround</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.maketmp_filt.check_library" title="Permalink to this definition"></a></dt>
 <dd><p>Check library if it has a consistency setup as input file.</p>
-<p>True is no problem.</p>
+<p>flag : bool</p>
 </dd></dl>
 
 <dl class="py function">
@@ -1018,8 +1018,9 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dt>phi<span class="classifier">float</span></dt><dd><p>Eq.34 of Draine &amp; Li 2007. (default: 0.055)
 ~0.055 g / (ergs/s)</p>
 </dd>
+<dt>DL<span class="classifier">float</span></dt><dd><p>in cm.</p>
+</dd>
 </dl>
-<p>DL : in cm.</p>
 <p>Interpolated dust emission in Fnu of m0=25.0. In units of Fnu/Msun</p>
 <p>umins = [‘0.10’, ‘0.15’, ‘0.20’, ‘0.30’, ‘0.40’, ‘0.50’, ‘0.70’, ‘0.80’, ‘1.00’, ‘1.20’,            ‘1.50’, ‘2.00’, ‘2.50’, ‘3.00’, ‘4.00’, ‘5.00’, ‘7.00’, ‘8.00’, ‘10.0’, ‘12.0’, ‘15.0’,            ‘20.0’, ‘25.0’]
 umaxs = [‘1e3’, ‘1e4’, ‘1e5’, ‘1e6’, ‘1e7’]</p>
diff --git a/docs/_build/html/auto/modules.html b/docs/_build/html/auto/modules.html
index 57d715c..e5cbc78 100644
--- a/docs/_build/html/auto/modules.html
+++ b/docs/_build/html/auto/modules.html
@@ -21,7 +21,7 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="gsf package" href="gsf.html" />
-    <link rel="prev" title="Prior" href="../prior.html" /> 
+    <link rel="prev" title="Redshift Fitting" href="../redshift_fitting.html" /> 
 </head>
 
 <body class="wy-body-for-nav"> 
@@ -50,6 +50,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../fitting.html">Fitting process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../output.html">Output files</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../prior.html">Prior</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../redshift_fitting.html">Redshift Fitting</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">gsf</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="gsf.html">gsf package</a></li>
 </ul>
@@ -115,7 +116,7 @@ <h1>gsf<a class="headerlink" href="#gsf" title="Permalink to this headline"><
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="../prior.html" class="btn btn-neutral float-left" title="Prior" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../redshift_fitting.html" class="btn btn-neutral float-left" title="Redshift Fitting" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
         <a href="gsf.html" class="btn btn-neutral float-right" title="gsf package" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html
index 81acd0d..bc2ce22 100644
--- a/docs/_build/html/genindex.html
+++ b/docs/_build/html/genindex.html
@@ -47,6 +47,7 @@
 <li class="toctree-l1"><a class="reference internal" href="fitting.html">Fitting process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="output.html">Output files</a></li>
 <li class="toctree-l1"><a class="reference internal" href="prior.html">Prior</a></li>
+<li class="toctree-l1"><a class="reference internal" href="redshift_fitting.html">Redshift Fitting</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto/modules.html">gsf</a></li>
 </ul>
 
diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html
index 187064d..e505088 100644
--- a/docs/_build/html/index.html
+++ b/docs/_build/html/index.html
@@ -49,6 +49,7 @@
 <li class="toctree-l1"><a class="reference internal" href="fitting.html">Fitting process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="output.html">Output files</a></li>
 <li class="toctree-l1"><a class="reference internal" href="prior.html">Prior</a></li>
+<li class="toctree-l1"><a class="reference internal" href="redshift_fitting.html">Redshift Fitting</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto/modules.html">gsf</a></li>
 </ul>
 
@@ -90,6 +91,7 @@ <h1>gsf’s documentation<a class="headerlink" href="#gsf-s-documentation" title
 <li class="toctree-l1"><a class="reference internal" href="fitting.html">Fitting process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="output.html">Output files</a></li>
 <li class="toctree-l1"><a class="reference internal" href="prior.html">Prior</a></li>
+<li class="toctree-l1"><a class="reference internal" href="redshift_fitting.html">Redshift Fitting</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto/modules.html">gsf</a></li>
 </ul>
 </div>
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diff --git a/docs/_build/html/parameters.html b/docs/_build/html/parameters.html
index ccc5150..fda014b 100644
--- a/docs/_build/html/parameters.html
+++ b/docs/_build/html/parameters.html
@@ -50,6 +50,7 @@
 <li class="toctree-l1"><a class="reference internal" href="fitting.html">Fitting process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="output.html">Output files</a></li>
 <li class="toctree-l1"><a class="reference internal" href="prior.html">Prior</a></li>
+<li class="toctree-l1"><a class="reference internal" href="redshift_fitting.html">Redshift Fitting</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto/modules.html">gsf</a></li>
 </ul>
 
@@ -304,6 +305,7 @@ <h1>Paramter Description<a class="headerlink" href="#paramter-description" title
 </tr>
 </tbody>
 </table>
+<p>Read  <a class="reference internal" href="redshift_fitting.html#redshift-fitting"><span class="std std-ref">Redshift Fitting</span></a> for a caveat about making redshift a free parameter.</p>
 <p><strong>Parameters specific for the target:</strong></p>
 <table class="colwidths-given tight-table docutils align-default">
 <colgroup>
diff --git a/docs/_build/html/py-modindex.html b/docs/_build/html/py-modindex.html
index 7adea61..7de2a19 100644
--- a/docs/_build/html/py-modindex.html
+++ b/docs/_build/html/py-modindex.html
@@ -50,6 +50,7 @@
 <li class="toctree-l1"><a class="reference internal" href="fitting.html">Fitting process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="output.html">Output files</a></li>
 <li class="toctree-l1"><a class="reference internal" href="prior.html">Prior</a></li>
+<li class="toctree-l1"><a class="reference internal" href="redshift_fitting.html">Redshift Fitting</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto/modules.html">gsf</a></li>
 </ul>
 
diff --git a/docs/_build/html/redshift_fitting.html b/docs/_build/html/redshift_fitting.html
new file mode 100644
index 0000000..44d3650
--- /dev/null
+++ b/docs/_build/html/redshift_fitting.html
@@ -0,0 +1,131 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
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+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Redshift Fitting &mdash; gsf 1.4 documentation</title>
+      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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+<li class="toctree-l1"><a class="reference internal" href="overview.html">Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="install.html">Installing gsf</a></li>
+<li class="toctree-l1"><a class="reference internal" href="example.html">Examples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="parameters.html">Paramter Description</a></li>
+<li class="toctree-l1"><a class="reference internal" href="input.html">Input files</a></li>
+<li class="toctree-l1"><a class="reference internal" href="templates.html">Generating templates</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fitting.html">Fitting process</a></li>
+<li class="toctree-l1"><a class="reference internal" href="output.html">Output files</a></li>
+<li class="toctree-l1"><a class="reference internal" href="prior.html">Prior</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Redshift Fitting</a></li>
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+            <a href="_sources/redshift_fitting.rst.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="redshift-fitting">
+<span id="id1"></span><h1>Redshift Fitting<a class="headerlink" href="#redshift-fitting" title="Permalink to this headline"></a></h1>
+<p>While gsf is now able to search the redshift grid during the fiting process,
+it was not originally designed to efficiently estimate the posterior for redshift.
+Users can turn this redshift fitting functionality by turning <code class="docutils literal notranslate"><span class="pre">F_ZMC</span></code> to 1,
+with <code class="docutils literal notranslate"><span class="pre">ZMCMIN</span></code> and <code class="docutils literal notranslate"><span class="pre">ZMCMAX</span></code> properly specified too, in the input configuration file.</p>
+<p>To minimize the disk space usage, gsf generates templates only at <code class="docutils literal notranslate"><span class="pre">ZMC</span></code> specified in the input file.
+How it works with redshift search is, during the fitting process, when gsf detects a large shift
+(specifically, larger than the limiting value specified by <code class="docutils literal notranslate"><span class="pre">deltaz_lim</span></code> in <cite>function_class</cite>) in redshift from <code class="docutils literal notranslate"><span class="pre">ZMC</span></code>,
+gsf calls the filter convolution function (<cite>function.filconv</cite>) to recalculate the model broadband flux for the given model,
+to better reflect the current place of each walker in the parameter space. <code class="docutils literal notranslate"><span class="pre">ZMC</span></code> is also replaced to this new redshift at
+this point. When the shift is small, gsf interpolates the model fluxes for the given set of filters
+after applying the shift to wavelength.</p>
+<p>This extra step allows more accurate parameter search for gsf with redshift as a variable, but significantly slows down the computing speed, by a factor up to 10.
+This overhead is primarily caused by the number of data points in the model templates and filter curves.
+To minimize the computation time, users may want to consder using sparse templates, by using <code class="docutils literal notranslate"><span class="pre">--delwave</span></code> argument (delta wave, in Aungstrome) when they run run_gsf.py
+script, or by directly passing the same parameter to <cite>maketemp</cite> function.</p>
+</section>
+
+
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diff --git a/docs/_build/html/search.html b/docs/_build/html/search.html
index 0098734..72478b2 100644
--- a/docs/_build/html/search.html
+++ b/docs/_build/html/search.html
@@ -50,6 +50,7 @@
 <li class="toctree-l1"><a class="reference internal" href="fitting.html">Fitting process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="output.html">Output files</a></li>
 <li class="toctree-l1"><a class="reference internal" href="prior.html">Prior</a></li>
+<li class="toctree-l1"><a class="reference internal" href="redshift_fitting.html">Redshift Fitting</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto/modules.html">gsf</a></li>
 </ul>
 
diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js
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\ No newline at end of file
diff --git a/docs/index.rst b/docs/index.rst
index 248725a..e5d6ad3 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -19,6 +19,7 @@ gsf's documentation
    fitting.rst
    output.rst
    prior.rst
+   redshift_fitting.rst
 
    auto/modules
 
diff --git a/docs/parameters.rst b/docs/parameters.rst
index 52f1070..cb5b189 100644
--- a/docs/parameters.rst
+++ b/docs/parameters.rst
@@ -173,6 +173,8 @@ An example config file can be generated by executing the following command (TBD)
      - 
      - 
 
+Read  :ref:`Redshift Fitting <redshift_fitting>` for a caveat about making redshift a free parameter.
+
 
 **Parameters specific for the target:**
 
diff --git a/docs/redshift_fitting.rst b/docs/redshift_fitting.rst
new file mode 100644
index 0000000..9cc1323
--- /dev/null
+++ b/docs/redshift_fitting.rst
@@ -0,0 +1,22 @@
+.. _redshift_fitting:
+
+Redshift Fitting
+================
+While gsf is now able to search the redshift grid during the fiting process, 
+it was not originally designed to efficiently estimate the posterior for redshift. 
+Users can turn this redshift fitting functionality by turning ``F_ZMC`` to 1, 
+with ``ZMCMIN`` and ``ZMCMAX`` properly specified too, in the input configuration file.
+
+To minimize the disk space usage, gsf generates templates only at ``ZMC`` specified in the input file. 
+How it works with redshift search is, during the fitting process, when gsf detects a large shift 
+(specifically, larger than the limiting value specified by ``deltaz_lim`` in `function_class`) in redshift from ``ZMC``,
+gsf calls the filter convolution function (`function.filconv`) to recalculate the model broadband flux for the given model, 
+to better reflect the current place of each walker in the parameter space. ``ZMC`` is also replaced to this new redshift at 
+this point. When the shift is small, gsf interpolates the model fluxes for the given set of filters 
+after applying the shift to wavelength. 
+
+This extra step allows more accurate parameter search for gsf with redshift as a variable, but significantly slows down the computing speed, by a factor up to 10. 
+This overhead is primarily caused by the number of data points in the model templates and filter curves. 
+To minimize the computation time, users may want to consder using sparse templates, by using ``--delwave`` argument (delta wave, in Aungstrome) when they run run_gsf.py 
+script, or by directly passing the same parameter to `maketemp` function.
+
diff --git a/gsf.egg-info/PKG-INFO b/gsf.egg-info/PKG-INFO
new file mode 100644
index 0000000..62ddb2b
--- /dev/null
+++ b/gsf.egg-info/PKG-INFO
@@ -0,0 +1,21 @@
+Metadata-Version: 2.1
+Name: gsf
+Version: 1.7.6.dev20+g1e4de8a.d20221210
+Summary: SED Fitting Code
+Home-page: https://github.com/mtakahiro
+Download-URL: https://github.com/
+Author: Takahiro Morishita
+Author-email: takahiro@ipac.caltech.edu
+License: IPAC
+Platform: UNKNOWN
+Classifier: Development Status :: 1 - Planning
+Classifier: Intended Audience :: Science/Research
+Classifier: Topic :: Scientific/Engineering :: Astronomy
+Requires: lmfit
+Requires: fsps
+Requires: emcee
+Requires: corner
+License-File: LICENSE
+
+UNKNOWN
+
diff --git a/gsf.egg-info/SOURCES.txt b/gsf.egg-info/SOURCES.txt
new file mode 100644
index 0000000..4d2f3e5
--- /dev/null
+++ b/gsf.egg-info/SOURCES.txt
@@ -0,0 +1,633 @@
+.gitattributes
+.gitignore
+HISTORY.rst
+LICENSE
+README.rst
+requirements.txt
+sample.png
+setup.py
+docs/Makefile
+docs/conf.py
+docs/default.profraw
+docs/example.rst
+docs/fitting.rst
+docs/index.rst
+docs/input.rst
+docs/install.rst
+docs/make.bat
+docs/output.rst
+docs/overview.rst
+docs/parameters.rst
+docs/prior.rst
+docs/templates.rst
+docs/_build/doctrees/corner.doctree
+docs/_build/doctrees/environment.pickle
+docs/_build/doctrees/example.doctree
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+docs/_build/doctrees/prior.doctree
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+docs/_build/html/.buildinfo
+docs/_build/html/example.html
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+docs/_build/html/genindex.html
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+docs/_build/html/objects.inv
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+docs/_build/html/search.html
+docs/_build/html/searchindex.js
+docs/_build/html/templates.html
+docs/_build/html/_images/flowchart.png
+docs/_build/html/_images/pixel.png
+docs/_build/html/_sources/corner.rst.txt
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+example/filter/README_ALMA
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+example/filter/filt_Sep20.lst
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+gsf/basic_func.py
+gsf/fitting.py
+gsf/function.py
+gsf/function_class.py
+gsf/function_igm.py
+gsf/gsf.py
+gsf/maketmp_filt.py
+gsf/maketmp_z0.py
+gsf/maketmp_z0_tau.py
+gsf/minimizer.py
+gsf/plot_sed.py
+gsf/plot_sfh.py
+gsf/posterior_flexible.py
+gsf/posterior_nested.py
+gsf/writing.py
+gsf/zfit.py
+gsf.egg-info/PKG-INFO
+gsf.egg-info/SOURCES.txt
+gsf.egg-info/dependency_links.txt
+gsf.egg-info/not-zip-safe
+gsf.egg-info/top_level.txt
+gsf/tests/__init__.py
+gsf/tests/test_fitting.py
+gsf/tests/test_function.py
\ No newline at end of file
diff --git a/gsf.egg-info/dependency_links.txt b/gsf.egg-info/dependency_links.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/gsf.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/gsf.egg-info/not-zip-safe b/gsf.egg-info/not-zip-safe
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/gsf.egg-info/not-zip-safe
@@ -0,0 +1 @@
+
diff --git a/gsf.egg-info/top_level.txt b/gsf.egg-info/top_level.txt
new file mode 100644
index 0000000..6936b2c
--- /dev/null
+++ b/gsf.egg-info/top_level.txt
@@ -0,0 +1 @@
+gsf
diff --git a/gsf/fitting.py b/gsf/fitting.py
index ad33581..e8478e5 100644
--- a/gsf/fitting.py
+++ b/gsf/fitting.py
@@ -647,6 +647,8 @@ def update_input(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:
 
 
     def get_lines(self, LW0):
+        '''
+        '''
         fLW = np.zeros(len(LW0), dtype='int')
         LW = LW0
         return LW, fLW
@@ -654,8 +656,6 @@ def get_lines(self, LW0):
 
     def read_data(self, Cz0:float, Cz1:float, Cz2:float, zgal:float, add_fir:bool=False, idman=None):
         '''
-        Purpose
-        -------
         Read in observed data. Not model fluxes.
 
         Parameters
@@ -883,8 +883,6 @@ def fit_redshift(self, xm_tmp, fm_tmp, delzz=0.01, ezmin=0.01, zliml=0.01,
         zlimu=None, snlim=0, priors=None, f_bb_zfit=True, f_line_check=False, 
         f_norm=True, f_lambda=False, zmax=20, include_bb=False, f_exclude_negative=False):
         '''
-        Purpose
-        -------
         Find the best-fit redshift, before going into a big fit, through an interactive inspection.
         This module is effective only when spec data is provided.
         When spectrun is provided, this does redshift fit, but **by using the SED model guessed from the BB photometry**.
@@ -1197,8 +1195,6 @@ def fit_redshift(self, xm_tmp, fm_tmp, delzz=0.01, ezmin=0.01, zliml=0.01,
 
     def get_zdist(self, f_interact=False, f_ascii=True):
         '''
-        Purpose
-        -------
         Saves a plot of z-distribution.
 
         Parameters
@@ -2063,56 +2059,3 @@ def __init__(self, flatchain, var_names, params_value, res):
                 print('Terminating process.')
                 print('\n\n')
                 return False
-
-    """
-    def quick_fit(self, specplot=1, sigz=1.0, ezmin=0.01, ferr=0, f_move=False, f_get_templates=False, Zini=None):
-        '''
-        Fits input data with a prepared template library, to get a chi-min result.
-
-        Parameters:
-        -----------
-        Zini : array
-            Array for initial values for metallicity.
-
-        Returns
-        -------
-        out, chidef, Zbest, xm_tmp, fm_tmp (if f_get_templates is set True).
-        '''
-        from .posterior_flexible import Post
-        print('#########')
-        print('Quick fit')
-        print('#########\n')
-
-        # Call likelihood/prior/posterior function;
-        class_post = Post(self)
-
-        # Prepare library, data, etc.
-        self.prepare_class()
-
-        # Initial Z:
-        if Zini == None:
-            Zini = self.Zall
-
-        # Temporarily disable zmc;
-        self.fzmc = 0
-        out, chidef, Zbest = get_leastsq(self, Zini, self.fneld, self.age, self.fit_params, class_post.residual,\
-            self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.ID)
-
-        if f_get_templates:
-            Av_tmp = out.params['Av'].value
-            AA_tmp = np.zeros(len(self.age), dtype='float')
-            ZZ_tmp = np.zeros(len(self.age), dtype='float')
-            fm_tmp, xm_tmp = self.fnc.get_template(out, f_val=True)
-
-            ########################
-            # Check redshift
-            ########################
-            if self.fzvis:
-                flag_z = self.fit_redshift(xm_tmp, fm_tmp, include_bb=include_bb)
-
-            self.fzmc = 1
-            return out,chidef,Zbest, xm_tmp, fm_tmp
-        else:
-            self.fzmc = 1
-            return out,chidef,Zbest
-    """
\ No newline at end of file
diff --git a/gsf/function_class.py b/gsf/function_class.py
index 584fdd8..d4a20f0 100644
--- a/gsf/function_class.py
+++ b/gsf/function_class.py
@@ -471,8 +471,9 @@ def get_template_single(self, A00, Av, nmodel, Z, zgal, lib, logU=None, f_apply_
     def get_template(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib_all:bool=False, f_nrd:bool=False, 
         f_apply_dust:bool=True, f_IGM=True, deltaz_lim=0.1, f_neb=False, EBVratio:float=2.27):
         '''
-        Makes model template for a nebular emission.
-        This is only for nubular template, with len(A) = 1. Thus, aa=0 during the code.
+        Purpose
+        -------
+        Makes model template for a given parameter set, ``par``.
 
         Parameters
         ----------
diff --git a/gsf/maketmp_filt.py b/gsf/maketmp_filt.py
index abafc1f..451bc6f 100644
--- a/gsf/maketmp_filt.py
+++ b/gsf/maketmp_filt.py
@@ -25,7 +25,8 @@ def get_spectrum_draine(lambda_d, DL, zbest, numin, numax, ndmodel, \
     phi : float
         Eq.34 of Draine & Li 2007. (default: 0.055)
         ~0.055 g / (ergs/s)
-    DL : in cm.
+    DL : float
+        in cm.
 
     Returns
     -------
@@ -120,13 +121,11 @@ def sim_spec(lmin, fin, sn):
 
 def check_library(MB, af, nround=3):
     '''
-    Purpose
-    -------
     Check library if it has a consistency setup as input file.
 
     Returns
     -------
-    True is no problem. 
+    flag : bool
     '''
 
     # Z needs special care in z0 script, to avoid Zfix.
@@ -270,8 +269,6 @@ def get_LSF(inputs, DIR_EXTR, ID, lm, c=3e18):
 def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, 
     tau_lim=0.001, tmp_norm=1e10, nthin=1, delwave=0, lammax=300000, f_IGM=True):
     '''
-    Purpose
-    -------
     Make SPECTRA at given z and filter set.
     Also, after v1.8, through this function library and data are register to the main class object, MB.
     
@@ -384,7 +381,6 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
     else:
         print('No FIR is implemented.\n')
 
-
     print('############################')
     print('Making templates at z=%.4f'%(zbest))
     print('############################')
@@ -394,7 +390,7 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
     #
     # Get ascii data.
     #
-    f_spec = False
+    MB.f_spec = False
     try:
         spec_files = inputs['SPEC_FILE']
         spec_files = [x.strip() for x in spec_files.split(',')]
@@ -429,7 +425,7 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
                     lm[ii]   = lm0tmp[ii1]
                     fobs[ii] = fobs0[ii1]
                     eobs[ii] = eobs0[ii1]
-                f_spec = True
+                MB.f_spec = True
             except Exception:
                 pass
     except:
@@ -535,7 +531,7 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
     #################
     # Get morphology;
     #################
-    if f_spec:
+    if MB.f_spec:
         LSF, lm = get_LSF(inputs, DIR_EXTR, MB.ID, lm)
     else:
         lm = []
@@ -624,7 +620,7 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
                                 print('realtau entry not found. SFR is set to 0. (Or rerun maketmp_z0.py)')
                             sfr[ss] = 0
 
-                        if f_spec:
+                        if MB.f_spec:
                             ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu[ss,:])
 
                         ltmpbb[ss,:], ftmpbb[ss,:] = filconv(SFILT, wavetmp, spec_mul_nu[ss,:], DIR_FILT, MB=MB, f_regist=False)
@@ -702,7 +698,7 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
                                 spec_mul_neb_nu[zz,uu,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
                                 ltmpbb_neb[zz,uu,:], ftmpbb_neb[zz,uu,:] = filconv(SFILT, wavetmp, spec_mul_neb_nu[zz,uu,:], DIR_FILT, MB=MB, f_regist=False)
 
-                                if f_spec:
+                                if MB.f_spec:
                                     ftmp_neb_nu_int[zz,uu,:] = data_int(lm, wavetmp, spec_mul_neb_nu[zz,uu,:])
 
                                 if len(lm)>0:
@@ -827,7 +823,7 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
             ltmpbb_d, ftmpbb_d = filconv(ALLFILT,lambda_d*(1.+zbest),fnu_d,DIR_FILT)
             nd_db = np.arange(0, len(ftmpbb_d), 1)
 
-            if f_spec:
+            if MB.f_spec:
                 ftmp_nu_int_d = data_int(lm, lambda_d*(1.+zbest), fnu_d)
                 ltmpbb_d = np.append(lm, ltmpbb_d)
                 ftmpbb_d = np.append(ftmp_nu_int_d, ftmpbb_d)
@@ -1073,7 +1069,7 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
     ####################################################
     # Get extracted spectra.
     ####################################################
-    f_spec = False
+    MB.f_spec = False
     try:
         spec_files = inputs['SPEC_FILE'] #.replace('$ID','%s'%(ID))
         spec_files = [x.strip() for x in spec_files.split(',')]
@@ -1108,14 +1104,14 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
                     lm[ii] = lm0tmp[ii1]
                     fobs[ii] = fobs0[ii1]
                     eobs[ii] = eobs0[ii1]
-                f_spec = True
+                MB.f_spec = True
             except Exception:
                 pass
     except:
         print('No spec file is provided.')
         pass
 
-    if f_spec:
+    if MB.f_spec:
         nthin = 1
 
     #############################
@@ -1197,7 +1193,7 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
     #############################
     Amp = 0
     f_morp = False
-    if f_spec:
+    if MB.f_spec:
         LSF, lm = get_LSF(inputs, DIR_EXTR, ID, lm)
     else:
         lm = []
@@ -1280,7 +1276,7 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
                 else:
                     spec_mul_nu_conv[ss,:] = spec_mul_nu[ss,:]
 
-                if f_spec:
+                if MB.f_spec:
                     #ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu_conv[ss,:])
                     ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu[ss,:])
                 
@@ -1345,7 +1341,7 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
                         spec_mul_neb_nu[zz,uu,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
                         ltmpbb_neb[zz,uu,:], ftmpbb_neb[zz,uu,:] = filconv(SFILT, wavetmp, spec_mul_neb_nu[zz,uu,:], DIR_FILT, MB=MB, f_regist=False)
 
-                        if f_spec:
+                        if MB.f_spec:
                             ftmp_neb_nu_int[zz,uu,:] = data_int(lm, wavetmp, spec_mul_neb_nu[zz,uu,:])
 
                         if len(lm)>0:
@@ -1430,7 +1426,7 @@ def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_
             ALLFILT = np.append(SFILT,DFILT)
             ltmpbb_d, ftmpbb_d = filconv(ALLFILT,lambda_d*(1.+zbest),fnu_d,DIR_FILT)
 
-            if f_spec:
+            if MB.f_spec:
                 ftmp_nu_int_d = data_int(lm, lambda_d*(1.+zbest), fnu_d)
                 ltmpbb_d = np.append(lm, ltmpbb_d)
                 ftmpbb_d = np.append(ftmp_nu_int_d, ftmpbb_d)
diff --git a/gsf/zfit.py b/gsf/zfit.py
index 63f89fd..3aec6aa 100644
--- a/gsf/zfit.py
+++ b/gsf/zfit.py
@@ -90,8 +90,6 @@ def residual_z(pars, xm_tmp, fm_tmp, xobs, fobs, eobs, NR, NRbb_lim=10000, inclu
 def check_redshift(fobs, eobs, xobs, fm_tmp, xm_tmp, zbest, zprior, prior, NR, zliml, zlimu, \
     nmc_cz=100, nwalk_cz=10, nthin=5, f_line_check=False, f_vary=True, NRbb_lim=10000, include_photometry=True):
     '''
-    Purpose
-    -------
     Fit observed flux with a template to get redshift probability.
 
     Parameters
@@ -162,6 +160,8 @@ def check_redshift(fobs, eobs, xobs, fm_tmp, xm_tmp, zbest, zprior, prior, NR, z
 
 def get_chi2(zz_prob, fy_cz, ey_cz, x_cz, fm_tmp, xm_tmp, file_zprob, rms_lim=1e4):
     '''
+    Parameters
+    ----------
     zz_prob : float array
         redshift array for fit.
     fy_cz, ey_cz, x_cz : 

From b54aca4d15955b232eb1728953dc294467118108 Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Sat, 10 Dec 2022 15:59:24 -0800
Subject: [PATCH 08/14] removed build

---
 build/lib/gsf/__init__.py                     |   31 -
 build/lib/gsf/basic_func.py                   |   39 -
 build/lib/gsf/fitting.py                      | 2061 ---------
 build/lib/gsf/function.py                     | 1487 ------
 build/lib/gsf/function_class.py               | 1179 -----
 build/lib/gsf/function_igm.py                 |  264 --
 build/lib/gsf/gsf.py                          |  298 --
 build/lib/gsf/maketmp_filt.py                 | 1517 ------
 build/lib/gsf/maketmp_z0.py                   |  503 --
 build/lib/gsf/maketmp_z0_tau.py               |  187 -
 build/lib/gsf/minimizer.py                    | 2428 ----------
 build/lib/gsf/plot_sed.py                     | 4122 -----------------
 build/lib/gsf/plot_sfh.py                     | 2004 --------
 build/lib/gsf/posterior_flexible.py           |  303 --
 build/lib/gsf/posterior_nested.py             |  239 -
 build/lib/gsf/writing.py                      |  420 --
 build/lib/gsf/zfit.py                         |  190 -
 ...f-1.7.6.dev20+g1e4de8a.d20221210-py3.9.egg |  Bin 316421 -> 0 bytes
 gsf.egg-info/PKG-INFO                         |   21 -
 gsf.egg-info/SOURCES.txt                      |  633 ---
 gsf.egg-info/dependency_links.txt             |    1 -
 gsf.egg-info/not-zip-safe                     |    1 -
 gsf.egg-info/top_level.txt                    |    1 -
 23 files changed, 17929 deletions(-)
 delete mode 100644 build/lib/gsf/__init__.py
 delete mode 100644 build/lib/gsf/basic_func.py
 delete mode 100644 build/lib/gsf/fitting.py
 delete mode 100644 build/lib/gsf/function.py
 delete mode 100644 build/lib/gsf/function_class.py
 delete mode 100644 build/lib/gsf/function_igm.py
 delete mode 100644 build/lib/gsf/gsf.py
 delete mode 100644 build/lib/gsf/maketmp_filt.py
 delete mode 100644 build/lib/gsf/maketmp_z0.py
 delete mode 100644 build/lib/gsf/maketmp_z0_tau.py
 delete mode 100644 build/lib/gsf/minimizer.py
 delete mode 100644 build/lib/gsf/plot_sed.py
 delete mode 100644 build/lib/gsf/plot_sfh.py
 delete mode 100644 build/lib/gsf/posterior_flexible.py
 delete mode 100644 build/lib/gsf/posterior_nested.py
 delete mode 100644 build/lib/gsf/writing.py
 delete mode 100644 build/lib/gsf/zfit.py
 delete mode 100644 dist/gsf-1.7.6.dev20+g1e4de8a.d20221210-py3.9.egg
 delete mode 100644 gsf.egg-info/PKG-INFO
 delete mode 100644 gsf.egg-info/SOURCES.txt
 delete mode 100644 gsf.egg-info/dependency_links.txt
 delete mode 100644 gsf.egg-info/not-zip-safe
 delete mode 100644 gsf.egg-info/top_level.txt

diff --git a/build/lib/gsf/__init__.py b/build/lib/gsf/__init__.py
deleted file mode 100644
index 32449c4..0000000
--- a/build/lib/gsf/__init__.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import re
-import sys
-import os
-from pkg_resources import get_distribution, DistributionNotFound
-
-__version_commit__ = ''
-_regex_git_hash = re.compile(r'.*\+g(\w+)')
-
-try:
-    __version__ = get_distribution(__name__).version
-except DistributionNotFound:
-    __version__ = 'dev'
-
-if '+' in __version__:
-    commit = _regex_git_hash.match(__version__).groups()
-    if commit:
-        __version_commit__ = commit[0]
-
-__author__ = 'Takahiro Morishita'
-__email__ = 'takahiro@ipac.caltech.edu'
-__credits__ = 'IPAC'
-
-package = 'gsf'
-
-print('Welcome to %s version %s'%(package,__version__))
-
-# Add path;
-#sys.path.append('%stmphot/'%borgpipe)
-
-import matplotlib as mat
-mat.rcParams['font.family'] = 'StixGeneral'
diff --git a/build/lib/gsf/basic_func.py b/build/lib/gsf/basic_func.py
deleted file mode 100644
index f5bff9d..0000000
--- a/build/lib/gsf/basic_func.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import numpy as np
-
-class Basic(object):
-    '''
-    '''
-    def __init__(self, MB):
-        self.ZZ = MB.Zall
-        self.NZ = len(self.ZZ)
-        self.delZ = MB.delZ
-
-    def Z2NZ(self, Z):
-        '''
-        Critical function to infer NZ from Z.
-        '''
-        NZ = np.argmin(np.abs(self.ZZ-Z))
-        return NZ
-
-
-class Basic_tau(object):
-    '''
-    '''
-    def __init__(self, MB):
-        self.ZZ = MB.Zall
-        self.NZ = len(self.ZZ)
-        self.delZ = MB.delZ
-        self.TT = MB.tau
-        self.delT = MB.deltau            
-        self.LA = MB.ageparam
-        self.delLA = MB.delage
-        
-    def Z2NZ(self, Z, tau, age):
-        '''
-        Critical function to infer NZ from Z.
-        '''
-        NZ = np.argmin(np.abs(Z-self.ZZ))
-        NT = np.argmin(np.abs(tau-self.TT))    
-        NA = np.argmin(np.abs(age-self.LA))
-
-        return NZ, NT, NA
diff --git a/build/lib/gsf/fitting.py b/build/lib/gsf/fitting.py
deleted file mode 100644
index e8478e5..0000000
--- a/build/lib/gsf/fitting.py
+++ /dev/null
@@ -1,2061 +0,0 @@
-"""
-This includes the main part of gsf.
-
-Original copyright:
-   Copyright (c) 2021 Takahiro Morishita, STScI
-"""
-import numpy as np
-import sys
-import matplotlib.pyplot as plt
-from lmfit import Model, Parameters, minimize, fit_report#, Minimizer
-from numpy import log10
-import pickle as cPickle
-import os.path
-import random
-import string
-import timeit
-from scipy import stats
-from scipy.stats import norm
-import scipy.interpolate as interpolate
-from astropy.io import fits,ascii
-import corner
-
-# import from custom codes
-from .function import check_line_man, check_line_cz_man, calc_Dn4, savecpkl, get_leastsq
-from .zfit import check_redshift,get_chi2
-from .writing import get_param
-from .function_class import Func
-from .minimizer import Minimizer
-
-############################
-py_v = (sys.version_info[0])
-if py_v > 2:
-    try:
-        raw_input = input
-    except NameError:
-        pass
-
-################
-# Line library
-################
-LN = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3L', 'O3H', 'Mgb', 'Halpha', 'S2L', 'S2H']
-LW = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4960, 5008, 5175, 6563, 6717, 6731]
-fLW = np.zeros(len(LW), dtype='int')
-
-NRbb_lim = 10000 # BB data is associated with ids greater than this number.
-
-class Mainbody():
-    '''
-    The list of (possible) `Mainbody` attributes is given below:
-
-    Attributes
-    ----------
-    nimf : int 
-        0:Salpeter, 1:Chabrier, 2:Kroupa, 3:vanDokkum08.
-    Z : float array
-        Stellar phase metallicity in logZsun.
-    age : float array
-        Age, in Gyr.
-    fneb : int
-        flag for adding nebular emission. 0: No, 1: Yes.
-    tau0 : float array
-        Width of age bin. If you want to fix it to a specific value, set it to >0.01, in Gyr.
-        Otherwise, it would be either minimum value (=0.01; if one age bin), 
-        or the width to the next age bin.
-    '''
-
-    def __init__(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:float=25.0, pixelscale:float=0.06, Lsun:float=3.839*1e33, cosmo=None, idman=None, zman=None):
-        self.update_input(inputs, idman=idman, zman=zman)
-
-
-    def update_input(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:float=25.0, pixelscale:float=0.06, Lsun:float=3.839*1e33, cosmo=None, \
-                    idman=None, zman=None, sigz:float=5.0):
-        '''
-        The purpose of this module is to register/update the parameter attributes in `Mainbody`
-        by visiting the configuration file.
-
-        Parameters
-        ----------
-        inputs : str
-            Configuration file that lists parameters.
-
-        Mpc_cm : float, optional
-            Conversion factor from Mpc-to-cm
-
-        pixelscale : float, optional
-            Conversion factor from pixel-to-arcsec
-
-        '''
-        # Then register;
-        self.inputs = inputs
-        self.c = c
-        self.Mpc_cm = Mpc_cm
-        self.pixelscale = pixelscale
-        self.Lsun = Lsun
-        self.sigz = sigz
-        self.fitc_cz_prev = None
-
-        # Magzp;
-        try:
-            self.m0set = float(inputs['MAGZP'])
-        except:
-            print('MAGZP is not found. Set to %.2f'%(m0set))
-            self.m0set = m0set
-        self.d = 10**((48.6+self.m0set)/2.5) # Conversion factor from [ergs/s/cm2/A] to Fnu.
-
-        if cosmo == None:
-            from astropy.cosmology import WMAP9 as cosmo
-            self.cosmo = cosmo
-        else:
-            self.cosmo = cosmo
-
-        if idman != None:
-            self.ID = idman
-        else:
-            self.ID = inputs['ID']
-        print('\nFitting : %s\n'%self.ID)
-
-        # Read catalog;
-        try:
-            self.CAT_BB = inputs['CAT_BB']
-            self.fd_cat = ascii.read(self.CAT_BB)
-        except:
-            self.CAT_BB = None
-            self.fd_cat = None
-
-        if zman != None:
-            self.zgal = zman
-            self.zmcmin = None
-            self.zmcmax = None
-        else:
-            try:
-                self.zgal = float(inputs['ZMC'])
-            except:
-                iix = np.where(self.fd_cat['id'] == self.ID)
-                self.zgal = float(self.fd_cat['redshift'][iix])
-
-            try:
-                self.zmcmin = float(inputs['ZMCMIN'])
-                self.zmcmax = float(inputs['ZMCMAX'])
-            except:
-                try:
-                    self.zmcmin = self.zgal - float(self.fd_cat['ez_l'][iix])
-                    self.zmcmax = self.zgal + float(self.fd_cat['ez_u'][iix])
-                except:
-                    print('ZMCMIN and ZMCMAX cannot be found. z range is set to z pm 1.0')
-                    self.zmcmin = None
-                    self.zmcmax = None
-
-        # Data directory;
-        self.DIR_TMP = inputs['DIR_TEMP']
-        if not os.path.exists(self.DIR_TMP):
-            os.mkdir(self.DIR_TMP)
-
-        # Minimization;
-        self.fneld = self.inputs['FNELD']
-
-        # Mdyn;
-        try:
-            #self.Mdyn = float(inputs['MDYN'])
-            if int(inputs['F_MDYN']) == 1:
-                self.f_Mdyn = True
-            else:
-                self.f_Mdyn = False
-        except:
-            self.f_Mdyn = False
-
-        if self.f_Mdyn:
-            iix = np.where(self.fd_cat['id'] == int(self.ID))
-            try:
-                self.logMdyn = float(self.fd_cat['logMdyn'][iix])
-                self.elogMdyn = float(self.fd_cat['elogMdyn'][iix])
-                self.f_Mdyn = True
-            except:
-                self.f_Mdyn = False
-
-        print('f_Mdyn is set to %s\n'%self.f_Mdyn)
-        
-        #self.f_Mdyn = True
-        #self.logMdyn = 11.1
-        #self.elogMdyn = 0.1
-
-        #if self.f_Mdyn:
-        #    # If Mdyn is included.
-        #    self.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '_PA' + self.PA + '.asdf')
-        try:
-            self.DIR_EXTR = inputs['DIR_EXTR']
-        except:
-            self.DIR_EXTR = False
-
-        try:
-            # Scaling for grism; 
-            self.Cz0 = float(inputs['CZ0'])
-            self.Cz1 = float(inputs['CZ1'])
-            self.Cz2 = float(inputs['CZ2'])
-        except:
-            self.Cz0 = 1
-            self.Cz1 = 1
-            self.Cz2 = 1
-
-        # BPASS Binary template
-        try:
-            self.f_bpass = int(inputs['BPASS'])
-            try:
-                self.f_bin = int(inputs['BINARY'])
-            except:
-                self.f_bin = 1
-        except:
-            self.f_bpass = 0
-
-        # Nebular emission;
-        # Becuase of force_no_neb, add logUs regardless of `ADD_NEBULAE` flag.
-        self.fneb = False
-        self.nlogU = 0
-        try:
-            if int(inputs['ADD_NEBULAE']) == 1:
-                self.fneb = True
-                try:
-                    self.logUMIN = float(inputs['logUMIN'])
-                    self.logUMAX = float(inputs['logUMAX'])
-                    self.DELlogU = float(inputs['DELlogU'])
-                except:
-                    self.logUMIN = -2.5
-                    self.logUMAX = -2.0
-                    self.DELlogU = 0.5
-                self.logUs = np.arange(self.logUMIN, self.logUMAX, self.DELlogU)
-                self.nlogU = len(self.logUs)
-
-                try:
-                    self.logUFIX = float(inputs['logUFIX'])
-                    self.nlogU = 1
-                    self.logUMIN = self.logUFIX
-                    self.logUMAX = self.logUFIX
-                    self.DELlogU = 0
-                    self.logUs = np.asarray([self.logUMAX])
-                except:
-                    self.logUFIX = None
-            else:
-                self.fneb = False                
-                self.logUMIN = -2.5
-                self.logUMAX = -2.0
-                self.DELlogU = 0.5
-                self.logUs = np.arange(self.logUMIN, self.logUMAX, self.DELlogU)
-        except:
-            print('No nebular added.')
-            self.fneb = False
-            self.logUMIN = -2.5
-            self.logUMAX = -2.0
-            self.DELlogU = 0.5
-            self.logUs = np.arange(self.logUMIN, self.logUMAX, self.DELlogU)
-            pass
-        
-        # Outpu directory;
-        try:
-            self.DIR_OUT = inputs['DIR_OUT']
-            if not os.path.exists(self.DIR_OUT):
-                os.mkdir(self.DIR_OUT)
-        except:
-            self.DIR_OUT = './'
-
-        # Filter response curve directory, if bb catalog is provided.
-        self.DIR_FILT = inputs['DIR_FILT']
-        try:
-            self.filts = inputs['FILTER']
-            self.filts = [x.strip() for x in self.filts.split(',')]
-        except:
-            self.filts = []
-            for column in self.fd_cat.columns:
-                if column[0] == 'F':
-                    self.filts.append(column[1:])
-            pass
-
-        self.band = {}
-        for ii in range(len(self.filts)):
-            fd = np.loadtxt(self.DIR_FILT + self.filts[ii] + '.fil', comments='#')
-            self.band['%s_lam'%(self.filts[ii])] = fd[:,1]
-            self.band['%s_res'%(self.filts[ii])] = fd[:,2] / np.max(fd[:,2])
-            ffil_cum = np.cumsum(fd[:,2])
-            ffil_cum /= ffil_cum.max()
-            con = (ffil_cum>0.05) & (ffil_cum<0.95)
-            self.band['%s_fwhm'%(self.filts[ii])] = np.max(fd[:,1][con]) - np.min(fd[:,1][con])       
-
-        # Filter response curve directory, for RF colors.
-        self.filts_rf = '93,141,95,220,160'.split(',')
-        self.band_rf = {}
-        for ii in range(len(self.filts_rf)):
-            fd = np.loadtxt(self.DIR_FILT + self.filts_rf[ii] + '.fil', comments='#')
-            self.band_rf['%s_lam'%(self.filts_rf[ii])] = fd[:,1]
-            self.band_rf['%s_res'%(self.filts_rf[ii])] = fd[:,2] / np.max(fd[:,2])
-
-        # Tau comparison?
-        # -> Deprecated;
-        # self.ftaucomp = 0
-
-        # Check if func model for SFH;
-        try:
-            self.SFH_FORM = int(inputs['SFH_FORM'])
-        except:
-            self.SFH_FORM = -99
-
-        # This is for non-functional form for SFH;
-        if self.SFH_FORM == -99:
-            # Age
-            try:
-                self.age = np.asarray([float(x.strip()) for x in inputs['AGE'].split(',')])
-            except:
-                try:
-                    self.delage = float(inputs['DELAGE'])
-                except:
-                    self.delage = 0.1
-                try:
-                    self.agemax = float(inputs['AGEMAX'])
-                    self.agemin = float(inputs['AGEMIN'])
-                except:
-                    self.agemax = 14.0
-                    self.agemin = 0.003
-                logage = np.arange(np.log10(self.agemin), np.log10(self.agemax), self.delage)
-                self.age = 10**logage
-
-            try:
-                self.age_fix = [float(x.strip()) for x in inputs['AGEFIX'].split(',')]
-                aamin = []
-                print('\n')
-                print('##########################')
-                print('AGEFIX is found.\nAge will be fixed to:')
-                for age_tmp in self.age_fix:
-                    ageind = np.argmin(np.abs(age_tmp-np.asarray(self.age[:])))
-                    aamin.append(ageind)
-                    print('%6s Gyr'%(self.age[ageind]))
-                print('##########################')
-                self.aamin = aamin
-            except:
-                aamin = []
-                for nn,age_tmp in enumerate(self.age):
-                    aamin.append(nn)
-                self.aamin = aamin
-
-            self.npeak = len(self.age)
-            self.nage = np.arange(0,len(self.age),1)
-            
-        else: # This is for functional form for SFH;
-            self.agemax = float(inputs['AGEMAX'])
-            self.agemin = float(inputs['AGEMIN'])
-            self.delage = float(inputs['DELAGE'])
-
-            if self.agemax-self.agemin<self.delage:
-                self.delage = 0.0001
-                self.agemax = self.agemin + self.delage
-
-            self.ageparam = np.arange(self.agemin, self.agemax, self.delage)
-            self.nage = len(self.ageparam)
-
-            self.taumax = float(inputs['TAUMAX'])
-            self.taumin = float(inputs['TAUMIN'])
-            self.deltau = float(inputs['DELTAU'])
-
-            if self.taumax-self.taumin<self.deltau:
-                self.deltau = 0.0001
-                self.taumax = self.taumin + self.deltau
-
-            self.tau = np.arange(self.taumin, self.taumax, self.deltau)
-            self.ntau = len(self.tau)
-
-            self.npeak = int(inputs['NPEAK'])
-            self.age = np.arange(0,self.npeak,1) # This is meaningless.
-            aamin = []
-            for nn,age_tmp in enumerate(self.age):
-                aamin.append(nn)
-            self.aamin = aamin
-
-        # SNlimit;
-        try:
-            self.SNlim = float(inputs['SNLIM']) # SN for non-detection in BB catalog.
-        except:
-            self.SNlim = 1.0
-
-        # Redshift as a param;
-        try:
-            self.fzmc = int(inputs['F_ZMC'])
-        except:
-            self.fzmc = 0
-            print('Cannot find ZMC. Set to %d.'%(self.fzmc))
-
-        # Metallicity
-        self.has_ZFIX = False
-        try:
-            self.ZFIX = float(inputs['ZFIX'])
-            try:
-                self.delZ = float(inputs['DELZ'])
-                self.Zmax, self.Zmin = float(inputs['ZMAX']), float(inputs['ZMIN'])
-            except:
-                self.delZ = 0.0001
-                self.Zmin, self.Zmax = self.ZFIX, self.ZFIX + self.delZ
-            self.Zall = np.arange(self.Zmin, self.Zmax, self.delZ)
-            print('\n##########################')
-            print('ZFIX is found.\nZ will be fixed to: %.2f'%(self.ZFIX))
-            self.has_ZFIX = True
-        except:
-            self.Zmax, self.Zmin = float(inputs['ZMAX']), float(inputs['ZMIN'])
-            self.delZ = float(inputs['DELZ'])
-            if self.Zmax == self.Zmin or self.delZ == 0:
-                self.delZ = 0.0
-                self.ZFIX = self.Zmin
-                self.Zall = np.asarray([self.ZFIX])
-                self.has_ZFIX = True
-            elif np.abs(self.Zmax - self.Zmin) <= self.delZ:
-                self.ZFIX = self.Zmin
-                self.Zall = np.asarray([self.ZFIX])
-                self.has_ZFIX = True
-            else:
-                self.Zall = np.arange(self.Zmin, self.Zmax, self.delZ)
-
-        # If BPASS;
-        if self.f_bpass == 1:
-            try:
-                self.BPASS_DIR = inputs['BPASS_DIR']
-            except:
-                print('BPASS_DIR is not found. Using default.')
-                self.BPASS_DIR = '/astro/udfcen3/Takahiro/BPASS/'
-
-            self.BPASS_ver = 'v2.2.1'
-            self.Zsun = 0.020
-            Zbpass = [1e-5, 1e-4, 0.001, 0.002, 0.003, 0.004, 0.006, 0.008, 0.010, 0.020, 0.030, 0.040]
-            Zbpass = np.log10(np.asarray(Zbpass)/self.Zsun)
-
-            try: # If ZFIX is found;
-                iiz = np.argmin(np.abs(Zbpass[:] - float(inputs['ZFIX']) ) )
-                if Zbpass[iiz] - float(inputs['ZFIX']) != 0:
-                    print('%.2f is not found in BPASS Z list. %.2f is used instead.'%(float(inputs['ZFIX']),Zbpass[iiz]))
-                self.ZFIX = Zbpass[iiz]
-                self.delZ = 0.0001
-                self.Zmin, self.Zmax = self.ZFIX, self.ZFIX + self.delZ
-                self.Zall = np.arange(self.Zmin, self.Zmax, self.delZ) # in logZsun
-                print('\n##########################')
-                print('ZFIX is found.\nZ will be fixed to: %.2f'%(self.ZFIX))
-                self.has_ZFIX = True
-            except:
-                print('ZFIX is not found.')
-                print('Metallicities available in BPASS are limited and discrete. ZFIX is recommended.',self.Zall)
-                self.Zmax, self.Zmin = float(inputs['ZMAX']), float(inputs['ZMIN'])
-                con_z = np.where((Zbpass >= self.Zmin) & (Zbpass <= self.Zmax))
-                self.Zall = Zbpass[con_z]
-                self.delZ = 0.0001
-                self.Zmax,self.Zmin = np.max(self.Zall), np.min(self.Zall)
-                print('Final list for log(Z_BPASS/Zsun) is:',self.Zall)
-                if len(self.Zall)>1:
-                    self.has_ZFIX = False
-                    self.ZFIX = None
-
-        # N of param:
-        self.has_AVFIX = False
-        try:
-            Avfix = float(inputs['AVFIX'])
-            self.AVFIX = Avfix
-            self.nAV = 0
-            print('\n##########################')
-            print('AVFIX is found.\nAv will be fixed to:\n %.2f'%(Avfix))
-            self.has_AVFIX = True
-        except:
-            try:
-                self.Avmin = float(inputs['AVMIN'])
-                self.Avmax = float(inputs['AVMAX'])
-                if Avmin == Avmax:
-                    self.nAV = 0
-                    self.AVFIX = Avmin
-                    self.has_AVFIX = True
-                else:
-                    self.nAV = 1
-            except:
-                self.nAV = 1
-                self.Avmin = 0
-                self.Avmax = 4.0
-
-        # Z evolution;
-        print('\n#############################')
-        if self.SFH_FORM == -99:
-            if int(inputs['ZEVOL']) == 1:
-                self.ZEVOL = 1
-                self.ndim = int(self.npeak * 2 + self.nAV) # age, Z, and Av.
-                print('Metallicity evolution is on')
-            else:
-                self.ZEVOL = 0
-                print('Metallicity evolution is off')
-                try:
-                    ZFIX = float(inputs['ZFIX'])
-                    self.nZ = 0
-                except:
-                    if np.max(self.Zall) == np.min(self.Zall):
-                        self.nZ = 0
-                    else:
-                        self.nZ = 1
-                self.ndim = int(self.npeak + self.nZ + self.nAV) # age, Z, and Av.
-        else:
-            if int(inputs['ZEVOL']) == 1:
-                self.ZEVOL = 1
-                self.nZ = self.npeak
-                print('Metallicity evolution is on')
-            else:
-                self.ZEVOL = 0
-                print('Metallicity evolution is off')
-                try:
-                    ZFIX = float(inputs['ZFIX'])
-                    self.nZ = 0
-                except:
-                    if np.max(self.Zall) == np.min(self.Zall):
-                        self.nZ = 0
-                    else:
-                        self.nZ = 1
-                    
-            self.ndim = int(self.npeak*3 + self.nZ + self.nAV) # age, Z, and Av.
-        print('#############################\n')
-
-        # Redshift
-        self.ndim += self.fzmc
-        print('\n##########################')
-        print('No. of params are : %d'%(self.ndim))
-
-        # Line
-        try:
-            self.LW0 = [float(x.strip()) for x in inputs['LINE'].split(',')]
-        except:
-            self.LW0 = []
-
-        # Dust model specification;
-        try:
-            self.dust_model = int(inputs['DUST_MODEL'])
-            if self.dust_model == 0:
-                self.dust_model_name = 'Calz'
-            elif self.dust_model == 1:
-                self.dust_model_name = 'MW'
-            elif self.dust_model == 2:
-                self.dust_model_name = 'LMC'
-            elif self.dust_model == 3:
-                self.dust_model_name = 'SMC'
-            elif self.dust_model == 4:
-                self.dust_model_name = 'KriekConroy'
-            else:
-                print('Unknown number for dust attenuation. Calzetti.')
-                self.dust_model = 0
-                self.dust_model_name = 'Calz'
-        except:
-            self.dust_model = 0
-            self.dust_model_name = 'Calz'
-        print('Dust attenuation is set to %s\n'%self.dust_model_name)
-
-        # If FIR data;
-        try:
-            DT0 = float(inputs['TDUST_LOW'])
-            DT1 = float(inputs['TDUST_HIG'])
-            dDT = float(inputs['TDUST_DEL'])
-            try:
-                self.TDUSTFIX = float(inputs['TDUSTFIX'])
-                if self.TDUSTFIX < DT0 or self.TDUSTFIX > DT1:
-                    print('TDUSTFIX is set out of the range. Exiting.')
-                    sys.exit()
-            except:
-                self.TDUSTFIX = None
-
-            if DT0 == DT1:
-                self.Temp = [DT0]
-            else:
-                self.Temp = np.arange(DT0,DT1,dDT)
-
-            if not self.TDUSTFIX == None:
-                self.NTDUST = np.argmin(np.abs(self.Temp-self.TDUSTFIX))
-            else:
-                self.NTDUST = None
-
-            self.f_dust = True
-
-            self.DT0 = DT0
-            self.DT1 = DT1
-            self.dDT = dDT
-            try:
-                self.dust_numax = int(inputs['DUST_NUMAX'])
-                self.dust_nmodel = int(inputs['DUST_NMODEL'])
-            except:
-                self.dust_numax = 3
-                self.dust_nmodel = 9
-            print('FIR fit is on.')
-        except:
-            self.Temp = []
-            self.f_dust = False
-            pass
-
-        try:
-            self.DIR_DUST = inputs['DIR_DUST']
-        except:
-            self.DIR_DUST = './'
-
-        try:
-            # Length of each ssp templates.
-            self.tau0 = np.asarray([float(x.strip()) for x in inputs['TAU0'].split(',')])
-            self.ntau0 = len(self.tau0)
-        except:
-            self.tau0 = np.asarray([-1.0])
-            self.ntau0 = 1
-
-        # IMF
-        try:
-            self.nimf = int(inputs['NIMF'])
-        except:
-            self.nimf = 0
-            print('Cannot find NIMF. Set to %d.'%(self.nimf))
-
-        # Error parameter
-        try:
-            self.ferr = int(self.inputs['F_ERR'])
-        except:
-            self.ferr = 0
-            pass
-
-        # Nested or ensemble slice sampler?
-        self.f_mcmc = False
-        self.f_nested = False
-        self.f_zeus = False
-        try:
-            nnested = inputs['MC_SAMP']
-            if nnested == 'NEST' or nnested == '1':
-                self.f_nested = True
-            elif nnested == 'ZEUS' or  nnested == 'SLICE' or nnested == '2':
-                self.f_zeus = True
-            else:
-                self.f_mcmc = True
-        except:
-            print('Missing MC_SAMP keyword. Setting f_mcmc True.')
-            self.f_mcmc = True
-            pass
-
-        # Force Age to Age fix?:
-        try:
-            if int(inputs['FORCE_AGE'])==1:
-                self.force_agefix = True
-            else:
-                self.force_agefix = False
-        except:
-            self.force_agefix = False
-
-        # SFH prior;
-        # try:
-        #     self.norder_sfh_prior = int(inputs['norder_sfh_prior'])
-        #     self.f_prior_sfh = True
-        # except:
-        #     self.norder_sfh_prior = None
-        #     self.f_prior_sfh = False
-
-        print('\n')
-
-
-    def get_lines(self, LW0):
-        '''
-        '''
-        fLW = np.zeros(len(LW0), dtype='int')
-        LW = LW0
-        return LW, fLW
-
-
-    def read_data(self, Cz0:float, Cz1:float, Cz2:float, zgal:float, add_fir:bool=False, idman=None):
-        '''
-        Read in observed data. Not model fluxes.
-
-        Parameters
-        ----------
-        Cz0, Cz1 : float
-            Normalization coefficients for grism spectra. Cz0 for G102, Cz1 for G141.
-        zgal : float
-            Current redshift estimate.
-
-        Returns
-        -------
-        Dictionary.
-
-        Notes
-        -----
-        This modeule can be used for any SFHs.
-        '''
-        print('Reading data with Cz0=%.2f, Cz1=%.2f, Cz2=%.2f, zgal=%.2f'%(Cz0, Cz1, Cz2, zgal))
-
-        ##############
-        # Spectrum
-        ##############
-        NR, x, fy00, ey00 = self.data['spec_obs']['NR'], self.data['spec_obs']['x'], self.data['spec_obs']['fy'], self.data['spec_obs']['ey']
-        con0 = (NR<1000)
-        xx0 = x[con0]
-        fy0 = fy00[con0] * Cz0
-        ey0 = ey00[con0] * Cz0
-        con1 = (NR>=1000) & (NR<2000)
-        xx1 = x[con1]
-        fy1 = fy00[con1] * Cz1
-        ey1 = ey00[con1] * Cz1
-        con2 = (NR>=2000) & (NR<NRbb_lim)
-        xx2 = x[con2]
-        fy2 = fy00[con2] * Cz2
-        ey2 = ey00[con2] * Cz2
-
-        ##############
-        # Broadband
-        ##############
-        try:
-            NRbb, xbb, fybb, eybb, exbb = self.data['bb_obs']['NR'], self.data['bb_obs']['x'], self.data['bb_obs']['fy'], self.data['bb_obs']['ey'], self.data['bb_obs']['ex']
-        except: # if no BB;
-            print('No BB data.')
-            NRbb = np.asarray([])
-            xbb  = np.asarray([])
-            fybb = np.asarray([])
-            eybb = np.asarray([])
-            exbb = np.asarray([])
-
-        con_bb = ()
-        xx_bb = xbb[con_bb]
-        ex_bb = exbb[con_bb]
-        fy_bb = fybb[con_bb]
-        ey_bb = eybb[con_bb]
-
-        xx01 = np.append(xx0,xx1)
-        fy01 = np.append(fy0,fy1)
-        ey01 = np.append(ey0,ey1)
-
-        xx02 = np.append(xx01,xx2)
-        fy02 = np.append(fy01,fy2)
-        ey02 = np.append(ey01,ey2)
-
-        xx = np.append(xx02,xx_bb)
-        fy = np.append(fy02,fy_bb)
-        ey = np.append(ey02,ey_bb)
-
-        wht = 1./np.square(ey)
-        con_wht = (ey<0)
-        wht[con_wht] = 0
-
-        # For now...
-        #wht2 = check_line_man(fy, x, wht, fy, zgal, self.LW0)
-        wht2 = wht[:]
-
-        # Append data;
-        if add_fir:
-            nr_d, x_d, fy_d, ey_d = self.data['spec_dust_obs']['NR'], self.data['spec_dust_obs']['x'], self.data['spec_dust_obs']['fy'], self.data['spec_dust_obs']['ey']
-            NR = np.append(NR,nr_d)
-            fy = np.append(fy,fy_d)
-            ey = np.append(ey,ey_d)
-            x = np.append(x,x_d)
-            wht = np.append(wht,1./np.square(ey_d))
-            # For now...
-            #wht2= check_line_man(fy, x, wht, fy, zgal, self.LW0)
-            wht2 = wht[:]
-
-        # Sort data along wave;
-        f_sort = False
-
-        if f_sort:
-            nrd_yyd = np.zeros((len(NR),6), dtype='float')
-            nrd_yyd[:,0] = NR
-            nrd_yyd[:,1] = x
-            nrd_yyd[:,2] = fy
-            nrd_yyd[:,3] = ey
-            nrd_yyd[:,4] = wht
-            nrd_yyd[:,5] = wht2
-
-            b = nrd_yyd
-            nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-            NR = nrd_yyd_sort[:,0]
-            x = nrd_yyd_sort[:,1]
-            fy = nrd_yyd_sort[:,2]
-            ey = nrd_yyd_sort[:,3]
-            wht = nrd_yyd_sort[:,4]
-            wht2= nrd_yyd_sort[:,5]
-
-        sn = fy/ey
-        self.n_optir = len(sn)
-
-        dict = {}
-        dict = {'NR':NR, 'x':x, 'fy':fy, 'ey':ey, 'NRbb':NRbb, 'xbb':xx_bb, 'exbb':ex_bb, 'fybb':fy_bb, 'eybb':ey_bb, 'wht':wht, 'wht2': wht2, 'sn':sn}
-
-        return dict
-
-
-    def search_redshift(self, dict, xm_tmp, fm_tmp, zliml:float=0.01, zlimu:float=6.0, delzz:float=0.01, lines:bool=False, prior=None, method:str='powell'):
-        '''
-        This module explores the redshift space to find the best redshift and probability distribution.
-
-        Parameters
-        ----------
-        dict : dictionary
-            Dictionary that includes input data.
-
-        xm_tmp : numpy.array
-            Wavelength array, common for fm_tmp below, at z=0. Should be in [len(wavelength)].
-        fm_tmp : numpy.array
-            Fluxes for various templates. Should be in a shape of [ n * len(wavelength)], 
-            where n is the number templates.
-        zliml : float
-            Lowest redshift for fitting range.
-        zlimu : float
-            Highest redshift for fitting range.
-        prior : numpy.array
-            Prior used for the redshift determination. E.g., Eazy z-probability.
-        method : str
-            Method for minimization. The option must be taken from lmfit. Powell is more accurate. Nelder is faster.
-
-        Returns
-        -------
-        zspace : numpy.array 
-            Array for redshift grid.
-        chi2s : numpy.array
-            Array of chi2 values corresponding to zspace.
-        '''
-
-        zspace = np.arange(zliml,zlimu,delzz)
-        chi2s  = np.zeros((len(zspace),2), 'float')
-        if prior == None:
-            prior = zspace[:] * 0 + 1.0
-
-        # Observed data points;
-        NR = dict['NR']
-        con0 = (NR<1000)
-        fy0 = dict['fy'][con0] #* Cz0s
-        ey0 = dict['ey'][con0] #* Cz0s
-        x0  = dict['x'][con0]
-        con1 = (NR>=1000) & (NR<10000)
-        fy1 = dict['fy'][con1] #* Cz1s
-        ey1 = dict['ey'][con1] #* Cz1s
-        x1  = dict['x'][con1]
-        con2 = (NR>=10000) # BB
-        fy2 = dict['fy'][con2]
-        ey2 = dict['ey'][con2]
-        x2 = dict['x'][con2]
-
-        fy01 = np.append(fy0,fy1)
-        fcon = np.append(fy01,fy2)
-        ey01 = np.append(ey0,ey1)
-        eycon = np.append(ey01,ey2)
-        x01 = np.append(x0,x1)
-        xobs = np.append(x01,x2)
-
-        wht = 1./np.square(eycon)
-        wht2 = wht
-
-        # Set parameters;
-        fit_par_cz = Parameters()
-        for nn in range(len(fm_tmp[:,0])):
-            fit_par_cz.add('C%d'%nn, value=1., min=0., max=1e5)
-
-        def residual_z(pars,z):
-            '''
-            '''
-            vals = pars.valuesdict()
-            xm_s = xm_tmp * (1+z)
-            fm_s = np.zeros(len(xm_tmp),'float')
-
-            for nn in range(len(fm_tmp[:,0])):
-                fm_s += fm_tmp[nn,:] * pars['C%d'%nn]
-
-            fint = interpolate.interp1d(xm_s, fm_s, kind='nearest', fill_value="extrapolate")
-            #fm_int = np.interp(xobs, xm_s, fm_s)
-            fm_int = fint(xobs)
-
-            if fcon is None:
-                print('Data is none')
-                return fm_int
-            else:
-                return (fm_int - fcon) * np.sqrt(wht2) # i.e. residual/sigma
-
-        # Start redshift search;
-        for zz in range(len(zspace)):
-            # Best fit
-            out_cz = minimize(residual_z, fit_par_cz, args=([zspace[zz]]), method=method)
-            keys = fit_report(out_cz).split('\n')
-
-            csq = out_cz.chisqr
-            rcsq = out_cz.redchi
-            fitc_cz = [csq, rcsq]
-
-            #return fitc_cz
-            chi2s[zz,0] = csq
-            chi2s[zz,1] = rcsq
-
-        self.zspace = zspace
-        self.chi2s = chi2s
-
-        return zspace, chi2s
-
-
-    def fit_redshift(self, xm_tmp, fm_tmp, delzz=0.01, ezmin=0.01, zliml=0.01, 
-        zlimu=None, snlim=0, priors=None, f_bb_zfit=True, f_line_check=False, 
-        f_norm=True, f_lambda=False, zmax=20, include_bb=False, f_exclude_negative=False):
-        '''
-        Find the best-fit redshift, before going into a big fit, through an interactive inspection.
-        This module is effective only when spec data is provided.
-        When spectrun is provided, this does redshift fit, but **by using the SED model guessed from the BB photometry**.
-        Thus, providing good BB photometry is critical in this step.
-
-        Parameters
-        ----------
-        xm_tmp, fm_tmp : float array
-            SED model.
-        delzz : float
-            Delta z in redshift search space
-        zliml : float
-            Lower limit range for redshift
-        zlimu : float
-            Upper limit range for redshift
-        ezmin : float
-            Minimum redshift uncertainty.
-        snlim : float
-            SN limit for data points. Those below the number will be cut from the fit.
-        include_bb : bool
-            Turn this True if Redshift fitting is requred for only BB data.
-        f_line_check : bool
-            If True, line masking.
-        priors : dict, optional
-            Dictionary that contains z (redshift grid) and chi2 (chi-square).
-        f_exclude_negative : bool
-            Exclude negative fluxes in spectrum.
-
-        Notes
-        -----
-        Spectral data must be provided to make this work.
-
-        '''
-        self.file_zprob = self.DIR_OUT + 'zprob_' + self.ID + '.txt'
-
-        # NMC for zfit
-        self.nmc_cz = int(self.inputs['NMCZ'])
-
-        # For z prior.
-        if self.zmcmin != None:
-            zliml = self.zmcmin
-        else:
-            zliml = 0
-        if self.zmcmax != None:
-            zlimu = self.zmcmax
-        else:
-            zlimu = 20
-
-        # Observed data;
-        sn = self.dict['fy'] / self.dict['ey']
-
-        # Only spec data?
-        if include_bb:
-            con_cz = ()#(sn>snlim)
-        else:
-            con_cz = (self.dict['NR']<NRbb_lim) #& (sn>snlim)
-
-        if f_exclude_negative:
-            con_cz &= (sn>snlim)
-            
-        fy_cz = self.dict['fy'][con_cz] # Already scaled by self.Cz0
-        ey_cz = self.dict['ey'][con_cz]
-        x_cz = self.dict['x'][con_cz] # Observed range
-        NR_cz = self.dict['NR'][con_cz]
-
-        # kind='cubic' causes an error if len(xm_tmp)<=3;
-        fint = interpolate.interp1d(xm_tmp, fm_tmp, kind='nearest', fill_value="extrapolate")
-        fm_s = fint(x_cz)
-
-        #
-        # If Eazy result exists;
-        #
-        try:
-            eaz_pz = self.inputs['EAZY_PZ']
-            f_eazy = True
-        except:
-            f_eazy = False
-
-        if f_eazy:
-            dprob = np.loadtxt(eaz_pz, comments='#')
-            zprob = dprob[:,0]
-            cprob = dprob[:,1]
-            # Then interpolate to a common z grid;
-            zz_prob = np.arange(0,zmax,delzz)
-            cprob_s = np.interp(zz_prob, zprob, cprob)
-            prior_s = np.exp(-0.5 * cprob_s)
-            prior_s /= np.sum(prior_s)
-        else:
-            zz_prob = np.arange(zliml,zlimu,delzz)
-            if priors != None:
-                zprob = priors['z']
-                cprob = priors['chi2']
-
-                cprob_s = np.interp(zz_prob, zprob, cprob)
-                prior_s = np.exp(-0.5 * cprob_s) / np.sum(cprob_s)
-                con_pri = (zz_prob<np.min(zprob)) | (zz_prob>np.max(zprob))
-                prior_s[con_pri] = 0
-                if f_norm:
-                    prior_s /= np.sum(prior_s)
-            else:
-                prior_s = zz_prob * 0 + 1.
-                prior_s /= np.sum(prior_s)
-
-        # Attach prior:
-        self.z_prior = zz_prob
-        self.p_prior = prior_s
-
-        # Plot;
-        if len(self.dict['fy'][con_cz])==0:
-            return 'y'
-
-        if self.fzvis==1 and (include_bb | len(fy_cz)>0):
-            import matplotlib as mpl
-            mpl.use('TkAgg')
-            data_model = np.zeros((len(x_cz),4),'float')
-            data_model[:,0] = x_cz
-            data_model[:,1] = fm_s
-            data_model[:,2] = fy_cz
-            data_model[:,3] = ey_cz
-
-            data_model_sort = data_model[data_model[:, 0].argsort()]            
-            #data_model_sort = np.sort(data_model, axis=0) # This does not work!!
-
-            plt.plot(data_model_sort[:,0], data_model_sort[:,1], 'gray', linestyle='--', linewidth=0.5, label='') # Model based on input z.
-            plt.plot(data_model_sort[:,0], data_model_sort[:,2],'.b', linestyle='-', linewidth=0.5, label='Obs.') # Observation
-            plt.errorbar(data_model_sort[:,0], data_model_sort[:,2], yerr=data_model_sort[:,3], color='b', capsize=0, linewidth=0.5) # Observation
-
-            # Write prob distribution;
-            if True:
-                get_chi2(zz_prob, fy_cz, ey_cz, x_cz, fm_tmp, xm_tmp/(1+self.zgal), self.file_zprob)
-
-            print('############################')
-            print('Start MCMC for redshift fit')
-            print('############################')
-            res_cz, fitc_cz = check_redshift(
-                fy_cz, ey_cz, x_cz, fm_tmp, xm_tmp/(1+self.zgal), 
-                self.zgal, self.z_prior, self.p_prior,
-                NR_cz, zliml, zlimu, self.nmc_cz, self.nwalk_cz, 
-                include_photometry=include_bb
-                )
-
-            z_cz = np.percentile(res_cz.flatchain['z'], [16,50,84])
-            scl_cz0 = np.percentile(res_cz.flatchain['Cz0'], [16,50,84])
-            scl_cz1 = np.percentile(res_cz.flatchain['Cz1'], [16,50,84])
-            scl_cz2 = np.percentile(res_cz.flatchain['Cz2'], [16,50,84])
-
-            zrecom = z_cz[1]
-            Czrec0 = scl_cz0[1]
-            Czrec1 = scl_cz1[1]
-            Czrec2 = scl_cz2[1]
-
-            # Switch to peak redshift:
-            # find minimum and maximum of xticks, so we know
-            # where we should compute theoretical distribution
-            ser = res_cz.flatchain['z']
-            xmin, xmax = self.zgal-0.2, self.zgal+0.2
-            lnspc = np.linspace(xmin, xmax, len(ser))
-            print('\n\n')
-            print('Recommended redshift, Cz0, Cz1, and Cz2, %.5f %.5f %.5f %.5f, with chi2/nu=%.3f'%(zrecom, Czrec0, Czrec1, Czrec2, fitc_cz[1]))
-            print('\n\n')
-            fit_label = 'Proposed model'
-            #self.fitc_cz = fitc_cz[1]
-
-        else:
-            print('fzvis is set to False. z fit not happening.')
-            try:
-                zmcmin = float(self.inputs['ZMCMIN'])
-                zmcmax = float(self.inputs['ZMCMAX'])
-                print('Redshift error is taken from input file.')
-            except:
-                zmcmin = self.zprev-ezmin
-                zmcmax = self.zprev+ezmin
-                print('Redshift error is assumed to %.1f.'%(ezmin))
-
-            z_cz = [zmcmin, self.zprev, zmcmax]
-            zrecom = z_cz[1]
-            scl_cz0 = [1.,1.,1.]
-            scl_cz1 = [1.,1.,1.]
-            scl_cz2 = [1.,1.,1.]
-            Czrec0 = scl_cz0[1]
-            Czrec1 = scl_cz1[1]
-            Czrec2 = scl_cz2[1]
-            res_cz = None
-            fitc_cz = [99,99]
-
-            # If this label is being used, it means that the fit is failed.
-            fit_label = 'Current model'
-
-        # New template at zrecom;
-        xm_s = xm_tmp / (1+self.zgal) * (1+zrecom)
-        fint = interpolate.interp1d(xm_s, fm_tmp, kind='nearest', fill_value="extrapolate")
-        fm_s = fint(x_cz)
-        whtl = 1/np.square(ey_cz)
-
-        if f_line_check:
-            try:
-                wht3, ypoly = check_line_cz_man(fy_cz, x_cz, whtl, fm_s, zrecom, LW=self.LW0)
-            except:
-                wht3, ypoly = whtl, fy_cz
-        else:
-            wht3 = whtl
-        con_line = (wht3==0)
-
-        # Visual inspection;
-        if self.fzvis==1:
-            #
-            # Ask interactively;
-            #
-            data_model_new = np.zeros((len(x_cz),4),'float')
-            data_model_new[:,0] = x_cz
-            data_model_new[:,1] = fm_s
-            # data_model_new_sort = np.sort(data_model_new, axis=0)
-            data_model_new_sort = data_model_new[data_model_new[:, 0].argsort()]
-
-            plt.plot(data_model_new_sort[:,0], data_model_new_sort[:,1], 'r', linestyle='-', linewidth=0.5, label='%s ($z=%.5f$)'%(fit_label,zrecom)) # Model based on recomended z.
-            plt.plot(x_cz[con_line], fm_s[con_line], color='orange', marker='o', linestyle='', linewidth=3.)
-
-            # Plot lines for reference
-            for ll in range(len(LW)):
-                try:
-                    conpoly = (x_cz/(1.+zrecom)>3000) & (x_cz/(1.+zrecom)<8000)
-                    yline = np.max(ypoly[conpoly])
-                    yy = np.arange(yline/1.02, yline*1.1)
-                    xxpre = yy * 0 + LW[ll] * (1.+self.zgal)
-                    xx = yy * 0 + LW[ll] * (1.+zrecom)
-                    plt.plot(xxpre, yy/1.02, linewidth=0.5, linestyle='--', color='gray')
-                    plt.text(LW[ll] * (1.+self.zgal), yline/1.05, '%s'%(LN[ll]), fontsize=8, color='gray')
-                    plt.plot(xx, yy, linewidth=0.5, linestyle='-', color='orangered')
-                    plt.text(LW[ll] * (1.+zrecom), yline, '%s'%(LN[ll]), fontsize=8, color='orangered')
-                except:
-                    pass
-
-            # Plot data
-            data_obsbb = np.zeros((len(self.dict['xbb']),3), 'float')
-            data_obsbb[:,0],data_obsbb[:,1] = self.dict['xbb'],self.dict['fybb']
-            if len(fm_tmp) == len(self.dict['xbb']): # BB only;
-                data_obsbb[:,2] = fm_tmp
-            #data_obsbb_sort = np.sort(data_obsbb, axis=0)
-            data_obsbb_sort = data_obsbb[data_obsbb[:, 0].argsort()]            
-            
-            if len(fm_tmp) == len(self.dict['xbb']): # BB only;
-                plt.scatter(data_obsbb_sort[:,0], data_obsbb_sort[:,2], color='none', marker='d', s=50, edgecolor='gray', zorder=4, label='Current model ($z=%.5f$)'%(self.zgal))
-            else:
-                model_spec = np.zeros((len(fm_tmp),2), 'float')
-                model_spec[:,0],model_spec[:,1] = xm_tmp,fm_tmp
-                #model_spec_sort = np.sort(model_spec, axis=0)
-                model_spec_sort = model_spec[model_spec[:, 0].argsort()]
-                plt.plot(model_spec_sort[:,0], model_spec_sort[:,1], marker='.', color='gray', ms=1, linestyle='-', linewidth=0.5, zorder=4, label='Current model ($z=%.5f$)'%(self.zgal))
-
-            try:
-                xmin, xmax = np.min(x_cz)/1.1,np.max(x_cz)*1.1
-            except:
-                xmin, xmax = 2000,10000
-
-            try:
-                plt.ylim(0,yline*1.1)
-            except:
-                pass
-
-            plt.xlim(xmin,xmax)
-            plt.xlabel('Wavelength ($\mathrm{\AA}$)')
-            plt.ylabel('$F_\\nu$ (arb.)')
-            plt.legend(loc=0)
-
-            zzsigma = ((z_cz[2] - z_cz[0])/2.)/self.zgal
-            zsigma = np.abs(self.zgal-zrecom) / (self.zgal)
-            C0sigma = np.abs(Czrec0-self.Cz0)/self.Cz0
-            eC0sigma = ((scl_cz0[2]-scl_cz0[0])/2.)/self.Cz0
-            C1sigma = np.abs(Czrec1-self.Cz1)/self.Cz1
-            eC1sigma = ((scl_cz1[2]-scl_cz1[0])/2.)/self.Cz1
-            C2sigma = np.abs(Czrec2-self.Cz2)/self.Cz2
-            eC2sigma = ((scl_cz2[2]-scl_cz2[0])/2.)/self.Cz2
-
-            print('\n##############################################################')
-            print('Input redshift is %.3f per cent agreement.'%((1.-zsigma)*100))
-            print('Error is %.3f per cent.'%(zzsigma*100))
-            print('Input Cz0 is %.3f per cent agreement.'%((1.-C0sigma)*100))
-            print('Error is %.3f per cent.'%(eC0sigma*100))
-            print('Input Cz1 is %.3f per cent agreement.'%((1.-C1sigma)*100))
-            print('Error is %.3f per cent.'%(eC1sigma*100))
-            print('Input Cz2 is %.3f per cent agreement.'%((1.-C2sigma)*100))
-            print('Error is %.3f per cent.'%(eC2sigma*100))
-            print('##############################################################\n')
-            plt.show()
-
-            flag_z = raw_input('Do you want to continue with the input redshift, Cz0, Cz1, Cz2, and chi2/nu, %.5f %.5f %.5f %.5f %.5f? ([y]/n/m) '%\
-                (self.zgal, self.Cz0, self.Cz1, self.Cz2, self.fitc_cz_prev))
-        else:
-            flag_z = 'y'
-
-        try:
-            self.z_cz_prev = self.z_cz
-        except:
-            self.z_cz_prev = [self.zgal,self.zgal,self.zgal]
-
-        # Write it to self;
-        self.zrecom = zrecom
-        self.Czrec0 = Czrec0 * self.Cz0
-        self.Czrec1 = Czrec1 * self.Cz1
-        self.Czrec2 = Czrec2 * self.Cz2
-        self.z_cz = z_cz
-        self.scl_cz0 = scl_cz0
-        self.scl_cz1 = scl_cz1
-        self.scl_cz2 = scl_cz2
-        self.res_cz = res_cz
-        self.fitc_cz_prev = fitc_cz[1]
-
-        return flag_z
-
-
-    def get_zdist(self, f_interact=False, f_ascii=True):
-        '''
-        Saves a plot of z-distribution.
-
-        Parameters
-        ----------
-        f_interact : bool
-            If true, this module returns figure ax.
-        '''
-
-        try:
-            fig = plt.figure(figsize=(6.5,2.5))
-            fig.subplots_adjust(top=0.96, bottom=0.16, left=0.09, right=0.99, hspace=0.15, wspace=0.25)
-            ax1 = fig.add_subplot(111)
-            n, nbins, patches = ax1.hist(self.res_cz.flatchain['z'], bins=200, density=True, color='gray', label='')
-
-            yy = np.arange(0,np.max(n),1)
-            xx = yy * 0 + self.z_cz[1]
-            ax1.plot(xx,yy,linestyle='-',linewidth=1,color='orangered',\
-                label='$z=%.5f_{-%.5f}^{+%.5f}$\n$C_{z0}=%.3f$\n$C_{z1}=%.3f$\n$C_{z2}=%.3f$'%\
-                (self.z_cz[1],self.z_cz[1]-self.z_cz[0],self.z_cz[2]-self.z_cz[1], self.Cz0, self.Cz1, self.Cz2))
-
-            if f_ascii:
-                file_ascii_out = self.DIR_OUT + 'zmc_' + self.ID + '.txt'
-                fw_ascii = open(file_ascii_out,'w')
-                fw_ascii.write('# z pz\n')
-                for ii in range(len(n)):
-                    fw_ascii.write('%.3f %.3f\n'%(nbins[ii]+(nbins[ii+1]-nbins[ii])/2.,n[ii]))
-                fw_ascii.close()
-
-            xx = yy * 0 + self.z_cz[0]
-            ax1.plot(xx,yy,linestyle='--',linewidth=1,color='orangered')
-            xx = yy * 0 + self.z_cz[2]
-            ax1.plot(xx,yy,linestyle='--',linewidth=1,color='orangered')
-            xx = yy * 0 + self.zgal
-            ax1.plot(xx,yy,linestyle='-',linewidth=1,color='royalblue', label='Input redshift')
-
-            # Prior:
-            ax1.plot(self.z_prior, self.p_prior * np.max(yy)/np.max(self.p_prior), linestyle='--', linewidth=1, color='cyan', label='Prior')
-
-            # Label:
-            ax1.set_xlabel('Redshift')
-            ax1.set_ylabel('$dn/dz$')
-            zp_min,zp_max = self.z_cz[0] - (self.z_cz[1]-self.z_cz[0])*3, self.z_cz[2] + (self.z_cz[2]-self.z_cz[1])*3
-            ax1.set_xlim(zp_min,zp_max)
-            ax1.legend(loc=0)
-            
-            # Save:
-            file_out = self.DIR_OUT + 'zmc_' + self.ID + '.png'
-            print('Figure is saved in %s'%file_out)
-
-            if f_interact:
-                fig.savefig(file_out, dpi=300)
-                # return fig, ax1
-            else:
-                plt.savefig(file_out, dpi=300)
-                plt.close()
-                # return True
-        except:
-            print('z-distribution figure is not generated.')
-            pass
-
-
-        if os.path.exists(self.file_zprob):
-            # Also, make zprob;
-            fig = plt.figure(figsize=(6.5,2.5))
-            fig.subplots_adjust(top=0.96, bottom=0.16, left=0.09, right=0.99, hspace=0.15, wspace=0.25)
-            ax1 = fig.add_subplot(111)
-
-            fd = ascii.read(self.file_zprob)
-            z = fd['z']
-            pz = fd['p(z)']
-            pz /= pz.max()
-
-            # prob:
-            ax1.plot(z, pz, linestyle='-', linewidth=1, color='r', label='')
-
-            # Label:
-            ax1.set_xlabel('Redshift')
-            ax1.set_ylabel('$p(z)$')
-            file_out = self.DIR_OUT + 'zprob_' + self.ID + '.png'
-            if f_interact:
-                fig.savefig(file_out, dpi=300)
-            else:
-                plt.savefig(file_out, dpi=300)
-                plt.close()
-
-
-    def add_param(self, fit_params, sigz=1.0, zmin=None, zmax=None):
-        '''
-        Add new parameters.
-
-        '''
-        f_add = False
-
-        # Redshift
-        if self.fzmc == 1:
-            if zmin == None:
-                self.zmcmin = 0
-            if zmax == None:
-                self.zmcmax = 15
-            fit_params.add('zmc', value=self.zgal, min=self.zmcmin, max=self.zmcmax)
-            print('Redshift is set as a free parameter (z in [%.2f:%.2f])'%(self.zmcmin, self.zmcmax))
-            f_add = True
-
-        # Dust;
-        if self.f_dust:
-            Tdust = self.Temp
-            if not self.TDUSTFIX == None:
-                fit_params.add('TDUST', value=self.NTDUST, vary=False)
-            elif len(Tdust)-1>0:
-                fit_params.add('TDUST', value=len(Tdust)/2., min=0, max=len(Tdust)-1)
-                self.ndim += 1
-            else:
-                fit_params.add('TDUST', value=0, vary=False)
-
-            fit_params.add('MDUST', value=9, min=0, max=15)
-            self.ndim += 1
-            self.dict = self.read_data(self.Cz0, self.Cz1, self.Cz2, self.zgal, add_fir=self.f_dust)
-            f_add = True
-
-        # Nebular; ver1.6
-        if self.fneb:
-            fit_params.add('Aneb', value=self.Aini, min=self.Amin, max=self.Amax)
-            self.ndim += 1
-            if not self.logUFIX == None:
-                fit_params.add('logU', value=self.logUFIX, vary=False)
-            else:
-                fit_params.add('logU', value=np.median(self.logUs), min=self.logUMIN, max=self.logUMAX)
-                self.ndim += 1
-            f_add = True
-
-        self.fit_params = fit_params
-
-        return f_add
-
-
-    def set_param(self):
-        '''
-        Set parameters.
-        '''
-        print('##################')
-        print('Setting parameters')
-        print('##################\n')
-        agemax = self.cosmo.age(self.zgal).value 
-        fit_params = Parameters()
-        f_Alog = True
-        if f_Alog:
-            try:
-                self.Amin = float(self.inputs['AMIN'])
-                self.Amax = float(self.inputs['AMAX'])
-            except:
-                self.Amin = -5
-                self.Amax = 10
-            self.Aini = -1
-        else:
-            self.Amin = 0
-            self.Amax = 1e3
-            self.Aini = 1
-
-        if self.SFH_FORM==-99:
-            self.age_vary = []
-            if len(self.age) != len(self.aamin):
-                for aa in range(len(self.age)):
-                    if aa not in self.aamin:
-                        fit_params.add('A'+str(aa), value=self.Amin, vary=False)
-                        self.ndim -= 1                    
-                        self.age_vary.append(False)
-                    else:
-                        fit_params.add('A'+str(aa), value=self.Aini, min=self.Amin, max=self.Amax)
-                        self.age_vary.append(True)
-            else:
-                for aa in range(len(self.age)):
-                    if self.age[aa] == 99:
-                        fit_params.add('A'+str(aa), value=self.Amin, vary=False)
-                        self.ndim -= 1
-                        self.age_vary.append(False)
-                    elif self.age[aa]>agemax and not self.force_agefix:
-                        print('At this redshift, A%d is beyond the age of universe and not being used.'%(aa))
-                        fit_params.add('A'+str(aa), value=self.Amin, vary=False)
-                        self.ndim -= 1
-                        self.age_vary.append(False)
-                    else:
-                        fit_params.add('A'+str(aa), value=self.Aini, min=self.Amin, max=self.Amax)
-                        self.age_vary.append(True)
-
-        else:
-            for aa in range(self.npeak):
-                tauini = (self.taumin+self.taumax)/2.
-                ageini = (self.agemin + self.agemax)/2.
-                fit_params.add('A%d'%aa, value=self.Aini, min=self.Amin, max=self.Amax)
-
-                # Check AGE fix; TBD.
-                try:
-                    AGEFIX = float(self.inputs['AGEFIX'])
-                    fit_params.add('AGE%d'%aa, value=AGEFIX, vary=False)
-                    self.agemin = AGEFIX
-                    self.agemax = AGEFIX
-                    self.ndim -= 1
-                    print('AGEFIX is found. Set to %.2f'%(AGEFIX))
-                except:
-                    tcosmo = np.log10(self.cosmo.age(self.zgal).value) 
-                    agemax_tmp = self.agemax
-                    if agemax_tmp > tcosmo:
-                        agemax_tmp = tcosmo
-                        print('Maximum age is set to the age of the univese (%.1fGyr) at this redshift.'%(self.cosmo.age(self.zgal).value))
-                    if self.npeak>1:
-                        if aa == 0:
-                            fit_params.add('AGE%d'%aa, value=ageini, min=self.agemin, max=np.log10(1.0))
-                        else:
-                            ageini = np.log10(1.0)
-                            fit_params.add('AGE%d'%aa, value=ageini, min=np.log10(1.0), max=agemax_tmp)
-                    else:
-                        #fit_params.add('AGE%d'%aa, value=0.0, min=0.0, max=0.01)
-                        fit_params.add('AGE%d'%aa, value=ageini, min=self.agemin, max=agemax_tmp)
-
-                # Check Tau fix;
-                if self.npeak>1:
-                    if aa == 0:
-                        fit_params.add('TAU%d'%aa, value=tauini, min=self.taumin, max=self.taumax)
-                    else:
-                        tauini = np.log10(0.3)
-                        fit_params.add('TAU%d'%aa, value=tauini, min=self.taumin, max=np.log10(0.3))
-                else:
-                    #fit_params.add('TAU%d'%aa, value=-0.8, min=-0.8, max=-0.79)
-                    fit_params.add('TAU%d'%aa, value=tauini, min=self.taumin, max=self.taumax)
-
-        #
-        # Dust attenuation;
-        #
-        try:
-            Avfix = float(self.inputs['AVFIX'])
-            fit_params.add('Av', value=Avfix, vary=False)
-            self.Avmin = Avfix
-            self.Avmax = Avfix
-        except:
-            try:
-                self.Avmin = float(self.inputs['AVMIN'])
-                self.Avmax = float(self.inputs['AVMAX'])
-                self.Avini = (self.Avmax+self.Avmin)/2.
-                self.Avini = 0.
-                if self.Avmin == self.Avmax:
-                    fit_params.add('Av', value=self.Avini, vary=False)
-                    self.Avmin = self.Avini
-                    self.Avmax = self.Avini
-                else:
-                    fit_params.add('Av', value=self.Avini, min=self.Avmin, max=self.Avmax)
-            except:
-                self.Avmin = 0.
-                self.Avmax = 4.
-                self.Avini = 0.5 #(Avmax-Avmin)/2. 
-                print('Dust is set in [%.1f:%.1f]/mag. Initial value is set to %.1f'%(self.Avmin,self.Avmax,self.Avini))
-                fit_params.add('Av', value=self.Avini, min=self.Avmin, max=self.Avmax)
-
-        #
-        # Metallicity;
-        #
-        if int(self.inputs['ZEVOL']) == 1:
-            for aa in range(len(self.age)):
-                if self.age[aa] == 99 or self.age[aa]>agemax:
-                    fit_params.add('Z'+str(aa), value=0, min=0, max=1e-10)
-                else:
-                    fit_params.add('Z'+str(aa), value=0, min=self.Zmin, max=self.Zmax)
-        else:
-            if self.has_ZFIX:
-                aa = 0
-                fit_params.add('Z'+str(aa), value=self.ZFIX, vary=False)
-            else:
-                aa = 0
-                if np.min(self.Zall)==np.max(self.Zall):
-                    fit_params.add('Z'+str(aa), value=np.min(self.Zall), vary=False)
-                else:
-                    fit_params.add('Z'+str(aa), value=0, min=self.Zmin, max=self.Zmax)
-
-       # Error parameter
-        try:
-            ferr = self.ferr
-            if ferr == 1:
-                fit_params.add('logf', value=0, min=-10, max=3) # in linear
-                self.ndim += 1
-                f_add = True
-        except:
-            ferr = 0
-            pass
-
-        self.fit_params = fit_params
-        return fit_params
-
-
-    def prepare_class(self, add_fir=None):
-        '''
-        '''
-        print('#################')
-        print('Preparing library')
-        print('#################\n')
-        # Load Spectral library;
-        self.lib = self.fnc.open_spec_fits(fall=0)
-        self.lib_all = self.fnc.open_spec_fits(fall=1, orig=True)
-
-        if self.f_dust:
-            self.lib_dust = self.fnc.open_spec_dust_fits(fall=0)
-            self.lib_dust_all = self.fnc.open_spec_dust_fits(fall=1)
-        if self.fneb:
-            self.lib_neb = self.fnc.open_spec_fits(fall=0, f_neb=True)
-            self.lib_neb_all = self.fnc.open_spec_fits(fall=1, f_neb=True)
-
-        if add_fir == None:
-            add_fir = self.f_dust
-
-        # For MCMC;
-        self.nmc = int(self.inputs['NMC'])
-        self.nwalk = int(self.inputs['NWALK'])
-        self.nmc_cz = int(self.inputs['NMCZ'])
-        self.nwalk_cz = int(self.inputs['NWALKZ'])
-        self.ZEVOL = int(self.inputs['ZEVOL'])
-        self.fzvis = int(self.inputs['ZVIS'])
-        if self.f_nested:
-            print('Nested sample is on. Nelder is used for time saving analysis.')
-            self.fneld = 1 
-
-        try:
-            self.ntemp = int(self.inputs['NTEMP'])
-        except:
-            self.ntemp = 1
-
-        try:
-            if int(inputs['DISP']) == 1:
-                self.f_disp = True
-            else:
-                self.f_disp = False
-        except:
-            self.f_disp = False
-
-        #
-        # Dust model specification;
-        #
-        try:
-            dust_model = int(self.inputs['DUST_MODEL'])
-        except:
-            dust_model = 0
-
-        # Error parameter
-        try:
-            self.ferr = int(self.inputs['F_ERR'])
-        except:
-            self.ferr = 0
-            pass
-
-        #
-        # Observed Data;
-        #
-        self.dict = self.read_data(self.Cz0, self.Cz1, self.Cz2, self.zgal, add_fir=add_fir)
-
-        # Set parameters;
-        self.set_param()
-    
-        return True
-
-
-    def get_shuffle(self, out, nshuf=3.0, amp=1e-4):
-        '''
-        Shuffles initial parameters of each walker, to give it extra randomeness.
-        '''
-        pos = np.zeros((self.nwalk, self.ndim), 'float')
-        for ii in range(pos.shape[0]):
-            aa = 0
-            for aatmp,key in enumerate(out.params.valuesdict()):
-                if out.params[key].vary == True:
-                    pos[ii,aa] += out.params[key].value
-                    # This is critical to avoid parameters fall on the boundary.
-                    delpar = (out.params[key].max-out.params[key].min)/1000
-                    # or not,
-                    delpar = 0
-                    if np.random.uniform(0,1) > (1. - 1./self.ndim):        
-                        pos[ii,aa] = np.random.uniform(out.params[key].min+delpar, out.params[key].max-delpar)
-                    else:
-                        if pos[ii,aa]<out.params[key].min+delpar or pos[ii,aa]>out.params[key].max-delpar:
-                            pos[ii,aa] = np.random.uniform(out.params[key].min+delpar, out.params[key].max-delpar)
-
-                    aa += 1
-        return pos
-
-
-    def main(self, cornerplot:bool=True, specplot=1, sigz=1.0, ezmin=0.01, ferr=0,
-            f_move:bool=False, verbose:bool=False, skip_fitz:bool=False, out=None, f_plot_accept:bool=True,
-            f_shuffle:bool=True, amp_shuffle=1e-2, check_converge:bool=True, Zini=None, f_plot_chain:bool=True,
-            f_chind:bool=True, ncpu:int=0, f_prior_sfh:bool=False, norder_sfh_prior:int=3, include_bb=True):
-        '''
-        Main module of this script.
-
-        Parameters
-        ----------
-        ferr : float
-            Used for error parameter.
-        skip_fitz : bool 
-            Skip redshift fit.
-        sigz : float 
-            Confidence interval for redshift fit (i.e. n-sigma).
-        ezmin : float 
-            minimum error in redshift
-        f_plot_accept : bool
-            Output acceptance plot of mcmc chains.
-        f_shuffle : bool
-            Randomly shuffle some of initial parameters in walkers.
-        check_converge : bool
-            Check convergence at every certain number.
-        f_plot_chain : book
-            Plot MC sample chain.
-        '''
-        import emcee
-        import zeus
-        try:
-            import multiprocess
-        except:
-            import multiprocessing as multiprocess
-
-        from .posterior_flexible import Post
-
-        # Call likelihood/prior/posterior function;
-        class_post = Post(self)
-
-        # Prepare library, data, etc.
-        self.prepare_class()
-
-        print('########################')
-        print('### Fitting Function ###')
-        print('########################')
-        start = timeit.default_timer()        
-        if not os.path.exists(self.DIR_TMP):
-            os.mkdir(self.DIR_TMP)
-
-        # Initial Z:
-        if Zini == None:
-            Zini = self.Zall
-
-        ####################################
-        # Initial Metallicity Determination
-        ####################################
-        # Get initial parameters
-        if not skip_fitz or out == None:
-            
-            # Do you want to prepare a template for redshift fit by using only spectrum?;
-            f_only_spec = False
-            out, chidef, Zbest = get_leastsq(self, Zini, self.fneld, self.age, self.fit_params, class_post.residual,\
-            self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.ID, f_only_spec=f_only_spec)
-
-            # Best fit
-            csq = out.chisqr
-            rcsq = out.redchi
-            fitc = [csq, rcsq] # Chi2, Reduced-chi2
-            ZZ = Zbest # This is really important/does affect lnprob/residual.
-            if self.fitc_cz_prev == None:
-                self.fitc_cz_prev = rcsq
-
-            print('\n\n')
-            print('#####################################')
-            print('Zbest, chi are;',Zbest,chidef)
-            print('Params are;',fit_report(out))
-            print('#####################################')
-            print('\n\n')
-
-            Av_tmp = out.params['Av'].value
-            AA_tmp = np.zeros(len(self.age), dtype='float')
-            ZZ_tmp = np.zeros(len(self.age), dtype='float')
-            nrd_tmp, fm_tmp, xm_tmp = self.fnc.get_template(out, f_val=True, f_nrd=True, f_neb=False)
-        else:
-            csq = out.chisqr
-            rcsq = out.redchi
-            fitc = [csq, rcsq]
-
-        ########################
-        # Check redshift
-        ########################
-        if skip_fitz:
-            flag_z = 'y'
-        else:
-            flag_z = self.fit_redshift(xm_tmp, fm_tmp, delzz=0.001, include_bb=include_bb)
-
-        #################################################
-        # Gor for mcmc phase
-        #################################################
-        if flag_z == 'y' or flag_z == '':
-
-            #######################
-            # Add parameters;
-            #######################
-            out_keep = out
-            f_add = self.add_param(self.fit_params, sigz=self.sigz, zmin=self.zmcmin, zmax=self.zmcmax)
-
-            # Then, minimize again.
-            if f_add:
-                if self.fneld == 1:
-                    fit_name = 'nelder'
-                elif self.fneld == 0:
-                    fit_name = 'powell'
-                elif self.fneld == 2:
-                    fit_name = 'leastsq'
-                else:
-                    fit_name = self.fneld
-
-                out = minimize(class_post.residual, self.fit_params, args=(self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust), method=fit_name) 
-                # showing this is confusing.
-                # print('\nMinimizer refinement;')
-                # print(fit_report(out))
-
-                # Fix params to what we had before.
-                if self.fzmc:
-                    out.params['zmc'].value = self.zgal
-                out.params['Av'].value = out_keep.params['Av'].value
-                for aa in range(len(self.age)):
-                    out.params['A'+str(aa)].value = out_keep.params['A'+str(aa)].value
-                    try:
-                        out.params['Z'+str(aa)].value = out_keep.params['Z'+str(aa)].value
-                    except:
-                        out.params['Z0'].value = out_keep.params['Z0'].value
-
-
-            ##############################
-            print('\n\n')
-            print('###############################')
-            print('Input redshift is adopted.')
-            print('Starting long journey in MCMC.')
-            print('###############################')
-            print('\n\n')
-
-            ################################
-            print('\nMinimizer Defined\n')
-
-            print('########################')
-            print('### Starting sampling ##')
-            print('########################\n')
-            start_mc = timeit.default_timer()
-
-            # MCMC;
-            if self.f_mcmc or self.f_zeus:
-                nburn = int(self.nmc/2)
-                if f_shuffle:
-                    # ZEUS may fail to run with f_shuffle.
-                    pos = self.get_shuffle(out, amp=amp_shuffle)
-                else:
-                    pos = amp_shuffle * np.random.randn(self.nwalk, self.ndim)
-                    aa = 0
-                    for aatmp,key in enumerate(out.params.valuesdict()):
-                        if out.params[key].vary:
-                            pos[:,aa] += out.params[key].value
-                            aa += 1
-
-                if self.f_zeus:
-                    print('sampling with ZEUS')
-                    check_converge = False
-                    f_burnin = True
-                    if f_burnin:
-                        # Burn phase;
-                        moves = zeus.moves.DifferentialMove() #GlobalMove()
-                        sampler = zeus.EnsembleSampler(self.nwalk, self.ndim, class_post.lnprob_emcee, \
-                            args=[out.params, self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust], \
-                            moves=moves, maxiter=1e6,\
-                            kwargs={'f_val':True, 'out':out, 'lnpreject':-np.inf, 'f_chind':f_chind, 
-                            'f_prior_sfh':f_prior_sfh, 'norder_sfh_prior':norder_sfh_prior},\
-                            )
-                        # Run MCMC
-                        nburn = int(self.nmc/10)
-
-                        print('Running burn-in')
-                        sampler.run_mcmc(pos, nburn)
-                        print('Done burn-in')
-
-                        # Get the burnin samples
-                        burnin = sampler.get_chain()
-
-                        # Set the new starting positions of walkers based on their last positions
-                        pos = burnin[-1]
-
-                    # Switch sampler;
-                    moves = zeus.moves.GlobalMove()
-                    sampler = zeus.EnsembleSampler(self.nwalk, self.ndim, class_post.lnprob_emcee, \
-                        args=[out.params, self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust], \
-                        moves=moves, maxiter=1e4,\
-                        kwargs={'f_val':True, 'out':out, 'lnpreject':-np.inf, 
-                        'f_prior_sfh':f_prior_sfh, 'norder_sfh_prior':norder_sfh_prior},\
-                        )
-
-                else:
-                    print('sampling with EMCEE')
-                    moves=[(emcee.moves.DEMove(), 0.8), (emcee.moves.DESnookerMove(), 0.2),]
-                    sampler = emcee.EnsembleSampler(self.nwalk, self.ndim, class_post.lnprob_emcee, \
-                        args=(out.params, self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust),\
-                        #moves=moves,\
-                        kwargs={'f_val': True, 'out': out, 'lnpreject':-np.inf, 
-                        'f_prior_sfh':f_prior_sfh, 'norder_sfh_prior':norder_sfh_prior},\
-                        )
-
-                if check_converge:
-                    # Check convergence every number;
-                    nevery = int(self.nmc/10)
-                    nconverge = 10.
-                    if nevery < 1000:
-                        nevery = 1000
-                    index = 0
-                    old_tau = np.inf
-                    autocorr = np.empty(self.nmc)
-                    for sample in sampler.sample(pos, iterations=self.nmc, progress=True):
-                        # Only check convergence every "nevery" steps
-                        if sampler.iteration % nevery:
-                            continue
-
-                        # Compute the autocorrelation time so far
-                        # Using tol=0 means that we'll always get an estimate even
-                        # if it isn't trustworthy
-                        tau = sampler.get_autocorr_time(tol=0)
-                        autocorr[index] = np.mean(tau)
-                        index += 1
-
-                        # Check convergence
-                        converged = np.all(tau * 100 < sampler.iteration)
-                        converged &= np.all(np.abs(old_tau - tau) / tau < nconverge)
-                        if converged:
-                            print('Converged at %d/%d\n'%(index*nevery,self.nmc))
-                            nburn = int(index*nevery / 50) # Burn 50%
-                            self.nmc = index*nevery
-                            break
-                        old_tau = tau
-                else:
-                    sampler.run_mcmc(pos, self.nmc, progress=True)
-                
-                flat_samples = sampler.get_chain(discard=nburn, thin=10, flat=True)
-
-                # Plot for chain.
-                if f_plot_chain:
-                    fig, axes = plt.subplots(self.ndim, figsize=(10, 7), sharex=True)
-                    samples = sampler.get_chain()
-                    labels = []
-                    for key in out.params.valuesdict():
-                        if out.params[key].vary:
-                            labels.append(key)
-                    for i in range(self.ndim):
-                        if self.ndim>1:
-                            ax = axes[i]
-                        else:
-                            ax = axes
-                        ax.plot(sampler.get_chain()[:,:,i], alpha=0.3, color='k')
-                        ax.set_xlim(0, len(samples))
-                        ax.yaxis.set_label_coords(-0.1, 0.5)
-                        ax.set_ylabel(labels[i])
-                    if self.ndim>1:
-                        axes[-1].set_xlabel("step number")
-                    else:
-                        axes.set_xlabel("step number")
-                    plt.savefig('%s/chain_%s.png'%(self.DIR_OUT,self.ID))
-
-                # Similar for nested;
-                # Dummy just to get structures;
-                print('\nRunning dummy sampler. Disregard message from here;\n')
-                mini = Minimizer(class_post.lnprob_emcee, out.params, 
-                fcn_args=[out.params, self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust], 
-                f_disp=False, nan_policy='omit',
-                moves=moves,\
-                kwargs={'f_val': True},\
-                )
-                res = mini.emcee(burn=0, steps=10, thin=1, nwalkers=self.nwalk, 
-                params=out.params, is_weighted=True, ntemps=self.ntemp, workers=ncpu, float_behavior='posterior', progress=False)
-                print('\nto here.\n')
-
-                # Update;
-                var_names = []#res.var_names
-                params_value = {}
-                ii = 0
-                for key in out.params:
-                    if out.params[key].vary:
-                        var_names.append(key)
-                        params_value[key] = np.median(flat_samples[nburn:,ii])
-                        ii += 1
-
-                import pandas as pd
-                flatchain = pd.DataFrame(data=flat_samples[nburn:,:], columns=var_names)
-
-                class get_res:
-                    def __init__(self, flatchain, var_names, params_value, res):
-                        self.flatchain = flatchain
-                        self.var_names = var_names
-                        self.params_value = params_value
-                        self.params = res.params
-                        for key in var_names:
-                            self.params[key].value = params_value[key]
-
-                # Inserting result from res0 into res structure;
-                res = get_res(flatchain, var_names, params_value, res)
-                res.bic = -99
-
-            elif self.f_nested:
-                import dynesty
-                from dynesty import NestedSampler
-
-                nlive = self.nmc      # number of live point
-                maxmcmc = self.nmc    # maximum MCMC chain length
-                nthreads = ncpu       # use one CPU core
-                bound = 'multi'   # use MutliNest algorithm for bounds
-                sample = 'unif' #'rwalk' # uniform sampling
-                tol = 0.01         # the stopping criterion
-                ndim_nest = self.ndim #0
-
-                #pars, fy, ey, wht, f_fir
-                logl_kwargs = {} #{'pars':out.params, 'fy':dict['fy'], 'ey':dict['ey'], 'wht':dict['wht2'], 'f_fir':self.f_dust}
-                logl_args = [self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust]
-                ptform_kwargs = {} #{'pars': out.params}
-                ptform_args = []
-
-                from .posterior_nested import Post_nested
-                class_post_nested = Post_nested(self, params=out.params)
-
-                # Initialize;
-                sampler = NestedSampler(class_post_nested.lnlike, class_post_nested.prior_transform, ndim_nest, walks=self.nwalk,\
-                bound=bound, sample=sample, nlive=nlive, ptform_args=ptform_args, logl_args=logl_args, logl_kwargs=logl_kwargs)
-
-                # Run;
-                sampler.run_nested(dlogz=tol, maxiter=maxmcmc, print_progress=self.f_disp)                 
-                res0 = sampler.results # get results dictionary from sampler
-                
-                # Dummy just to get structures;
-                mini = Minimizer(class_post.lnprob, out.params, fcn_args=[self.dict['fy'], self.dict['ey'], self.dict['wht2'], self.f_dust], f_disp=False, \
-                    moves=[(emcee.moves.DEMove(), 0.8), (emcee.moves.DESnookerMove(), 0.2),])
-                res = mini.emcee(burn=0, steps=10, thin=1, nwalkers=self.nwalk, 
-                params=out.params, is_weighted=True, ntemps=self.ntemp, workers=ncpu, float_behavior='posterior')
-
-                # Update;
-                nburn = int(self.nmc/2)
-                var_names = []#res.var_names
-                params_value = {}
-                ii = 0
-                for key in out.params:
-                    if out.params[key].vary:
-                        var_names.append(key)
-                        params_value[key] = np.median(res0.samples[nburn:,ii])
-                        ii += 1
-
-                import pandas as pd
-                flatchain = pd.DataFrame(data=res0.samples[nburn:,:], columns=var_names)
-
-                class get_res:
-                    def __init__(self, flatchain, var_names, params_value, res):
-                        self.flatchain = flatchain
-                        self.var_names = var_names
-                        self.params_value = params_value
-                        self.params = res.params
-                        for key in var_names:
-                            self.params[key].value = params_value[key]
-
-                # Inserting result from res0 into res structure;
-                res = get_res(flatchain, var_names, params_value, res)
-                res.bic = -99
-
-            else:
-                print('Sampling is not specified. Failed. Exiting.')
-                return False
-
-            stop_mc  = timeit.default_timer()
-            tcalc_mc = stop_mc - start_mc
-            print('###############################')
-            print('### MCMC part took %.1f sec ###'%(tcalc_mc))
-            print('###############################')
-
-            #----------- Save pckl file
-            #-------- store chain into a cpkl file
-            start_mc = timeit.default_timer()
-            burnin = int(self.nmc/2)
-            #burnin   = 0 # Since already burnt in.
-            savepath = self.DIR_OUT
-            cpklname = 'chain_' + self.ID + '_corner.cpkl'
-            savecpkl({'chain':flatchain,
-                          'burnin':burnin, 'nwalkers':self.nwalk,'niter':self.nmc,'ndim':self.ndim},
-                         savepath+cpklname) # Already burn in
-            stop_mc = timeit.default_timer()
-            tcalc_mc = stop_mc - start_mc
-            if verbose:
-                print('#################################')
-                print('### Saving chain took %.1f sec'%(tcalc_mc))
-                print('#################################')
-
-
-            ####################
-            # MCMC corner plot.
-            ####################
-            if cornerplot:
-                levels = [0.68, 0.95, 0.997]
-                quantiles = [0.01, 0.99]
-                val_truth = []
-                for par in var_names:
-                    val_truth.append(params_value[par])
-                fig1 = corner.corner(flatchain, labels=var_names, \
-                label_kwargs={'fontsize':16}, quantiles=quantiles, show_titles=False, \
-                title_kwargs={"fontsize": 14}, \
-                truths=val_truth, \
-                plot_datapoints=False, plot_contours=True, no_fill_contours=True, \
-                plot_density=False, levels=levels, truth_color='gray', color='#4682b4')
-                fig1.savefig(self.DIR_OUT + 'SPEC_' + self.ID + '_corner.png')
-                self.cornerplot_fig = fig1
-
-            # Analyze MCMC results.
-            # Write to file.
-            stop  = timeit.default_timer()
-            tcalc = stop - start
-
-            # Then writing;
-            start_mc = timeit.default_timer()
-            get_param(self, res, fitc, tcalc=tcalc, burnin=burnin)
-            stop_mc = timeit.default_timer()
-            tcalc_mc = stop_mc - start_mc
-
-            return 2 # Cannot set to 1, to distinguish from retuen True
-
-
-        elif flag_z == 'm':
-            zrecom = raw_input('What is your manual input for redshift? [%.3f] '%(self.zgal))
-            if zrecom != '':
-                zrecom = float(zrecom)
-            else:
-                zrecom = self.zgal
-
-            Czrec0 = raw_input('What is your manual input for Cz0? [%.3f] '%(self.Cz0))
-            if Czrec0 != '':
-                Czrec0 = float(Czrec0)
-            else:
-                Czrec0 = self.Cz0
-
-            Czrec1 = raw_input('What is your manual input for Cz1? [%.3f] '%(self.Cz1))
-            if Czrec1 != '':
-                Czrec1 = float(Czrec1)
-            else:
-                Czrec1 = self.Cz1
-
-            Czrec2 = raw_input('What is your manual input for Cz2? [%.3f] '%(self.Cz2))
-            if Czrec2 != '':
-                Czrec2 = float(Czrec2)
-            else:
-                Czrec2 = self.Cz2
-
-            self.zprev = self.zgal   # Input redshift for previous run
-            self.zgal = zrecom # Recommended redshift from previous run
-            self.Cz0 = Czrec0
-            self.Cz1 = Czrec1
-            self.Cz2 = Czrec2
-            print('\n\n')
-            print('Generating model templates with input redshift and Scale.')
-            print('\n\n')
-            return True
-
-        else:
-            print('\n\n')
-            
-            flag_gen = raw_input('Do you want to make templates with recommended redshift, Cz0, Cz1, Cz2, and chi2/nu , %.5f %.5f %.5f %.5f %.5f? ([y]/n) '%\
-                (self.zrecom, self.Czrec0, self.Czrec1, self.Czrec2, self.fitc_cz_prev))
-
-            self.get_zdist()
-
-            if flag_gen == 'y' or flag_gen == '':
-                self.zprev = self.zgal # Input redshift for previous run
-                self.zgal = self.zrecom # Recommended redshift from previous run
-                self.Cz0 = self.Czrec0
-                self.Cz1 = self.Czrec1
-                self.Cz2 = self.Czrec2
-                return True
-            else:
-                print('\n\n')
-                print('Terminating process.')
-                print('\n\n')
-                return False
diff --git a/build/lib/gsf/function.py b/build/lib/gsf/function.py
deleted file mode 100644
index d31c168..0000000
--- a/build/lib/gsf/function.py
+++ /dev/null
@@ -1,1487 +0,0 @@
-from scipy import asarray as ar,exp
-import numpy as np
-import sys
-from scipy.integrate import simps
-import pickle as cPickle
-import os
-import scipy.interpolate as interpolate
-from scipy.interpolate import interp1d
-import logging
-
-################
-# Line library
-################
-LN0 = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3L', 'O3H', 'Mgb', 'Halpha', 'S2L', 'S2H']
-LW0 = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4960, 5008, 5175, 6563, 6717, 6731]
-fLW = np.zeros(len(LW0), dtype='int') # flag.
-c = 3.e18 # A/s
-
-
-def str2bool(v):
-    '''
-    '''
-    if isinstance(v, bool):
-       return v
-    if v.lower() in ('yes', 'true', 't', 'y', '1'):
-        return True
-    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
-        return False
-    else:
-        raise argparse.ArgumentTypeError('Boolean value expected.')
-
-
-def get_uvbeta(lm, flam, zbes, lam_blue=1650, lam_red=2300):
-    '''
-    Purpose
-    -------
-    get UV beta_lambda slope.
-
-    Parameters
-    ----------
-    lm : float array
-        in lambda
-    flam : float array
-        in flambda 
-    '''
-    con_uv = (lm/(1.+zbes)>lam_blue) & (lm/(1.+zbes)<lam_red)
-    #flam = fnutolam(lm,fl)
-    try:
-        fit_results = np.polyfit(np.log10(lm/(1.+zbes))[con_uv], np.log10(flam)[con_uv], 1) #, w=flam[con_uv])
-        beta = fit_results[0]
-        if np.isnan(beta):
-            beta = -99
-    except:
-        beta = -99
-    return beta
-
-
-def func_tmp(xint,eobs,fmodel):
-    '''
-    '''
-    int_tmp = np.exp(-0.5 * ((xint-fmodel)/eobs)**2)
-    return int_tmp
-
-
-def get_chi2(fy, ey, wht3, ysump, ndim_eff, SNlim=1.0, f_chind=True, f_exclude=False, xbb=None, x_ex=None):
-    '''
-    '''
-    from scipy import special
-    if f_chind:
-        conw = (wht3>0) & (ey>0) & (fy/ey>SNlim)
-    else:
-        conw = (wht3>0) & (ey>0)
-
-    resid = fy-ysump
-    chi2 = sum((np.square(resid) * np.sqrt(wht3))[conw])
-
-    chi_nd = 0.0
-    if f_chind:
-        f_ex = np.zeros(len(fy), 'int')
-        for ii in range(len(fy)):
-            if f_exclude:
-                if xbb[ii] in x_ex:
-                    f_ex[ii] = 1
-
-        con_up = (ey>0) & (fy/ey<=SNlim) & (f_ex == 0)
-        x_erf = (ey[con_up] - ysump[con_up]) / (np.sqrt(2) * ey[con_up])
-        f_erf = special.erf(x_erf)
-        chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey[con_up] * (1 + f_erf)) )
-
-    # Number of degree;
-    con_nod = (wht3>0) & (ey>0) #& (fy/ey>SNlim)
-    nod = int(len(wht3[con_nod])-ndim_eff)
-    if nod>0:
-        fin_chi2 = (chi2 - 2 * chi_nd) / nod
-    else:
-        fin_chi2 = -99
-
-    return chi2,fin_chi2
-
-
-def get_ind(wave,flux):
-    '''
-    Gets Lick index for input
-
-    Returns
-    -------
-    equivalent width
-    '''    
-    lml     = [4268, 5143, 5233, 5305, 5862, 4828, 4628, 4985, 5669, 5742, 4895, 4895, 5818, 6068]
-    lmcl    = [4283, 5161, 5246, 5312, 5879, 4848, 4648, 5005, 5689, 5762, 5069, 5154, 5938, 6191]
-    lmcr    = [4318, 5193, 5286, 5352, 5911, 4877, 4668, 5925, 5709, 5782, 5134, 5197, 5996, 6274]
-    lmr     = [4336, 5206, 5318, 5363, 5950, 4892, 4688, 5945, 5729, 5802, 5366, 5366, 6105, 6417]
-
-    W = np.zeros(len(lml), dtype='float')
-    for ii in range(len(lml)):
-        con_cen = (wave>lmcl[ii]) & (wave<lmcr[ii])
-        con_sid = ((wave<lmcl[ii]) & (wave>lml[ii])) | ((wave<lmr[ii]) & (wave>lmcr[ii]))
-
-        Ic = np.mean(flux[con_cen])
-        Is = np.mean(flux[con_sid])
-
-        delam = lmcr[ii] - lmcl[ii]
-
-        if ii < 10:
-            W[ii] = (1. - Ic/Is) * delam
-        elif 1. - Ic/Is > 0:
-            W[ii] = -2.5 * np.log10(1. - Ic/Is)
-        else:
-            W[ii] = -99
-
-    return W
-
-
-def printProgressBar (iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', printEnd="\r", emojis=['']):
-    '''
-    Call in a loop to create terminal progress bar.
-
-    Parameters
-    ----------
-    iteration : int 
-        current iteration
-    total : int 
-        total iterations
-    prefix : str
-        prefix string
-    suffix : str
-        suffix string
-    decimals : int
-        positive number of decimals in percent complete
-    length : int
-        character length of bar
-    fill : str
-        bar fill character
-    printEnd : str 
-        end character
-    '''
-    percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
-    if fill == None:
-        if float(percent) < 33:
-            fill = emojis[0]
-        elif float(percent) < 66:
-            fill = emojis[1]
-        elif float(percent) < 99:
-            fill = emojis[2]
-        else:
-            fill = emojis[3]
-
-    filledLength = int(length * iteration // total)
-    bar = fill * filledLength + '-' * (length - filledLength)
-    string = '(%d/%d)'%(iteration,total)
-    print(f'\r{prefix} |{bar}| {percent}% {suffix} {string}', end = printEnd)
-    # Print New Line on Complete
-    if iteration == total: 
-        print()
-
-
-def get_input():
-    '''
-    Gets a default dictionary for input params.
-
-    '''
-    inputs = {'ID':'10000', 'PA':'00', 'ZMC':0.01, 'CZ0':1.0, 'CZ1':1.0, 'BPASS':0,
-    'DIR_OUT':'./output/', 'DIR_TEMP':'./templates/', 'DIR_FILT':'./filter/',
-    'NIMF':0, 'NMC':100, 'NWALK':50, 'NMCZ':30, 'NWALKZ':20,
-    'ZEVOL':0, 'ZVIS':0, 'FNELD':'differential_evolution', 'MC_SAMP':'EMCEE'}
-
-    return inputs
-
-
-def read_input(parfile):
-    '''
-    Gets info from param file.
-
-    Returns
-    -------
-    Input dictionary.
-
-    '''
-    input0 = []
-    input1 = []
-    file = open(parfile,'r')
-    while 1:
-        line = file.readline()
-        if not line:
-            break
-        else:
-            cols = str.split(line)
-            if len(cols)>0 and cols[0][0] != '#':
-                input0.append(cols[0])
-                input1.append(cols[1])
-    file.close()
-    inputs = {}
-    for i in range(len(input0)):
-        inputs[input0[i]]=input1[i]
-
-    return inputs
-
-
-def write_input(inputs, file_out='gsf.input'):
-    '''
-    Gets an ascii format param file.    
-
-    Returns
-    -------
-    file_out
-    '''
-    import gsf
-    fw = open(file_out, 'w')
-    fw.write('# gsf ver %s\n'%(gsf.__version__))
-    for key in inputs.keys():
-        fw.write('%s %s\n'%(key,inputs[key]))
-    return True
-
-
-def loadcpkl(cpklfile):
-    """
-    Load cpkl files.
-    """
-    import pickle
-    if not os.path.isfile(cpklfile): raise ValueError(' ERR: cannot find the input file')
-    f = open(cpklfile, 'rb') #, encoding='ISO-8859-1')
-
-    if sys.version_info.major == 2:
-        data = pickle.load(f)
-    elif sys.version_info.major == 3:
-        data = pickle.load(f, encoding='latin-1')
-
-    f.close()
-    return data
-
-
-def get_leastsq(MB, ZZtmp, fneld, age, fit_params, residual, fy, ey, wht, ID0, 
-    chidef=None, Zbest=0, f_keep=False, f_only_spec=False):
-    '''
-    Get initial parameters at various Z
-    '''
-    from lmfit import Model, Parameters, minimize, fit_report, Minimizer
-
-    if len(fy)<2:
-        print('Not enough data for quick fit. Exiting.')
-        return False
-
-    file = 'Z_' + ID0 + '.cat'
-    fwz = open(file, 'w')
-    fwz.write('# ID Zini chi/nu AA Av Zbest\n')
-
-    if fneld == 1:
-        fit_name = 'nelder'
-    elif fneld == 0:
-        fit_name = 'powell'
-    elif fneld == 2:
-        fit_name = 'leastsq'
-    else:
-        fit_name = fneld
-
-    fwz.write('# minimizer: %s\n' % fit_name)
-
-    if MB.has_ZFIX:
-        ZZtmp = [MB.ZFIX]
-
-    for zz in range(len(ZZtmp)):
-        ZZ = ZZtmp[zz]
-        for aa in range(MB.npeak):
-            if MB.ZEVOL == 1 or aa == 0:
-                fit_params['Z'+str(aa)].value = ZZ
-
-        f_fir = False
-
-        out_tmp = minimize(residual, fit_params, args=(fy, ey, wht, f_fir), 
-            method=fit_name, kws={'f_only_spec':f_only_spec})
-            
-        csq = out_tmp.chisqr
-        rcsq = out_tmp.redchi
-        fitc = [csq, rcsq] # Chi2, Reduced-chi2
-
-        fwz.write('%s %.2f %.5f'%(ID0, ZZ, fitc[1]))
-
-        AA_tmp = np.zeros(MB.npeak, dtype='float')
-        ZZ_tmp = np.zeros(MB.npeak, dtype='float')
-        for aa in range(MB.npeak):
-            AA_tmp[aa] = out_tmp.params['A'+str(aa)].value
-            fwz.write(' %.5f'%(AA_tmp[aa]))
-
-        Av_tmp = out_tmp.params['Av'].value
-        fwz.write(' %.5f'%(Av_tmp))
-        for aa in range(MB.npeak):
-            if MB.ZEVOL == 1 or aa == 0:
-                ZZ_tmp[aa] = out_tmp.params['Z'+str(aa)].value
-                fwz.write(' %.5f'%(ZZ_tmp[aa]))
-
-        fwz.write('\n')
-        if chidef==None:
-            chidef = fitc[1]
-            out = out_tmp
-        elif fitc[1]<chidef:
-            chidef = fitc[1]
-            out = out_tmp
-
-    fwz.close()
-
-    if not f_keep:
-        os.system('rm %s'%file)
-    
-    return out,chidef,Zbest
-
-
-def check_rejuv(age,SF,MS,SFMS_50,lm_old=10.0,delMS=0.2):
-    '''
-    A Function to check rejuvenation.
-
-    Parameters
-    ----------
-    delMS : float
-        Scatter around the Main Sequence. 0.2 dex, in default.
-    '''
-    age_set = 1.0
-    con_old = (age>=age_set)
-    con_mid = (age<age_set) & (age>np.min(age))
-
-    f_quen  = 0
-    f_rejuv = 0.
-    t_quench= 10.0
-    t_rejuv = np.min(age)
-    if np.max(MS[con_old])>lm_old:
-
-        # Set the most recent quenching;
-        #for ii in range(len(age)-1,-1,-1): # 4 to 0.01
-        for ii in range(len(age)): # 0.01 to 4
-            #if (age[ii]<age_set) and (age[ii]>np.min(age)) and SF[ii,2]<SFMS_50[ii]-delMS:
-            if (age[ii]<age_set) and (age[ii]>np.min(age)) and SF[ii,2]<SFMS_50[ii]-delMS and f_rejuv == 0:
-                if ii>0 and f_quen==0:
-                    t_rejuv = age[ii-1]
-                f_quen  = 1
-            if f_quen==1 and SF[ii,2]>SFMS_50[ii]-delMS:
-                    t_quench = age[ii-1] # Time the most recent quenching has set.
-                    f_rejuv  = 1
-                    break
-
-    #print(f_rejuv,t_quench,t_rejuv)
-    return f_rejuv,t_quench,t_rejuv
-
-
-def get_SFMS(red,age,mass,IMF=1):
-    '''
-    Gets SFMS at age ago from z=red.
-
-    Parameters
-    ----------
-    red : float
-        Observed redshift
-    age : array
-        lookback time, in Gyr.
-    mass : array
-        stellar mass (array) at each age, in Msun (not logM). 
-
-    Returns
-    -------
-    SFR, in logMsun/yr.
-
-    Notes
-    -----
-    From Speagle+14 Eq28.
-    Chabrier IMF, in default
-    '''
-    from astropy.cosmology import WMAP9
-    cosmo = WMAP9
-
-    CIMF = 0
-    if IMF == 0:#
-        CIMF = 0.23
-        print('SFMS is shifted to Salpeter IMF.')
-    elif IMF == 2:
-        CIMF = 0.04
-        print('SFMS is shifted to Kroupa IMF.')
-
-    x = np.log10(mass) - CIMF #np.arange(6,13,0.1)
-    tz = cosmo.age(z=red).value - age # in Gyr
-    y1 = (0.84 - 0.026*tz) * x - (6.51 - 0.11*tz) # in log Msun/yr
-    con = (y1<=0)
-    y1[con] = -10
-    return y1
-
-
-def fit_spec(lm, fobs, eobs, ftmp):
-    '''
-    Fitting. (Not sure)
-    '''
-    s = np.sum(fobs*ftmp/eobs**2)/np.sum(ftmp**2/eobs**2)
-    chi2 = np.sum(((fobs-s*ftmp)/eobs)**2)
-    return chi2, s
-
-
-def fit_specphot(lm, fobs, eobs, ftmp, fbb, ebb, ltmp_bb, ftmp_bb):
-    I1   = np.sum(fobs*ftmp/eobs**2) + np.sum(fbb*ftmp_bb/ebb**2)
-    I2   = np.sum(ftmp**2/eobs**2)   + np.sum(ftmp_bb**2/ebb**2)
-    s    = I1/I2
-    chi2 = np.sum(((fobs-s*ftmp)/eobs)**2) + np.sum(((fbb-s*ftmp_bb)/ebb)**2)
-    return chi2, s
-
-
-def SFH_del(t0, tau, A, tt=None, minsfr = 1e-10):
-    '''
-    SFH
-    '''
-    try:
-        if tt == None:
-            tt = np.arange(0.,10,0.1)
-    except:
-        pass
-    sfr = np.zeros(len(tt), dtype='float')+minsfr
-    sfr[:] = A * (tt[:]-t0) * np.exp(-(tt[:]-t0)/tau)
-    con = (tt[:]-t0<0)
-    sfr[:][con] = minsfr
-    return sfr
-
-
-def SFH_dec(t0, tau, A, tt=None, minsfr = 1e-10):
-    '''
-    '''
-    try:
-        if tt == None:
-            tt = np.arange(0.,10,0.1)
-    except:
-        pass
-    sfr = np.zeros(len(tt), dtype='float')+minsfr
-    sfr[:] = A * (np.exp(-(tt[:]-t0)/tau))
-    con = (tt[:]-t0<0)
-    sfr[:][con] = minsfr
-    return sfr
-
-
-def SFH_cons(t0, tau, A, tt=None, minsfr = 1e-10):
-    '''
-    '''
-    try:
-        if tt == None:
-            tt = np.arange(0.,10,0.1)
-    except:
-        pass
-    sfr = np.zeros(len(tt), dtype='float')+minsfr
-    sfr[:] = A #* (np.exp(-(tt[:]-t0)/tau))
-    con = (tt[:]<t0) | (tt[:]>tau)
-    sfr[:][con] = minsfr
-    return sfr
-
-
-def get_Fint(lmtmp, ftmp, lmin=1400, lmax=1500):
-    '''
-    Parameters
-    ----------
-    lmtmp : 
-        Rest frame wave (AA)
-    ftmp :
-        Fnu ()
-
-    Returns
-    -------
-    integrated flux.
-    '''
-    
-    con = (lmtmp>lmin) & (lmtmp<lmax) & (ftmp>0)
-    if len(lmtmp[con])>0:
-        lamS,spec = lmtmp[con], ftmp[con] # Two columns with wavelength and flux density
-        # Equivalent to the following;
-        I1 = simps(spec*lamS*1.,lamS)
-        I2 = simps(lamS*1.,lamS)
-        fnu = I1
-    return fnu
-
-
-def get_Fuv(lmtmp, ftmp, lmin=1400, lmax=1500):
-    '''
-    Purpose
-    -------
-    Get RF UV (or any wavelength) flux density.
-
-    Parameters
-    ----------
-    lmtmp : 
-        Rest-frame wavelength, in AA.
-    ftmp : 
-        Fnu
-    
-    Returns
-    -------
-    Flux density estimated over lmin:lmax. Not integrated sum.
-    '''
-    con = (lmtmp>lmin) & (lmtmp<lmax) & (ftmp>0)
-    if len(lmtmp[con])>0:
-        lamS,spec = lmtmp[con], ftmp[con] # Two columns with wavelength and flux density
-        I1 = simps(spec*lamS*1.,lamS)
-        I2 = simps(lamS*1.,lamS)
-        fnu = I1/I2
-    else:
-        fnu = None
-    return fnu
-
-
-def data_int(lmobs, lmtmp, ftmp):
-    '''
-
-    Parameters
-    ----------
-    lmobs : 
-        Observed wavelength.
-    lmtmp, ftmp: 
-        Those to be interpolated.
-    '''
-    ftmp_int  = np.interp(lmobs,lmtmp,ftmp) # Interpolate model flux to observed wavelength axis.
-    return ftmp_int
-
-
-def fnutonu(fnu, m0set=25.0, m0input=-48.6):
-    '''
-    Converts from Fnu (cgs) to Fnu (m0=m0set)
-    
-    Parameters
-    ----------
-    fnu : float array
-        flux in cgs, with magnitude zero point of m0input.
-    m0set : float
-        Target mag zero point.
-    m0input : float
-        Original value for magzp. If erg/s/cm2/Hz, -48.6.
-    '''
-    Ctmp = 10**((m0set-m0input)/2.5)
-    fnu_new = fnu * Ctmp
-    return fnu_new
-
-
-def flamtonu(lam, flam, m0set=25.0):
-    '''
-    Converts from Flam to Fnu, with mag zeropoint of m0set.
-    
-    '''
-    Ctmp = lam**2/c * 10**((48.6+m0set)/2.5) #/ delx_org
-    fnu  = flam * Ctmp
-    return fnu
-
-
-def fnutolam(lam, fnu, m0set=25.0, m0=-48.6):
-    '''
-    Converts from Fnu to Flam, from mag zeropoint of m0set (to -48.6).
-
-    Parameters
-    ----------
-    m0set : float
-        current magzp.
-    m0 : float
-        target magzp. The default, -48.6, is for flam (erg/s/cm2/lambda).
-    '''
-    Ctmp = lam**2/c * 10**((m0set-m0)/2.5)
-    flam = fnu / Ctmp
-    return flam
-
-
-def delta(x,A):
-    '''
-    '''
-    yy = np.zeros(len(x),float)
-    iix = np.argmin(np.abs(x))
-    if len(x)%2 == 0:
-        logger= logging.getLogger( __name__ )
-        print(logger)
-        print('Input array has an even number.')
-        yy[iix] = A/2.
-        yy[iix+1] = A/2.
-    else:
-        yy[iix] = A
-    return yy
-
-
-def gauss(x,A,sig):
-    return A * np.exp(-0.5*x**2/sig**2)
-
-
-def moffat(xx, A, x0, gamma, alp):
-    yy = A * (1. + (xx-x0)**2/gamma**2)**(-alp)
-    return yy
-
-
-def get_fit(x, y, xer, yer, nsfh:str = 'Del.'):
-    '''
-    '''
-    from lmfit import Model, Parameters, minimize, fit_report, Minimizer
-
-    fit_params = Parameters()
-    #fit_params.add('t0', value=1., min=0, max=np.max(tt))
-    fit_params.add('t0', value=.5, min=0, max=14)
-    fit_params.add('tau', value=.1, min=0, max=100)
-    fit_params.add('A', value=1, min=0, max=5000)
-
-    def residual_tmp(pars):
-        vals = pars.valuesdict()
-        t0_tmp, tau_tmp, A_tmp = vals['t0'],vals['tau'],vals['A']
-
-        if nsfh == 'Del.':
-            model = SFH_del(t0_tmp, tau_tmp, A_tmp, tt=x)
-        elif nsfh == 'Decl.':
-            model = SFH_dec(t0_tmp, tau_tmp, A_tmp, tt=x)
-        elif nsfh == 'Cons.':
-            model = SFH_cons(t0_tmp, tau_tmp, A_tmp, tt=x)
-
-        con = (model>0)
-        resid = (np.log10(model[con]) - y[con]) / yer[con]
-        return resid
-
-    out = minimize(residual_tmp, fit_params, method='powell')
-    print(fit_report(out))
-
-    t0 = out.params['t0'].value
-    tau = out.params['tau'].value
-    A = out.params['A'].value
-    param = [t0, tau, A]
-
-    keys = fit_report(out).split('\n')
-    for key in keys:
-        if key[4:7] == 'chi':
-            skey = key.split(' ')
-            csq  = float(skey[14])
-        if key[4:7] == 'red':
-            skey = key.split(' ')
-            rcsq = float(skey[7])
-
-    return param, rcsq
-
-
-def savecpkl(data, cpklfile, verbose=True):
-    """
-    Save data into cpklfile.
-    """
-    if verbose: print(' => Saving data to cpklfile '+cpklfile)
-    f = open(cpklfile,'wb')
-    cPickle.dump(data, f, 2)
-    f.close()
-
-
-def apply_dust(yy, xx, nr, Av, dust_model=0):
-	'''
-	xx : float array
-		RF Wavelength
-	'''
-	if dust_model == 0:
-		yyd, xxd, nrd = dust_calz(xx, yy, Av, nr)
-	elif dust_model == 1:
-		yyd, xxd, nrd = dust_mw(xx, yy, Av, nr)
-	elif dust_model == 2: # LMC
-		yyd, xxd, nrd = dust_gen(xx, yy, Av, nr, Rv=4.05, gamma=-0.06, Eb=2.8)
-	elif dust_model == 3: # SMC
-		yyd, xxd, nrd = dust_gen(xx, yy, Av, nr, Rv=4.05, gamma=-0.42, Eb=0.0)
-	elif dust_model == 4: # Kriek&Conroy with gamma=-0.2
-		yyd, xxd, nrd = dust_kc(xx, yy, Av, nr, Rv=4.05, gamma=-0.2)
-	else:
-		yyd, xxd, nrd = dust_calz(xx, yy, Av, nr)
-	return yyd, xxd, nrd
-
-
-def dust_gen(lm, fl, Av, nr, Rv=4.05, gamma=-0.05, Eb=3.0, lmlimu=3.115, lmv=5000/10000, f_Alam=False):
-    '''
-    For general purpose (Noll+09).
-    This function is much better than previous, but is hard to impliment for the current version.
-    A difference from dust_gen is Eb is defined as a function of gamma.
-
-    Parameters
-    ----------
-    lm : float array
-        wavelength, at RF.
-    fl : float array
-        fnu
-    Av : float
-        in mag
-    nr : int array
-        index, to be used for sorting.
-    Rv : 
-        from Calzetti+00
-    gamma :
-        gamma.
-    Eb:
-        Eb
-    '''
-    Kl = np.zeros(len(lm), dtype='float')
-
-    lmm  = lm/10000. # in micron
-    con1 = (lmm<=0.63)
-    con2 = (lmm>0.63)  & (lmm<=lmlimu)
-    con3 = (lmm>lmlimu)
-
-    Kl1 = (2.659 * (-2.156 + 1.509/lmm[con1] - 0.198/lmm[con1]**2 + 0.011/lmm[con1]**3) + Rv)
-    Kl2 = (2.659 * (-1.857 + 1.040/lmm[con2]) + Rv)
-    Kl3 = (2.659 * (-1.857 + 1.040/lmlimu + lmm[con3] * 0) + Rv)
-
-    #nr0 = nr[con0]
-    nr1 = nr[con1]
-    nr2 = nr[con2]
-    nr3 = nr[con3]
-
-    #lmm0 = lmm[con0]
-    lmm1 = lmm[con1]
-    lmm2 = lmm[con2]
-    lmm3 = lmm[con3]
-
-    #fl0 = fl[con0]
-    fl1 = fl[con1]
-    fl2 = fl[con2]
-    fl3 = fl[con3]
-
-    Kl   = np.concatenate([Kl1,Kl2,Kl3])
-    nrd  = np.concatenate([nr1,nr2,nr3])
-    lmmc = np.concatenate([lmm1,lmm2,lmm3])
-    flc  = np.concatenate([fl1,fl2,fl3])
-
-    # Bump;
-    lm0   = 2175 / 10000 # micron m by Noll et al. (2009)
-    dellm = 350 / 10000 # micron m by Noll et al. (2009)
-    D = Eb * (lmmc * dellm)**2 / ((lmmc**2-lm0**2)**2+(lmmc * dellm)**2)
-    #
-    Alam   = Av / Rv * (Kl + D) * (lmmc / lmv)**gamma
-    fl_cor = flc[:] * 10**(-0.4*Alam[:])
-
-    if f_Alam:
-        return fl_cor, lmmc*10000., nrd, Alam
-    else:
-        return fl_cor, lmmc*10000., nrd
-
-
-def dust_kc(lm, fl, Av, nr, Rv=4.05, gamma=0, lmlimu=3.115, lmv=5000/10000, f_Alam=False):
-    '''
-    Dust model by Kriek&Conroy13
-    
-    Parameters
-    ----------
-    lm : float array
-        RF Wavelength.
-    fl : float array
-        in fnu
-    Av : float
-        in mag
-    nr : int array
-        index, to be used for sorting.
-    Rv : float
-        from Calzetti+00
-    gamma : float
-        See Eq.1
-    '''
-    Kl = np.zeros(len(lm), dtype='float')
-
-    lmm  = lm/10000. # in micron
-    con1 = (lmm<=0.63)
-    con2 = (lmm>0.63)  & (lmm<=lmlimu)
-    con3 = (lmm>lmlimu)
-
-    Kl1 = (2.659 * (-2.156 + 1.509/lmm[con1] - 0.198/lmm[con1]**2 + 0.011/lmm[con1]**3) + Rv)
-    Kl2 = (2.659 * (-1.857 + 1.040/lmm[con2]) + Rv)
-    Kl3 = (2.659 * (-1.857 + 1.040/lmlimu + lmm[con3] * 0) + Rv)
-
-    #nr0 = nr[con0]
-    nr1 = nr[con1]
-    nr2 = nr[con2]
-    nr3 = nr[con3]
-
-    #lmm0 = lmm[con0]
-    lmm1 = lmm[con1]
-    lmm2 = lmm[con2]
-    lmm3 = lmm[con3]
-
-    #fl0 = fl[con0]
-    fl1 = fl[con1]
-    fl2 = fl[con2]
-    fl3 = fl[con3]
-
-    Kl   = np.concatenate([Kl1,Kl2,Kl3])
-    nrd  = np.concatenate([nr1,nr2,nr3])
-    lmmc = np.concatenate([lmm1,lmm2,lmm3])
-    flc  = np.concatenate([fl1,fl2,fl3])
-
-    Eb = 0.85 - 1.9 * gamma
-    #print('Eb is %.2f'%(Eb))
-
-    # Bump;
-    lm0   = 2175 / 10000 # micron m by Noll et al. (2009)
-    dellm = 350 / 10000 # micron m by Noll et al. (2009)
-    D = Eb * (lmmc * dellm)**2 / ((lmmc**2-lm0**2)**2+(lmmc * dellm)**2)
-    #
-    Alam   = Av / Rv * (Kl + D) * (lmmc / lmv)**gamma
-    fl_cor = flc[:] * 10**(-0.4*Alam[:])
-
-    if f_Alam:
-        return fl_cor, lmmc*10000., nrd, Alam
-    else:
-        return fl_cor, lmmc*10000., nrd
-
-
-def dust_calz(lm, fl, Av:float, nr, Rv:float = 4.05, lmlimu:float = 3.115, f_Alam:bool = False):
-    '''
-    Parameters
-    ----------
-    lm : float array
-        wavelength, at RF.
-    fl : float array
-        fnu
-    Av : float
-        in mag
-    nr : int array
-        index, to be used for sorting.
-    Rv : float
-        from Calzetti+00
-    lmlimu : float
-        Upper limit. 2.2 in Calz+00
-    '''
-    Kl = lm[:]*0 #np.zeros(len(lm), dtype='float')
-    nrd = lm[:]*0 #np.zeros(len(lm), dtype='float')
-    lmmc = lm[:]*0 #np.zeros(len(lm), dtype='float')
-    flc = lm[:]*0 #np.zeros(len(lm), dtype='float')
-
-    lmm = lm/10000. # in micron
-    con1 = (lmm<=0.63)
-    con2 = (lmm>0.63)  & (lmm<=lmlimu)
-    con3 = (lmm>lmlimu)
-
-    Kl[con1] = (2.659 * (-2.156 + 1.509/lmm[con1] - 0.198/lmm[con1]**2 + 0.011/lmm[con1]**3) + Rv)
-    Kl[con2] = (2.659 * (-1.857 + 1.040/lmm[con2]) + Rv)
-    Kl[con3] = (2.659 * (-1.857 + 1.040/lmlimu + lmm[con3] * 0) + Rv)
-
-    nrd[con1] = nr[con1]
-    nrd[con2] = nr[con2]
-    nrd[con3] = nr[con3]
-
-    lmmc[con1] = lmm[con1]
-    lmmc[con2] = lmm[con2]
-    lmmc[con3] = lmm[con3]
-
-    flc[con1] = fl[con1]
-    flc[con2] = fl[con2]
-    flc[con3] = fl[con3]
-
-    Alam = Kl * Av / Rv
-    fl_cor = flc[:] * 10**(-0.4*Alam[:])
-
-    if f_Alam:
-        return fl_cor, lmmc*10000., nrd, Alam
-    else:
-        return fl_cor, lmmc*10000., nrd
-
-
-def dust_mw(lm, fl, Av, nr, Rv=3.1, f_Alam=False):
-    '''
-    Parameters
-    ----------
-    lm : float array
-        wavelength, at RF, in AA.
-    fl : float array
-        fnu
-    Av : float
-        mag
-    nr : int array
-        index, to be used for sorting.
-    Rv : float
-        3.1 for MW.
-    '''
-    Kl = np.zeros(len(lm), dtype='float')
-
-    lmm  = lm/10000. # into micron
-    xx   = 1./lmm
-
-    con0 = (xx>=8.0)
-    con1 = (xx>=5.9) & (xx<8.0)
-    con2 = (xx>=3.3) & (xx<5.9)
-    con3 = (xx>=1.1) & (xx<3.3)
-    con4 = (xx<1.1)
-
-    nr0 = nr[con0]
-    nr1 = nr[con1]
-    nr2 = nr[con2]
-    nr3 = nr[con3]
-    nr4 = nr[con4]
-
-    lmm0 = lmm[con0]
-    lmm1 = lmm[con1]
-    lmm2 = lmm[con2]
-    lmm3 = lmm[con3]
-    lmm4 = lmm[con4]
-
-    fl0 = fl[con0]
-    fl1 = fl[con1]
-    fl2 = fl[con2]
-    fl3 = fl[con3]
-    fl4 = fl[con4]
-
-    ax4 =  0.574 * (1./lmm4)**1.61
-    bx4 = -0.527 * (1./lmm4)**1.61
-
-    yy  = ((1./lmm3) - 1.82)
-    ax3 = 1. + 0.17699 * yy - 0.50447 * yy**2 - 0.02427 * yy**3 + 0.72085 * yy**4\
-          + 0.01979 * yy**5 - 0.77530 * yy**6 + 0.32999 * yy**7
-    bx3 = 1.41338 * yy + 2.28305 * yy**2 + 1.07233 * yy**3 - 5.38434 * yy**4\
-          - 0.62251 * yy**5 + 5.30260 * yy**6 - 2.09002 * yy**7
-
-    Fax2 = Fbx2 = lmm2 * 0
-    ax2  = 1.752 - 0.316 * (1./lmm2) - 0.104/(((1./lmm2)-4.67)**2+0.341) + Fax2
-    bx2  = -3.090 + 1.825 * (1./lmm2) + 1.206/(((1./lmm2)-4.62)**2+0.263) + Fbx2
-
-    Fax1 = -0.04473 * ((1./lmm1) - 5.9)**2 - 0.009779 * ((1./lmm1) - 5.9)**3
-    Fbx1 = 0.2130 * ((1./lmm1) - 5.9)**2 + 0.1207 * ((1./lmm1) - 5.9)**3
-    ax1  = 1.752 - 0.316 * (1./lmm1) - 0.104/(((1./lmm1)-4.67)**2+0.341) + Fax1
-    bx1  = -3.090 + 1.825 * (1./lmm1) + 1.206/(((1./lmm1)-4.62)**2+0.263) + Fbx1
-
-    Fax0 = -0.04473 * ((1./lmm0) - 5.9)**2 - 0.009779 * ((1./lmm0) - 5.9)**3
-    Fbx0 = 0.2130 * ((1./lmm0) - 5.9)**2 + 0.1207 * ((1./lmm0) - 5.9)**3
-    ax0  = 1.752 - 0.316 * (1./lmm0) - 0.104/(((1./lmm0)-4.67)**2+0.341) + Fax0
-    bx0  = -3.090 + 1.825 * (1./lmm0) + 1.206/(((1./lmm0)-4.62)**2+0.263) + Fbx0
-
-    nrd  = np.concatenate([nr0,nr1,nr2,nr3,nr4])
-    lmmc = np.concatenate([lmm0,lmm1,lmm2,lmm3,lmm4])
-    flc  = np.concatenate([fl0,fl1,fl2,fl3,fl4])
-    ax   = np.concatenate([ax0,ax1,ax2,ax3,ax4])
-    bx   = np.concatenate([bx0,bx1,bx2,bx3,bx4])
-
-    Alam   = Av * (ax + bx / Rv)
-
-    if False:
-        import matplotlib.pyplot as plt
-        plt.plot(1/lmmc,Alam/Av,linestyle='-')
-        plt.xlim(0,3)
-        plt.ylim(0,2.0)
-        plt.show()
-
-    fl_cor = flc[:] * 10**(-0.4*Alam[:])
-
-    if f_Alam:
-        return fl_cor, lmmc*10000., nrd, Alam
-    else:
-        return fl_cor, lmmc*10000., nrd
-
-
-def check_line(data,wave,wht,model):
-    '''
-    '''
-    R_grs = 50
-    dw  = 10
-    ldw = 5
-    dlw = R_grs * 2
-    lsig = 1.5 # significance of lines.
-    ii0 = 0 + dw
-    ii9 = len(data) - dw
-    wd  = data * wht
-    er  = 1./np.sqrt(wht)
-    wht2 = wht
-
-    for ii in range(ii0, ii9, 1):
-        concont = (((wave>wave[ii]-dw*R_grs) & (wave<wave[ii]-(dw-ldw)*R_grs)) \
-                   | ((wave<wave[ii]+dw*R_grs) & ((wave>wave[ii]+(dw-ldw)*R_grs))))
-
-        xcont = wave[concont]
-        ycont = data[concont]
-        wycont = wht[concont]
-
-        if len(xcont)>5:
-            try:
-                z = np.polyfit(xcont, ycont, 1, w=wycont)
-                p = np.poly1d(z)
-                fconttmp  = p(wave[ii])
-                fconttmp1 = p(wave[ii-1])
-                fconttmp2 = p(wave[ii+1])
-
-                if data[ii] > er[ii]*lsig + fconttmp and data[ii-1] > er[ii-1]*lsig + fconttmp1 and data[ii+1] > er[ii+1]*lsig + fconttmp2:
-                    #print wave[ii]/(1.+zgal), dlw/(1+zgal)
-                    for jj in range(len(LW)):
-                        #print wave[ii],  LW[jj]*(1.+zgal) - dlw, LW[jj]*(1.+zgal) + dlw, LN[jj]
-                        if wave[ii] > LW[jj]*(1.+zgal) - dlw\
-                           and wave[ii] < LW[jj]*(1.+zgal) + dlw:
-                            wht2[ii-dw:ii+dw] *= 0
-                            fLW[jj] = 1
-                            print(p, LN[jj], fconttmp, data[ii], wave[ii]/(1.+zgal))
-                elif wht2[ii] != 0:
-                    wht2[ii] = wht[ii]
-
-            except Exception:
-                print('Error in Line Check.')
-                pass
-
-    return wht2
-
-
-def filconv_cen(band0, l0, f0, DIR='FILT/'):
-    '''
-    Convolution of templates with filter response curves.
-
-    Parameters
-    ----------
-    '''
-
-    fnu  = np.zeros(len(band0), dtype='float')
-    lcen = np.zeros(len(band0), dtype='float')
-    for ii in range(len(band0)):
-        fd = np.loadtxt(DIR + '%s.fil'%str(band0[ii]), comments='#')
-        lfil = fd[:,1]
-        ffil = fd[:,2]
-
-        lmin  = np.min(lfil)
-        lmax = np.max(lfil)
-        imin = 0
-        imax = 0
-
-        lcen[ii] = np.sum(lfil*ffil)/np.sum(ffil)
-
-        lamS,spec = l0, f0 #Two columns with wavelength and flux density
-        lamF,filt = lfil, ffil #Two columns with wavelength and response in the range [0,1]
-        #filt_int = np.interp(lamS,lamF,filt)  #Interpolate Filter to common(spectra) wavelength axis
-        fint = interpolate.interp1d(lamF, filt, kind='nearest', fill_value="extrapolate")
-        filt_int = fint(lamS)
-        filtSpec = filt_int * spec #Calculate throughput
-        wht = 1. #/(er1[con_rf])**2
-
-        if len(lamS)>0: #./3*len(x0[con_org]): # Can be affect results.
-            I1  = simps(spec/lamS**2*c*filt_int*lamS,lamS)   #Denominator for Fnu
-            I2  = simps(filt_int/lamS,lamS)                  #Numerator
-            fnu[ii] = I1/I2/c         #Average flux density
-        else:
-            I1  = 0
-            I2  = 0
-            fnu[ii] = 0
-
-    return lcen, fnu
-
-
-def filconv_fast(filts, band, l0, f0, fw=False):
-    '''
-    Parameters
-    ----------
-    filts, band : 
-        From MB.filts and MB.band, respectively.
-    f0: 
-        Flux for spectrum, in fnu
-    l0: 
-        Wavelength for spectrum, in AA (that matches filter response curve's.)
-
-    '''
-    fnu  = np.zeros(len(filts), dtype='float')
-    lcen = np.zeros(len(filts), dtype='float')
-    if fw:
-        fwhm = np.zeros(len(filts[:]), dtype='float')
-
-    for ii in range(len(filts[:])):
-        lfil = band['%s_lam'%(filts[ii])]
-        ffil = band['%s_res'%(filts[ii])]
-
-        if fw:
-            ffil_cum = np.cumsum(ffil)
-            ffil_cum/= ffil_cum.max()
-            con      = (ffil_cum>0.05) & (ffil_cum<0.95)
-            fwhm[ii] = np.max(lfil[con]) - np.min(lfil[con])
-
-        lmin  = np.min(lfil)
-        lmax  = np.max(lfil)
-        imin  = 0
-        imax  = 0
-
-        con = (l0>lmin) & (l0<lmax) #& (f0>0)
-        lcen[ii] = np.sum(lfil*ffil)/np.sum(ffil)
-        if len(l0[con])>1:
-            lamS,spec = l0[con], f0[con]                     # Two columns with wavelength and flux density
-            lamF,filt = lfil, ffil                 # Two columns with wavelength and response in the range [0,1]
-            #filt_int = np.interp(lamS,lamF,filt)  # Interpolate Filter to the same wavelength axis of l0, or lamS
-            fint = interpolate.interp1d(lamF, filt, kind='nearest', fill_value="extrapolate")
-            filt_int = fint(lamS)
-
-            wht = 1.
-
-
-            # This does not work sometimes;
-            delS = lamS[1]-lamS[0]
-            I1 = np.sum(spec/lamS**2*c*filt_int*lamS*delS)   #Denominator for Fnu
-            I2 = np.sum(filt_int/lamS*delS)                  #Numerator
-            if I2>0:
-                fnu[ii] = I1/I2/c #Average flux density
-            else:
-                fnu[ii] = 0
-        else:
-            fnu[ii] = 0
-
-    if fw:
-        return lcen, fnu, fwhm
-    else:
-        return lcen, fnu
-
-
-def filconv(band0, l0, f0, DIR, fw=False, f_regist=True, MB=None):
-    '''
-    Parameters
-    ----------
-    f0 : float array
-        Flux for spectrum, in fnu
-    l0 : float array
-        Wavelength for spectrum, in AA (that matches filter response curve's.)
-    f_regist : bool
-        If True, read filter response curves and register those to MB.
-    '''
-    if MB==None:
-        f_regist = True
-    if f_regist:
-        lfil_lib = {}
-        ffil_lib = {}
-
-    fnu = np.zeros_like(band0, dtype=float)
-    lcen = np.zeros_like(band0, dtype=float)
-    if fw:
-        fwhm = np.zeros_like(band0, dtype=float)
-    
-    for ii in range(len(band0)):
-        if not f_regist:
-            try:
-                lfil = MB.lfil_lib['%s'%str(band0[ii])]
-                ffil = MB.ffil_lib['%s'%str(band0[ii])]
-                lmin = MB.lfil_lib['%s_lmin'%str(band0[ii])]
-                lmax = MB.lfil_lib['%s_lmax'%str(band0[ii])]
-                lcen[ii] = MB.lfil_lib['%s_lcen'%str(band0[ii])]
-                if fw:
-                    fwhm = MB.filt_fwhm
-            except:
-                f_regist = True
-                lfil_lib = {}
-                ffil_lib = {}
-                
-        if f_regist:
-            fd = np.loadtxt(DIR + '%s.fil'%str(band0[ii]), comments='#')
-            lfil = fd[:,1]
-            ffil = fd[:,2]
-            ffil /= np.max(ffil)
-            lmin = np.min(lfil)
-            lmax = np.max(lfil)
-
-            if fw:
-                ffil_cum = np.cumsum(ffil)
-                ffil_cum/= ffil_cum.max()
-                con = (ffil_cum>0.05) & (ffil_cum<0.95)
-                fwhm[ii] = np.max(lfil[con]) - np.min(lfil[con])
-
-            con = (l0>lmin) & (l0<lmax)
-            delw = np.nanmin(np.diff(l0))
-            if delw > np.nanmin(np.diff(ffil)):
-                lfil_new = np.arange(lmin,lmax,delw)
-                fint = interpolate.interp1d(lfil, ffil, kind='nearest', fill_value="extrapolate")
-                ffil = fint(lfil_new)
-                lfil = lfil_new
-
-            lfil_lib['%s'%str(band0[ii])] = lfil
-            ffil_lib['%s'%str(band0[ii])] = ffil
-
-            lcen[ii] = np.sum(lfil*ffil)/np.sum(ffil)
-            lfil_lib['%s_lmin'%str(band0[ii])] = lmin
-            lfil_lib['%s_lmax'%str(band0[ii])] = lmax
-            lfil_lib['%s_lcen'%str(band0[ii])] = lcen[ii]
-
-        con = (l0>lmin) & (l0<lmax) #& (f0>0)
-        if len(l0[con])>1:
-            fint = interp1d(lfil, ffil, kind='nearest', fill_value="extrapolate")
-            filt_int = fint(l0[con])
-
-            # This does not work sometimes;
-            #delS = l0[con][1]-l0[con][0]
-            I1 = np.sum(f0[con]/l0[con]**2*c*filt_int*l0[con])
-            I2 = np.sum(filt_int/l0[con])
-            if I2>0:
-                fnu[ii] = I1/I2/c
-            else:
-                fnu[ii] = 0
-        else:
-            fnu[ii] = 0
-
-    if MB != None and f_regist:
-        MB.lfil_lib = lfil_lib
-        MB.ffil_lib = ffil_lib
-        if fw:
-            MB.filt_fwhm = fwhm
-        
-    if fw:
-        return lcen, fnu, fwhm
-    else:
-        return lcen, fnu
-
-
-"""
-def filconv(band0, l0, f0, DIR, fw=False, f_regist=True, MB=None):
-    '''
-    This one does not improve much.
-    
-    Parameters
-    ----------
-    f0 : float array
-        Flux for spectrum, in fnu
-    l0 : float array
-        Wavelength for spectrum, in AA (that matches filter response curve's.)
-    f_regist : bool
-        If True, read filter response curves and register those to MB.
-    '''
-    if MB==None:
-        f_regist = True
-    if f_regist:
-        lfil_lib = {}
-        ffil_lib = {}
-
-    if fw:
-        fwhm = np.zeros_like(band0, dtype=float)
-
-    if not f_regist:
-        try:
-            ffil_lib = MB.ffil_lib
-            if fw:
-                fwhm = MB.filt_fwhm
-        except:
-            f_regist = True
-            ffil_lib = {}
-            # lfils = np.zeros_like(band0, dtype=float)
-            # ffils = np.zeros_like(band0, dtype=float)
-            # lmins = np.zeros_like(band0, dtype=float)
-            # lmaxs = np.zeros_like(band0, dtype=float)
-
-    if f_regist:
-        lcen = np.zeros_like(band0, dtype=float)
-        for ii in range(len(band0)):
-            ffil_lib['%s'%band0[ii]] = {}
-            
-            fd = np.loadtxt(DIR + '%s.fil'%str(band0[ii]), comments='#')
-            lfil = fd[:,1]
-            ffil = fd[:,2]
-            ffil /= np.max(ffil)
-            lmin = np.min(lfil)
-            lmax = np.max(lfil)
-
-            if fw:
-                ffil_cum = np.cumsum(ffil)
-                ffil_cum/= ffil_cum.max()
-                con = (ffil_cum>0.05) & (ffil_cum<0.95)
-                fwhm[ii] = np.max(lfil[con]) - np.min(lfil[con])
-
-            con = (l0>lmin) & (l0<lmax)
-            delw = np.nanmin(np.diff(l0))
-            if delw > np.nanmin(np.diff(ffil)):
-                lfil_new = np.arange(lmin,lmax,delw)
-                fint = interpolate.interp1d(lfil, ffil, kind='nearest', fill_value="extrapolate")
-                ffil = fint(lfil_new)
-                lfil = lfil_new
-
-            ffil_lib['%s'%band0[ii]]['lfil'] = lfil
-            ffil_lib['%s'%band0[ii]]['ffil'] = ffil
-            ffil_lib['%s'%band0[ii]]['lmin'] = lmin
-            ffil_lib['%s'%band0[ii]]['lmax'] = lmax
-            lcen[ii] = np.sum(lfil*ffil)/np.sum(ffil)
-        
-        ffil_lib['lcen'] = lcen
-
-    fnu = np.zeros_like(band0, dtype=float)
-    for ii in range(len(band0)):
-        con = (l0>ffil_lib['%s'%band0[ii]]['lmin']) & (l0<ffil_lib['%s'%band0[ii]]['lmax']) #& (f0>0)
-
-        if len(l0[con])>1:
-            fint = interp1d(ffil_lib['%s'%band0[ii]]['lfil'], ffil_lib['%s'%band0[ii]]['ffil'], kind='nearest', fill_value="extrapolate")
-            filt_int = fint(l0[con])
-
-            # This does not work sometimes;
-            I1 = np.sum(f0[con]/l0[con]**2*c*filt_int*l0[con])
-            I2 = np.sum(filt_int/l0[con])
-            if I2>0:
-                fnu[ii] = I1/I2/c
-            else:
-                fnu[ii] = 0
-        else:
-            fnu[ii] = 0
-
-    if MB != None and f_regist:
-        MB.ffil_lib = ffil_lib
-        if fw:
-            MB.filt_fwhm = fwhm
-        
-    if fw:
-        return ffil_lib['lcen'], fnu, fwhm
-    else:
-        return ffil_lib['lcen'], fnu
-"""
-
-
-def fil_fwhm(band0, DIR):
-    '''
-    Parameters
-    ----------
-    f0 :
-        in fnu
-    '''
-    fwhm = np.zeros(len(band0), dtype='float')
-    for ii in range(len(band0)):
-        fd = np.loadtxt(DIR + band0[ii] + '.fil', comments='#')
-        lfil = fd[:,1]
-        ffil = fd[:,2]
-
-        fsum = np.sum(ffil)
-        fcum = np.zeros(len(ffil), dtype='float')
-        lam0,lam1 = 0,0
-
-        for jj in range(len(ffil)):
-            fcum[jj] = np.sum(ffil[:jj])/fsum
-            if lam0 == 0 and fcum[jj]>0.05:
-                lam0 = lfil[jj]
-            if lam1 == 0 and fcum[jj]>0.95:
-                lam1 = lfil[jj]
-
-        fwhm[ii] = lam1 - lam0
-
-    return fwhm
-
-
-def calc_Dn4(x0, y0, z0):
-    con1 = (x0/(1+z0)>3750) & (x0/(1+z0)<3950)
-    con2 = (x0/(1+z0)>4050) & (x0/(1+z0)<4250)
-    D41  = np.average(y0[con1])
-    D42  = np.average(y0[con2])
-    if D41>0 and D42>0:
-        D4 = D42/D41
-        return D4
-    else:
-        return -99
-
-
-def gaus(x,a,x0,sigma):
-    return a*exp(-(x-x0)**2/(2*sigma**2))
-
-
-def detect_line(xcont, ycont, wycont, zgal):
-    ################
-    # Line library
-    ################
-    LN = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3', 'Halpha', 'S2L', 'S2H']
-    LW = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4983, 6563, 6717, 6731]
-    fLW = np.zeros(len(LW), dtype='int') # flag.
-
-    R_grs = 50
-    dw  = 5
-    ldw = 5
-    dlw = R_grs * 2
-    lsig = 3 # significance of lines.
-    er   = 1./np.sqrt(wycont)
-
-    con = (xcont<20000)
-    z = np.polyfit(xcont[con], ycont[con], 5, w=wycont[con])
-    p = np.poly1d(z)
-    ypoly = p(xcont)
-
-    wht2   = wycont
-    flag_l = 0
-
-    for ii in range(len(xcont)):
-        if ycont[ii] > er[ii]*lsig + ypoly[ii] and ycont[ii-1] > er[ii-1]*lsig + ypoly[ii-1] and ycont[ii+1] > er[ii+1]*lsig + ypoly[ii+1] and wht2[ii]:
-            for jj in range(len(LW)):
-                if xcont[ii]/(1.+zgal) > LW[jj] -  dw and xcont[ii]/(1.+zgal) < LW[jj] +  dw:
-                    wht2[ii-dw:ii+dw] *= 0
-                    flag_l  = 1
-                    fLW[jj] = 1
-
-    return wht2,flag_l
-
-
-def check_line_cz(ycont,xcont,wycont,model,zgal):
-    er = 1./np.sqrt(wycont)
-    try:
-        wht2, flag_l = detect_line(xcont, ycont, wycont, zgal)
-        if flag_l == 1:
-            wycont = wht2
-            wht2, flag_l = detect_line(xcont, ycont, wycont,zgal)
-    except Exception:
-        wht2 = wycont
-        pass
-
-    z = np.polyfit(xcont, ycont, 5, w=wht2)
-    p = np.poly1d(z)
-    ypoly = p(xcont)
-
-    return wht2, ypoly
-
-
-def check_line_cz_man(ycont,xcont,wycont,model,zgal,LW=LW0,norder=5.):
-    '''
-    Parameters
-    ----------
-    LW : 
-        List for emission lines to be masked.
-
-    Returns
-    -------
-    wht : 
-        Processed weight, where wavelength at line exists is masked.
-    ypoly : 
-        Fitted continuum flux.
-    '''
-
-    er  = 1./np.sqrt(wycont)
-    try:
-        wht2, flag_l = detect_line_man(xcont, ycont, wycont, zgal, LW, model)
-    except Exception:
-        print('Error in Line Check.')
-        wht2 = wycont
-        pass
-
-    z     = np.polyfit(xcont, ycont, norder, w=wht2)
-    p     = np.poly1d(z)
-    ypoly = p(xcont)
-
-    return wht2, ypoly
-
-def detect_line_man(xcont, ycont, wycont, zgal, LW, model):
-    ################
-    # Line library
-    ################
-    #LN = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3', 'Halpha', 'S2L', 'S2H']
-    #LW = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4983, 6563, 6717, 6731]
-    fLW = np.zeros(len(LW), dtype='int') # flag.
-
-    #R_grs = 45
-    #R_grs = 23.0
-    R_grs = (xcont[1] - xcont[0])
-    dw   = 1
-    lsig = 1.5 # significance of lines.
-    er   = 1./np.sqrt(wycont)
-
-    con   = (xcont<20000)
-    z     = np.polyfit(xcont[con], ycont[con], 5, w=wycont[con])
-    p     = np.poly1d(z)
-    ypoly = p(xcont)
-
-    wht2   = wycont
-    flag_l = 0
-
-    for ii in range(len(xcont)):
-        if 1 > 0:
-            for jj in range(len(LW)):
-                if xcont[ii]/(1.+zgal) > LW[jj] - dw*R_grs and xcont[ii]/(1.+zgal) < LW[jj] + dw*R_grs:
-                    wht2[int(ii-dw):int(ii+dw)] *= 0
-                    flag_l  = 1
-
-    return wht2,flag_l
-
-
-def check_line_man(data,xcont,wht,model,zgal,LW=LW0,lsig=1.5):
-    '''
-    Parameters
-    ----------
-    lsig : float
-        which sigma to detect lines.
-    '''
-    fLW = np.zeros(len(LW), dtype='int') # flag.
-    R_grs = (xcont[1] - xcont[0])
-    dw    = 1.
-
-    er = wht * 0
-    con_wht = (wht>0)
-    er[con_wht] = 1./np.sqrt(wht[con_wht])
-
-    wht2 = np.zeros(len(wht),'float')
-    wht2[:]= wht[:]
-    flag_l = 0
-
-    for ii in range(len(xcont)):
-        for jj in range(len(LW)):
-            if LW[jj]>0:
-                if xcont[ii]/(1.+zgal) > LW[jj] - dw*R_grs and xcont[ii]/(1.+zgal) < LW[jj] + dw*R_grs:
-                    wht2[int(ii-dw):int(ii+dw)] *= 0
-                    flag_l  = 1
-
-    return wht2
diff --git a/build/lib/gsf/function_class.py b/build/lib/gsf/function_class.py
deleted file mode 100644
index d4a20f0..0000000
--- a/build/lib/gsf/function_class.py
+++ /dev/null
@@ -1,1179 +0,0 @@
-import numpy as np
-import sys
-import scipy.interpolate as interpolate
-import asdf
-from astropy.io import fits
-
-from .function import *
-from .basic_func import Basic
-from .function_igm import dijkstra_igm_abs
-from .maketmp_filt import maketemp,maketemp_tau
-
-
-class Func:
-    '''
-    The list of (possible) `Func` attributes is given below:
-
-    Attributes
-    ----------
-    '''
-    def __init__(self, MB, dust_model:int = 0):
-        '''
-        Parameters
-        ----------
-        dust_model : int
-            0 for Calzetti.
-        '''
-        self.ID = MB.ID
-        self.ZZ = MB.Zall
-        self.age = MB.age
-        self.AA = MB.nage
-        self.tau0 = MB.tau0
-        self.MB = MB
-
-        self.dust_model = dust_model
-        self.DIR_TMP = MB.DIR_TMP
-
-        if MB.f_dust:
-            self.Temp = MB.Temp
-
-        try:
-            self.filts = MB.filts
-            self.DIR_FIL = MB.DIR_FILT
-        except:
-            pass
-
-        # Already Read or not;
-        self.f_af = False
-        self.f_af0 = False
-
-
-    def open_spec_fits(self, fall:int = 0, orig:bool = False, f_neb=False):
-        '''
-        Load template in obs range.
-        '''
-        ID0 = self.MB.ID
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-        tau0 = np.arange(0,1,1)
-        ntau0 = len(tau0)
-
-        # ASDF;
-        if fall == 0:
-            app = ''
-            hdu0 = self.MB.af['spec']
-        elif fall == 1:
-            app = 'all_'
-            hdu0 = self.MB.af['spec_full']
-
-        if not f_neb:
-            DIR_TMP = self.DIR_TMP
-            for pp in range(ntau0):
-                for zz in range(len(ZZ)):
-                    Z = ZZ[zz]
-                    NZ = bfnc.Z2NZ(Z)
-                    if zz == 0 and pp == 0:
-                        nr = hdu0['colnum']
-                        xx = hdu0['wavelength']
-                        lib = np.zeros((len(nr), 2+len(AA)*len(ZZ)*len(tau0)), dtype='float')
-                        lib[:,0] = nr[:]
-                        lib[:,1] = xx[:]
-
-                    for aa in range(self.MB.npeak):
-                        coln = int(2+aa)
-                        if orig:
-                            colname = 'fspec_orig_' + str(zz) + '_' + str(aa) + '_' + str(pp)
-                        else:
-                            colname = 'fspec_' + str(zz) + '_' + str(aa) + '_' + str(pp)
-                        colnall = int(2 + pp*len(ZZ)*len(AA) + zz*len(AA) + aa) # 2 takes account of wavelength and AV columns.
-                        lib[:,colnall] = hdu0[colname]
-        else:
-            NZ = len(ZZ)
-            NU = len(self.MB.logUs)
-            for zz,Z in enumerate(ZZ):
-                for uu,logU in enumerate(self.MB.logUs):
-                    if zz == 0 and uu == 0:
-                        nr = hdu0['colnum']
-                        xx = hdu0['wavelength']
-                        coln = int(2 + NZ * NU)
-                        lib = np.zeros((len(nr), coln), dtype=float)
-                        lib[:,0] = nr[:]
-                        lib[:,1] = xx[:]
-                    if orig:
-                        colname = 'fspec_orig_nebular_Z%d'%zz + '_logU%d'%uu
-                    else:
-                        colname = 'fspec_nebular_Z%d'%zz + '_logU%d'%uu
-                    colnall = int(2 + zz * NU + uu) # 2 takes account of wavelength and AV columns.
-                    lib[:,colnall] = hdu0[colname]
-
-        return lib
-
-
-    def open_spec_dust_fits(self, fall:int = 0):
-        '''
-        Loads dust template in obs range.
-        '''
-        ID0 = self.MB.ID
-        tau0= self.MB.tau0
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-
-        if fall == 0:
-            app = ''
-            hdu0 = self.MB.af['spec_dust']
-        elif fall == 1:
-            app = 'all_'
-            hdu0 = self.MB.af['spec_dust_full']
-
-        DIR_TMP = self.DIR_TMP
-        nr = hdu0['colnum']
-        xx = hdu0['wavelength']
-        
-        lib = np.zeros((len(nr), 2+len(self.Temp)), dtype='float')
-        lib[:,0] = nr[:]
-        lib[:,1] = xx[:]
-
-        for aa in range(len(self.Temp)):
-            coln = int(2 + aa)
-            colname = 'fspec_' + str(aa)
-            colnall = int(2 + aa)
-            lib[:,colnall] = hdu0[colname]
-            if fall==1 and False:
-                import matplotlib.pyplot as plt
-                plt.close()
-                plt.plot(lib[:,1],lib[:,coln],linestyle='-')
-                plt.show()
-        return lib
-
-
-    """
-    def open_spec_neb_fits(self, fall:int = 0, orig:bool = False):
-        '''
-        Loads template in obs range.
-        '''
-        ID0 = self.MB.ID
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-
-        # ASDF;
-        if fall == 0:
-            app = ''
-            hdu0 = self.MB.af['spec']
-        elif fall == 1:
-            app = 'all_'
-            hdu0 = self.MB.af['spec_full']
-
-        DIR_TMP = self.DIR_TMP
-
-        NZ = len(ZZ)
-        NU = len(self.MB.logUs)
-        for zz,Z in enumerate(ZZ):
-            for uu,logU in enumerate(self.MB.logUs):
-                if zz == 0 and uu == 0:
-                    nr = hdu0['colnum']
-                    xx = hdu0['wavelength']
-                    coln = int(2 + NZ * NU)
-                    lib = np.zeros((len(nr), coln), dtype=float)
-                    lib[:,0] = nr[:]
-                    lib[:,1] = xx[:]
-
-                if orig:
-                    colname = 'fspec_orig_nebular_Z%d'%zz + '_logU%d'%uu
-                else:
-                    colname = 'fspec_nebular_Z%d'%zz + '_logU%d'%uu
-                colnall = int(2 + zz * NU + uu) # 2 takes account of wavelength and AV columns.
-                lib[:,colnall] = hdu0[colname]
-
-        return lib
-    """
-
-    """
-    def open_spec_fits_dir(self, nage:int, nz:int, kk, Av:float, zgal:float, A00:float, f_IGM=True):
-        '''
-        Load template in obs range.
-        But for weird template.
-        '''
-        tau0= self.tau0
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-
-        app = 'all'
-        hdu0 = self.MB.af['spec_full']
-        DIR_TMP = self.DIR_TMP
-
-        pp = 0
-        zz = nz
-
-        # Luminosity
-        mshdu = self.MB.af0['ML']
-        Ls = mshdu['Ls_%d'%nz] 
-
-        xx = hdu0['wavelength']
-        nr = np.arange(0,len(xx),1)
-
-        lib = np.zeros((len(nr), 2+1), dtype='float')
-        lib[:,0] = nr[:]
-        lib[:,1] = xx[:]
-
-        aa = nage
-        coln = int(2 + aa)
-        colname = 'fspec_' + str(zz) + '_' + str(aa) + '_' + str(pp)
-
-        yy0 = hdu0[colname]/Ls[aa]
-        yy = flamtonu(xx, yy0, m0set=self.MB.m0set)
-        lib[:,2] = yy[:]
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
-
-        xxd *= (1.+zgal)
-        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-        nrd_yyd[:,0] = nrd[:]
-        nrd_yyd[:,1] = yyd[:]
-        nrd_yyd[:,2] = xxd[:]
-
-        b = nrd_yyd
-        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-        yyd_sort = nrd_yyd_sort[:,1]
-        xxd_sort = nrd_yyd_sort[:,2]
-
-        return A00 * yyd_sort, xxd_sort
-    """
-
-    def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_get_Mtot=False, f_check_limit=True):
-        '''
-        Purpose
-        -------
-        get total flux for a given set of parameter.
-
-        Parameters
-        ----------
-        par : library
-            contains parameters. Needs to include: ['Z','A']
-
-        Returns
-        -------
-        xx : 
-            OBSERVED wavelength range at z=MB.zgal
-        '''
-        Mtot:float = 0
-        if lib == None:
-            if lib_all:
-                lib = self.MB.lib_all
-            else:
-                lib = self.MB.lib
-
-        for aa in range(self.MB.npeak):
-            if self.MB.ZEVOL==1 or aa == 0:
-                Z = par['Z'+str(aa)]
-                NZ = self.MB.bfnc.Z2NZ(Z)
-
-            if f_check_limit:
-                # Check limit;
-                if par['A'+str(aa)] < self.MB.Amin:
-                    par['A'+str(aa)] = self.MB.Amin
-                if par['A'+str(aa)] > self.MB.Amax:
-                    par['A'+str(aa)] = self.MB.Amax
-                # Z limit:
-                if aa == 0 or self.MB.ZEVOL == 1:
-                    if par['Z%d'%aa] < self.MB.Zmin:
-                        par['Z%d'%aa] = self.MB.Zmin
-                    if par['Z%d'%aa] > self.MB.Zmax:
-                        par['Z%d'%aa] = self.MB.Zmax
-
-            # Is A in logspace?
-            if f_Alog:
-                A00 = 10**par['A'+str(aa)]
-            else:
-                A00 = par['A'+str(aa)]
-
-            coln = int(2 + pp*len(self.ZZ)*len(self.AA) + NZ*len(self.AA) + aa)
-
-            if f_get_Mtot:
-                mslist = self.MB.af['ML']['ML_'+str(NZ)][aa]
-                Mtot += 10**(par['A%d'%aa] + np.log10(mslist))
-
-            if aa == 0:
-                nr = lib[:,0]
-                xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
-                yy = A00 * lib[:,coln]
-            else:
-                yy += A00 * lib[:,coln]
-
-        if f_get_Mtot:
-            return nr, xx, yy, Mtot
-        else:
-            return nr, xx, yy
-
-
-    def get_total_flux_neb(self, par, f_Alog=True, lib_all=True, lib=None):
-        '''
-        Purpose
-        -------
-        get total flux for a given set of parameter.
-
-        Parameters
-        ----------
-        par : library
-            contains parameters
-        '''
-        if lib == None:
-            if lib_all:
-                lib = self.MB.lib_neb_all
-            else:
-                lib = self.MB.lib_neb
-
-        aa = 0
-        if self.MB.ZEVOL==1 or aa == 0:
-            Z = par['Z'+str(aa)]
-            NZ = self.MB.bfnc.Z2NZ(Z)
-
-        try:
-            Aneb = par['Aneb']
-            logU = par['logU']
-            nlogU = np.argmin(np.abs(self.MB.logUs - logU))
-        except: # This is exception for initial minimizing;
-            Aneb = -99
-            logU = self.MB.logUs[0]
-            nlogU = 0
-
-        # logU
-        NU = self.MB.nlogU
-
-        # Check limit;
-        if Aneb < self.MB.Amin:
-            Aneb = self.MB.Amin
-        if Aneb > self.MB.Amax:
-            Aneb = self.MB.Amax
-        # Z limit:
-        if aa == 0 or self.MB.ZEVOL == 1:
-            if par['Z%d'%aa] < self.MB.Zmin:
-                par['Z%d'%aa] = self.MB.Zmin
-            if par['Z%d'%aa] > self.MB.Zmax:
-                par['Z%d'%aa] = self.MB.Zmax
-
-        # Is A in logspace?
-        if f_Alog:
-            A00 = 10**Aneb
-        else:
-            A00 = Aneb
-
-        coln = int(2 + NZ*NU + nlogU)
-
-        if aa == 0:
-            nr = lib[:,0]
-            xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
-            yy = A00 * lib[:,coln]
-        else:
-            yy += A00 * lib[:,coln]
-
-        return nr, xx, yy
-
-    """
-    def get_template_single(self, A00, Av, nmodel, Z, zgal, lib):
-        '''
-        '''
-        tau0= self.tau0
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-        DIR_TMP = self.MB.DIR_TMP
-        NZ = bfnc.Z2NZ(Z)
-
-        pp0 = np.random.uniform(low=0, high=len(tau0), size=(1,))
-        pp = int(pp0[0])
-        if pp>=len(tau0):
-            pp += -1
-
-        coln = int(2 + pp*len(ZZ)*len(AA) + NZ*len(AA) + nmodel)
-        nr = lib[:,0]
-        xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
-        yy = lib[:,coln]
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
-
-        xxd *= (1.+zgal)
-
-        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-        nrd_yyd[:,0] = nrd[:]
-        nrd_yyd[:,1] = yyd[:]
-        nrd_yyd[:,2] = xxd[:]
-
-        b = nrd_yyd
-        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-        yyd_sort = nrd_yyd_sort[:,1]
-        xxd_sort = nrd_yyd_sort[:,2]
-
-        return A00 * yyd_sort, xxd_sort
-    """
-
-    def get_template_single(self, A00, Av, nmodel, Z, zgal, lib, logU=None, f_apply_dust=True, EBVratio=2.27):
-        '''
-        EBVratio : float
-            E(B-V)_neb / E(B-V)_st. 
-            Useful table in https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf
-
-        Notes
-        -----
-        This function is only used in plot_sed.py.
-        Common function for mebular and nonnebular temlates.
-        '''
-        tau0 = self.tau0
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-        DIR_TMP = self.MB.DIR_TMP
-        NZ = bfnc.Z2NZ(Z)
-
-        pp0 = np.random.uniform(low=0, high=len(tau0), size=(1,))
-        pp = int(pp0[0])
-        if pp>=len(tau0):
-            pp += -1
-
-        if logU != None:
-            NU = len(self.MB.logUs)
-
-            # Dust attenuation to nebulae
-            Av *= EBVratio
-            nlogU = np.argmin(np.abs(self.MB.logUs - logU))
-            coln = int(2 + NZ*NU + nlogU)
-        else:
-            coln = int(2 + pp*len(ZZ)*len(AA) + NZ*len(AA) + nmodel)
-
-        # 
-        nr = lib[:,0]
-        xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
-        yy = lib[:,coln]
-
-        if f_apply_dust:
-            yyd, xxd, nrd = apply_dust(yy, xx/(1+zgal), nr, Av, dust_model=self.dust_model)
-        else:
-            yyd, xxd, nrd = yy, xx, nr
-
-        xxd *= (1.+zgal)
-
-        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-        nrd_yyd[:,0] = nrd[:]
-        nrd_yyd[:,1] = yyd[:]
-        nrd_yyd[:,2] = xxd[:]
-
-        b = nrd_yyd
-        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-        yyd_sort = nrd_yyd_sort[:,1]
-        xxd_sort = nrd_yyd_sort[:,2]
-
-        return A00 * yyd_sort, xxd_sort
-
-
-    def get_template(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib_all:bool=False, f_nrd:bool=False, 
-        f_apply_dust:bool=True, f_IGM=True, deltaz_lim=0.1, f_neb=False, EBVratio:float=2.27):
-        '''
-        Purpose
-        -------
-        Makes model template for a given parameter set, ``par``.
-
-        Parameters
-        ----------
-        nprec : int
-            Precision when redshift is refined. 
-        f_apply_dust : bool
-            Apply dust attenuation to nebular emission?
-        EBVratio : float
-            E(B-V)_neb / E(B-V)_st. 
-            Useful table in https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf
-        f_neb : bool
-            Expect to explore nebular template or not.
-
-        Notes
-        -----
-        This function is only used in plot_sed.py.
-        Common function for mebular and nonnebular temlates.
-        '''
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-
-        if f_val:
-            par = par.params
-
-        if self.MB.fzmc == 1:
-            try:
-                zmc = par['zmc'].value
-            except:
-                zmc = self.MB.zgal
-        else:
-            zmc = self.MB.zgal
-
-        # AV limit;
-        if par['Av'] < self.MB.Avmin:
-            par['Av'] = self.MB.Avmin
-        if par['Av'] > self.MB.Avmax:
-            par['Av'] = self.MB.Avmax
-        Av = par['Av']
-
-        if f_neb:
-            # Dust attenuation to nebulae
-            Av *= EBVratio
-
-        if f_neb:
-            # Get total flux;
-            nr, xx, yy = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=lib_all)
-        else:
-            nr, xx, yy = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all)
-
-        # @@@ Filter convolution may need to happpen here
-        if round(zmc,nprec) != round(self.MB.zgal,nprec):
-            if np.abs(zmc - self.MB.zgal) > deltaz_lim and not self.MB.f_spec:
-                # print('!!! zmc (%.3f) is exploring too far from zgal (%.3f).'%(zmc, self.MB.zgal))
-
-                # @@@ This only work for BB only data set.
-                # Get total flux;
-                if lib_all:
-                    nr_full, xx_full, yy_full = nr, xx, yy
-                else:
-                    if f_neb:
-                        nr_full, xx_full, yy_full = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=True)
-                    else:
-                        nr_full, xx_full, yy_full = self.get_total_flux(par, f_Alog=f_Alog, lib_all=True)
-
-                xx_s, yy_s = filconv(self.MB.filts, xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, self.MB.DIR_FILT, MB=self.MB, f_regist=False)
-            else:
-                fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
-                xx_s = xx / (1+self.MB.zgal) * (1+zmc)
-                yy_s = fint(xx_s)
-
-        else:
-            xx_s = xx
-            yy_s = yy
-
-        xx = xx_s
-        yy = yy_s
-
-        if f_apply_dust:
-            yyd, xxd, nrd = apply_dust(yy, xx/(1+zmc), nr, Av, dust_model=self.dust_model)
-            xxd *= (1.+zmc)
-
-            if self.dust_model != 0:
-                nrd_yyd = np.zeros((len(nrd),3), dtype=float)
-                nrd_yyd[:,0] = nrd[:]
-                nrd_yyd[:,1] = yyd[:]
-                nrd_yyd[:,2] = xxd[:]
-                nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
-                nrd[:],yyd[:],xxd[:] = nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
-
-            if not f_nrd:
-                return yyd[:],xxd[:]
-            else:
-                return nrd[:],yyd[:],xxd[:]
-
-        else:
-            if not f_nrd:
-                return yy,xx
-            else:
-                return nr,yy,xx
-
-
-    def tmp04_dust(self, par, nprec=1):
-        '''
-        Makes model template with a given param setself.
-        Also dust attenuation.
-        '''
-        tau0= self.tau0
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-        DIR_TMP = self.MB.DIR_TMP
-
-        try:
-            m_dust = par['MDUST']
-            t_dust = par['TDUST']
-        except: # This is exception for initial minimizing;
-            m_dust = -99
-            t_dust = 0
-
-        nr = self.MB.lib_dust[:,0]
-        xx = self.MB.lib_dust[:,1] # This is OBSERVED wavelength range at z=zgal
-        coln = 2+int(t_dust+0.5)
-        yy = 10**m_dust * self.MB.lib_dust[:,coln]
-
-        if self.MB.fzmc == 1:
-            zmc = par.params['zmc'].value
-        else:
-            zmc = self.MB.zgal
-
-        # How much does this cost in time?
-        if round(zmc,nprec) != round(self.MB.zgal,nprec):
-            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
-            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
-            yy_s = fint(xx_s)
-        else:
-            xx_s = xx
-            yy_s = yy
-
-        return yy_s, xx_s
-
-
-"""
-## Duplicated. To be deprecated.
-    def get_template(self, lib, Amp:float = 1.0, T:float = 1.0, Av:float = 0.0, Z:float = 0.0, zgal:float = 1.0, f_bb:bool = False, fneb=False):
-        '''
-        Gets an element template given a set of parameters.
-        Not necessarily the most efficient way, but easy to use.
-
-        Parameters:
-        -----------
-        lib : dict
-            library dictionary.
-        Amp : float
-            Amplitude of the target template. Note that each template has Lbol = 1e10Lsun.
-        T : float
-            Age, in Gyr.
-        Av : float
-            Dust attenuation, in mag.
-        Z : float
-            Metallicity, in log(Z/Zsun).
-        zgal : float
-            Redshift.
-        f_bb: bool
-            If calculate bb photometry for the spectrum requested.
-
-        Returns
-            flux : float array. Flux in Fnu. 
-            wavelength : float array. Wave in AA.
-            lcen, lflux : , if f_bb==True.
-
-        '''
-        bfnc = self.MB.bfnc
-        DIR_TMP = self.MB.DIR_TMP 
-        NZ  = bfnc.Z2NZ(Z)
-
-        pp0 = np.random.uniform(low=0, high=self.MB.ntau0, size=(1,))
-        pp  = int(pp0[0])
-        if pp>=self.MB.ntau0:
-            pp += -1
-
-        nmodel = np.argmin(np.abs(T-self.age[:]))
-        if T - self.age[nmodel] != 0:
-            print('T=%.2f is not found in age library. T=%.2f is used.'%(T,self.age[nmodel]))
-
-        if fneb:
-            coln = int(2 + pp*len(self.ZZ)*1 + NZ*1 + 0)
-        else:
-            coln = int(2 + pp*len(self.ZZ)*len(self.AA) + NZ*len(self.AA) + nmodel)
-        nr = lib[:, 0]
-        xx = lib[:, 1] # This is OBSERVED wavelength range at z=zgal
-        yy = lib[:, coln]
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
-        xxd *= (1.+zgal)
-
-        if self.dust_model != 0:
-            # This may be needed when not calzetti model
-            nrd_yyd = np.zeros((len(nrd),3), dtype=float)
-            nrd_yyd[:,0] = nrd[:]
-            nrd_yyd[:,1] = yyd[:]
-            nrd_yyd[:,2] = xxd[:]
-            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
-            nrd[:],yyd[:],xxd[:] = nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
-
-        if f_bb:
-            fil_cen, fil_flux = filconv_fast(self.MB, xxd, Amp * yyd)
-            return Amp * yyd, xxd, fil_flux, fil_cen
-        else:
-            return Amp * yyd, xxd
-"""
-
-class Func_tau:
-    '''
-    '''
-    def __init__(self, MB, dust_model:int=0):
-        '''
-        Parameters:
-        -----------
-        dust_model : int
-            0 for Calzetti. 1 for MW. 4 for Kriek Conroy
-        '''
-        self.MB = MB
-        self.ID = MB.ID
-        self.ZZ = MB.Zall
-        self.AA = MB.nage
-        self.tau = MB.tau
-
-        self.dust_model = dust_model
-        self.DIR_TMP = MB.DIR_TMP
-
-        if MB.f_dust:
-            self.Temp = MB.Temp
-
-        try:
-            self.filts = MB.filts
-            self.DIR_FIL = MB.DIR_FILT
-        except:
-            pass
-
-        # Already Read or not;
-        self.f_af = False
-        self.f_af0 = False
-
-
-    def open_spec_fits(self, fall:int=0, orig:bool=False):
-        '''
-        Loads template in obs range.
-        '''
-        ID0 = self.MB.ID
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-        DIR_TMP = self.DIR_TMP
-
-        # ASDF;
-        if fall == 0:
-            app = ''
-            hdu0 = self.MB.af['spec']
-        elif fall == 1:
-            app = 'all_'
-            hdu0 = self.MB.af['spec_full']
-
-        NZ = len(ZZ)
-        NT = self.MB.ntau
-        NA = self.MB.nage
-        for zz,Z in enumerate(ZZ):
-            for tt,TT in enumerate(self.MB.tau):                
-                for ss,TA in enumerate(self.MB.ageparam):
-                    if zz == 0 and tt == 0 and ss == 0:
-                        nr = hdu0['colnum']
-                        xx = hdu0['wavelength']
-                        coln = int(2 + NZ * NT * NA)
-                        lib = np.zeros((len(nr), coln), dtype=float)
-                        lib[:,0] = nr[:]
-                        lib[:,1] = xx[:]
-
-                    if orig:
-                        colname = 'fspec_orig_' + str(zz) + '_' + str(tt) + '_' + str(ss)
-                    else:
-                        colname = 'fspec_' + str(zz) + '_' + str(tt) + '_' + str(ss)
-                    colnall = int(2 + zz * NT * NA + tt * NA + ss) # 2 takes account of wavelength and AV columns.
-                    lib[:,colnall] = hdu0[colname]
-
-        return lib
-
-
-    def open_spec_dust_fits(self, fall:int = 0):
-        '''
-        Load dust template in obs range.
-        '''
-        ID0 = self.MB.ID
-        tau0= self.MB.tau0
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc #Basic(ZZ)
-
-        # self.MB.af = asdf.open(self.DIR_TMP + 'spec_all_' + self.ID + '.asdf')
-        # self.MB.af0 = asdf.open(self.DIR_TMP + 'spec_all.asdf')
-
-        if fall == 0:
-            app = ''
-            hdu0 = self.MB.af['spec_dust']
-        elif fall == 1:
-            app = 'all_'
-            hdu0 = self.MB.af['spec_dust_full']
-
-        DIR_TMP = self.DIR_TMP
-        nr = hdu0['colnum']
-        xx = hdu0['wavelength']
-        
-        lib = np.zeros((len(nr), 2+len(self.Temp)), dtype='float')
-        lib[:,0] = nr[:]
-        lib[:,1] = xx[:]
-
-        for aa in range(len(self.Temp)):
-            coln = int(2 + aa)
-            colname = 'fspec_' + str(aa)
-            colnall = int(2 + aa) # 2 takes account of wavelength and AV columns.
-            lib[:,colnall] = hdu0[colname]
-            if fall==1 and False:
-                import matplotlib.pyplot as plt
-                plt.close()
-                plt.plot(lib[:,1],lib[:,coln],linestyle='-')
-                plt.show()
-        return lib
-
-
-    def open_spec_neb_fits(self, fall=0, orig=False):
-        '''
-        Loads template in obs range.
-        '''
-        ID0 = self.MB.ID
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-
-        # ASDF;
-        if fall == 0:
-            app = ''
-            hdu0 = self.MB.af['spec']
-        elif fall == 1:
-            app = 'all_'
-            hdu0 = self.MB.af['spec_full']
-
-        DIR_TMP = self.DIR_TMP
-
-        NZ = len(ZZ)
-        NU = len(self.MB.logUs)
-        for zz,Z in enumerate(ZZ):
-            for uu,logU in enumerate(self.MB.logUs):
-                if zz == 0 and uu == 0:
-                    nr = hdu0['colnum']
-                    xx = hdu0['wavelength']
-                    coln = int(2 + NZ * NU)
-                    lib = np.zeros((len(nr), coln), dtype=float)
-                    lib[:,0] = nr[:]
-                    lib[:,1] = xx[:]
-
-                if orig:
-                    colname = 'fspec_orig_nebular_Z%d'%zz + '_logU%d'%uu
-                else:
-                    colname = 'fspec_nebular_Z%d'%zz + '_logU%d'%uu
-                colnall = int(2 + zz * NU + uu) # 2 takes account of wavelength and AV columns.
-                lib[:,colnall] = hdu0[colname]
-
-        return lib
-
-
-    def open_spec_fits_dir(self, nage, nz, kk, Av, zgal, A00):
-        '''
-        Loads template in obs range.
-        But for weird template.
-        '''
-        tau0= self.tau0
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-
-        app = 'all'
-        hdu0 = self.MB.af['spec_full']
-        DIR_TMP = self.DIR_TMP
-
-        pp = 0
-        zz = nz
-
-        # Luminosity
-        mshdu = self.MB.af0['ML']
-        Ls = mshdu['Ls_%d'%nz] 
-
-        xx = hdu0['wavelength'] # at RF;
-        nr = np.arange(0,len(xx),1) #hdu0.data['colnum']
-
-        lib = np.zeros((len(nr), 2+1), dtype='float')
-        lib[:,0] = nr[:]
-        lib[:,1] = xx[:]
-
-        aa = nage
-        coln = int(2 + aa)
-        colname = 'fspec_' + str(zz) + '_' + str(aa) + '_' + str(pp)
-
-        yy0 = hdu0[colname]/Ls[aa]
-        yy = flamtonu(xx, yy0, m0set=self.MB.m0set)
-        lib[:,2] = yy[:]
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
-        xxd *= (1.+zgal)
-
-        if self.dust_model == 0:
-            return A00 * yyd, xxd
-        else:
-            nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-            nrd_yyd[:,0] = nrd[:]
-            nrd_yyd[:,1] = yyd[:]
-            nrd_yyd[:,2] = xxd[:]
-
-            b = nrd_yyd
-            nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-            yyd_sort = nrd_yyd_sort[:,1]
-            xxd_sort = nrd_yyd_sort[:,2]
-            return A00 * yyd_sort, xxd_sort
-
-
-    def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, lib_all=False, f_nrd=False):
-        '''
-        Makes model template with a given param set.
-        Also dust attenuation.
-
-        Parameters:
-        -----------
-        nprec : int
-            Precision when redshift is refined. 
-        '''
-        ZZ = self.ZZ
-        AA = self.AA 
-        bfnc = self.MB.bfnc
-        Mtot = 0
-        pp = 0
-
-        if f_val:
-            par = par.params
-
-        if self.MB.fzmc == 1:
-            try:
-                zmc = par['zmc'].value
-            except:
-                zmc = self.MB.zgal
-        else:
-            zmc = self.MB.zgal
-
-        if check_bound:
-            # AV limit;
-            if par['Av'] < self.MB.Avmin:
-                par['Av'] = self.MB.Avmin
-            if par['Av'] > self.MB.Avmax:
-                par['Av'] = self.MB.Avmax
-        Av = par['Av']
-
-        for aa in range(self.MB.npeak):
-            if self.MB.ZEVOL==1 or aa == 0:
-                if check_bound:
-                    # Z limit:
-                    if par['Z%d'%aa] < self.MB.Zmin:
-                        par['Z%d'%aa] = self.MB.Zmin
-                    if par['Z%d'%aa] > self.MB.Zmax:
-                        par['Z%d'%aa] = self.MB.Zmax
-                Z = par['Z%d'%aa]
-            else:
-                pass
-
-            if check_bound:
-                # A
-                if par['A'+str(aa)] < self.MB.Amin:
-                    par['A'+str(aa)] = self.MB.Amin
-                if par['A'+str(aa)] > self.MB.Amax:
-                    par['A'+str(aa)] = self.MB.Amax
-
-                if par['TAU'+str(aa)] < self.MB.taumin:
-                    par['TAU'+str(aa)] = self.MB.taumin
-                if par['TAU'+str(aa)] > self.MB.taumax:
-                    par['TAU'+str(aa)] = self.MB.taumax
-
-                if par['AGE'+str(aa)] < self.MB.agemin:
-                    par['AGE'+str(aa)] = self.MB.agemin
-                if par['AGE'+str(aa)] > self.MB.agemax:
-                    par['AGE'+str(aa)] = self.MB.agemax
-
-            # Is A in logspace?
-            if f_Alog:
-                A00 = 10**par['A'+str(aa)]
-            else:
-                A00 = par['A'+str(aa)]
-
-            tau,age = par['TAU%d'%aa],par['AGE%d'%aa]
-
-            NZ, NT, NA = bfnc.Z2NZ(Z,tau,age)
-            coln = int(2 + NZ*self.MB.ntau*self.MB.npeak + NT*self.MB.npeak + NA)
-            mslist = self.MB.af['ML']['ML_'+str(NZ)+'_'+str(NT)][NA]
-            Mtot += 10**(par['A%d'%aa] + np.log10(mslist))
-
-            if lib_all:
-                if aa == 0:
-                    nr = self.MB.lib_all[:, 0]
-                    xx = self.MB.lib_all[:, 1] # This is OBSERVED wavelength range at z=zgal
-                    yy = A00 * self.MB.lib_all[:, coln]
-                else:
-                    yy += A00 * self.MB.lib_all[:, coln]
-            else:
-                if aa == 0:
-                    nr = self.MB.lib[:, 0]
-                    xx = self.MB.lib[:, 1] # This is OBSERVED wavelength range at z=zgal
-                    yy = A00 * self.MB.lib[:, coln]
-                else:
-                    yy += A00 * self.MB.lib[:, coln]
-
-        # Keep logM
-        self.MB.logMtmp = np.log10(Mtot)
-
-        # Redshift refinement;
-        if round(zmc,nprec) != round(self.MB.zgal,nprec): # Not sure how much this costs in time.
-            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
-            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
-            yy_s = fint(xx_s)
-        else:
-            xx_s = xx
-            yy_s = yy
-
-        xx = xx_s
-        yy = yy_s
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zmc), nr, Av, dust_model=self.dust_model)
-
-        xxd *= (1.+zmc)
-
-        if self.dust_model == 0:
-            if not f_nrd:
-                return yyd,xxd
-            else:
-                return nrd,yyd,xxd
-        else:
-            nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-            nrd_yyd[:,0] = nrd[:]
-            nrd_yyd[:,1] = yyd[:]
-            nrd_yyd[:,2] = xxd[:]
-            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
-            if not f_nrd:
-                return nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
-            else:
-                return nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
-
-
-    def tmp04_dust(self, par, nprec=1):
-        '''
-        Makes model template with a given param setself.
-        Also dust attenuation.
-        '''
-        bfnc = self.MB.bfnc #Basic(ZZ)
-        DIR_TMP = self.MB.DIR_TMP #'./templates/'
-
-        try:
-            m_dust = par['MDUST']
-            t_dust = par['TDUST']
-        except: # This is exception for initial minimizing;
-            m_dust = -99
-            t_dust = 0
-
-        nr = self.MB.lib_dust[:,0]
-        xx = self.MB.lib_dust[:,1] # This is OBSERVED wavelength range at z=zgal
-        coln= 2+int(t_dust+0.5)
-        yy = 10**m_dust * self.MB.lib_dust[:,coln]
-
-        if self.MB.fzmc == 1:
-            zmc = par.params['zmc'].value
-        else:
-            zmc = self.MB.zgal
-
-        # How much does this cost in time?
-        if round(zmc,nprec) != round(self.MB.zgal,nprec):
-            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
-            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
-            yy_s = fint(xx_s)
-        else:
-            xx_s = xx
-            yy_s = yy
-
-        return yy_s, xx_s
-
-  
-    def get_template_neb(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, 
-        lib_all=False, lib=None, f_nrd=False, f_apply_dust=True):
-        '''
-        Makes model template with a given param set.
-        Also dust attenuation.
-
-        Parameters:
-        -----------
-        nprec : int
-            Precision when redshift is refined. 
-        '''
-        ZZ = self.ZZ
-        AA = self.AA 
-        bfnc = self.MB.bfnc
-        Mtot = 0
-        pp = 0
-
-        if lib == None:
-            if lib_all:
-                lib = self.MB.lib_all
-            else:
-                lib = self.MB.lib
-
-        if f_val:
-            par = par.params
-
-        if self.MB.fzmc == 1:
-            try:
-                zmc = par['zmc'].value
-            except:
-                zmc = self.MB.zgal
-        else:
-            zmc = self.MB.zgal
-
-        if check_bound:
-            # AV limit;
-            if par['Av'] < self.MB.Avmin:
-                par['Av'] = self.MB.Avmin
-            if par['Av'] > self.MB.Avmax:
-                par['Av'] = self.MB.Avmax
-        Av = par['Av']
-
-        try:
-            Aneb = par['Aneb']
-            logU = par['logU']
-            nlogU = np.argmin(np.abs(self.MB.logUs - logU))
-        except: # This is exception for initial minimizing;
-            Aneb = -99
-            logU = self.MB.logUs[0]
-            nlogU = 0
-
-        # logU
-        NU = len(self.MB.logUs)
-        # Check limit;
-        if Aneb < self.MB.Amin:
-            Aneb = self.MB.Amin
-        if Aneb > self.MB.Amax:
-            Aneb = self.MB.Amax
-
-        # Is A in logspace?
-        if f_Alog:
-            A00 = 10**Aneb
-        else:
-            A00 = Aneb
-
-        aa = 0
-        if self.MB.ZEVOL==1 or aa == 0:
-            if check_bound:
-                # Z limit:
-                if par['Z%d'%aa] < self.MB.Zmin:
-                    par['Z%d'%aa] = self.MB.Zmin
-                if par['Z%d'%aa] > self.MB.Zmax:
-                    par['Z%d'%aa] = self.MB.Zmax
-            Z = par['Z%d'%aa]
-            NZ = np.argmin(np.abs(self.MB.Zall-Z))
-
-        coln = int(2 + NZ*NU + nlogU)
-        if aa == 0:
-            nr = lib[:, 0]
-            xx = lib[:, 1] # This is OBSERVED wavelength range at z=zgal
-            yy = A00 * lib[:, coln]
-        else:
-            yy += A00 * lib[:, coln]
-        
-        if round(zmc,nprec) != round(self.MB.zgal,nprec):
-            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
-            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
-            yy_s = fint(xx_s)
-        else:
-            xx_s = xx
-            yy_s = yy
-
-        xx = xx_s
-        yy = yy_s
-
-        if f_apply_dust:
-            yyd, xxd, nrd = apply_dust(yy, xx/(1.+zmc), nr, Av, dust_model=self.dust_model)
-            xxd *= (1.+zmc)
-
-            nrd_yyd = np.zeros((len(nrd),3), dtype=float)
-            nrd_yyd[:,0] = nrd[:]
-            nrd_yyd[:,1] = yyd[:]
-            nrd_yyd[:,2] = xxd[:]
-            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
-
-            if not f_nrd:
-                return nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
-            else:
-                return nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
-
-        else:
-            if not f_nrd:
-                return yy,xx
-            else:
-                return nr, yy,xx
diff --git a/build/lib/gsf/function_igm.py b/build/lib/gsf/function_igm.py
deleted file mode 100644
index 5e34b3c..0000000
--- a/build/lib/gsf/function_igm.py
+++ /dev/null
@@ -1,264 +0,0 @@
-from scipy import asarray as ar,exp
-import numpy as np
-import sys
-from scipy.integrate import simps
-from scipy import integrate
-
-
-def get_XI(z, zout=6, zin=8):
-	'''
-	Very simplified model.
-	'''
-	zs = np.linspace(zout, zin, 100)
-	if z < zout:
-		XI = 0
-	elif z > zin:
-		XI = 1.
-	else:
-		XI = (z-zout) / (zin-zout) * 1.0
-
-	return XI
-
-
-def get_dtdz(z, zs, dtdzs):
-	'''
-	'''
-	iix = np.argmin(np.abs(zs[:-1]-z))
-	return dtdzs[iix]
-
-
-def masongronke_igm_abs(xtmp, ytmp, zin, cosmo=None, xLL=1216., c=3e18, ckms=3e5, zobs=6, xLLL=1400):
-	'''
-	Purpose
-	-------
-
-	Parameters
-	----------
-	xtmp : float array
-		Rest-frame wavelength, in AA.
-	ytmp : float array
-		flux, in f_lambda.
-	zin : target redshift of IGM application
-
-	Returns
-	-------
-	IGM attenuated flux.
-	'''
-	if cosmo == None:
-		from astropy.cosmology import WMAP9 as cosmo
-
-	tau = np.zeros(len(xtmp), dtype=float)
-	xobs = xtmp * (1.*zin)
-	ytmp_abs = np.zeros(len(ytmp), float)
-	zs = np.linspace(zobs,zin,1000)
-
-	xtmp_obs = xtmp * (1+zin)
-	x_HI = 0.8
-	T = 1e0 #1e4 # K
-	sigma_0 = 5.9e-14 * (T / 1e4)**(-1/2) # cm2
-	a_V = 4.7e-4  * (T / 1e4)**(-1/2) # 
-	nu_a = 2.46e15 #Hz
-	k_B = 1.380649e-23 / (1e3)**2 #m2 kg s-2 K-1 * (km/m)**2 = km2 kg/s2/K
-	m_p = 1.67262192e-27 #kilograms
-	delta_nu_d = nu_a * np.sqrt(2 * k_B * T / m_p / ckms**2)
-
-	dtdzs = (cosmo.age(zs)[0:-1].value - cosmo.age(zs)[1:].value) * 1e9 * 365.25 * 3600 * 24 / np.diff(zs) # s
-
-	# xLL = 1390
-	for ii in range(len(xtmp_obs)):
-		if xtmp[ii] < xLL:
-			tau[ii] = 100
-		elif xtmp[ii] < xLLL:
-			nu = c / xtmp_obs[ii] # Hz
-			x = (nu - nu_a) / delta_nu_d
-			phi_x = get_H(x,a_V) #
-
-			# tau[ii] = integrate.quad(lambda z: ckms * get_dtdz(z, zs, dtdzs) * x_HI * sigma_0 * phi_x, zobs, zin)[0] *  get_column(zin, cosmo)
-			tau[ii] = integrate.quad(lambda z: ckms * get_dtdz(z, zs, dtdzs) * x_HI * (1.88e-7 * (1+z)**3) * sigma_0 * phi_x, zobs, zin)[0]
-			print(tau[ii], xtmp_obs[ii])
-		else:
-			tau[ii] = 1e-9
-
-	# R_b1 = 0.0 # Mpc
-	# x_D = 0.8 # neutral fraction
-	#NH = get_column(zin, cosmo)
-	ytmp_abs = ytmp * np.exp(-tau)
-	# con = ()
-	# ytmp_abs[con] = ytmp[con] * np.exp(-tau[con])
-
-	return ytmp_abs
-
-
-def dijkstra_igm_abs(xtmp, ytmp, zin, cosmo=None, xLL=1216., ckms=3e5, 
-	R_b1=1.0, delta_v_0=600, alpha_x=1.0):
-	'''
-	Purpose
-	-------
-	Apply IMG-attenuation of Dijikstra (2014).
-	https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1323358014000332
-
-	Parameters
-	----------
-	xtmp : float array
-		Rest-frame wavelength, in AA.
-	ytmp : float array
-		flux, in f_lambda.
-	zin : 
-		target redshift of IGM application
-	R_b1 : float
-		Bubble size, in Mpc
-
-	Returns
-	-------
-	IGM attenuated flux.
-	'''
-	import scipy.interpolate as interpolate
-	if cosmo == None:
-		from astropy.cosmology import WMAP9 as cosmo
-
-	tau = np.zeros(len(xtmp), dtype=float)
-	xobs = xtmp * (1.*zin)
-	ytmp_abs = np.zeros(len(ytmp), float)
-
-	xtmp_obs = xtmp * (1+zin)
-
-	x_HI = get_XI(zin) # neutral fraction
-	x_D = alpha_x * x_HI # x_D is not clear..
-	delta_lam = (xtmp - xLL) * (zin + 1)
-	delta_lam_fine = (np.linspace(900,2000,1000) - xLL) * (zin + 1)
-
-	delta_v = ckms * delta_lam_fine / (xLL * (1.+zin))
-	delta_v_b1 = delta_v
-	if R_b1>0:
-		delta_v_b1 += cosmo.H(zin).value * R_b1 / (1.+zin) # km / (Mpc s) * Mpc
-
-	tau_fine = 2.3 * x_D * (delta_v_b1/delta_v_0)**(-1) * ((1+zin)/10)**(3/2)
-	con_tau = (tau_fine < 0) | (delta_v_b1 == 0)
-	tau_fine[con_tau] = 100
-
-	fint = interpolate.interp1d(delta_lam_fine, tau_fine, kind='nearest', fill_value="extrapolate")
-	tau = fint(delta_lam)
-
-	# import matplotlib.pyplot as plt
-	# plt.close()
-	# plt.plot(xtmp, tau[:] )
-	# plt.xlim(1200,1500)
-	# plt.show()
-	# hoge
-
-	ytmp_abs = ytmp * np.exp(-tau)
-
-	return ytmp_abs, x_HI
-
-
-def madau_igm_abs(xtmp, ytmp, zin, cosmo=None, xLL=1216.):
-	'''
-	Purpose
-	-------
-	Apply IMG-attenuation of Madau (1995) of zin to the input flux.
-
-	Parameters
-	----------
-	xtmp : float array
-		Rest-frame wavelength, in AA.
-	ytmp : float array
-		flux, in f_lambda.
-	zin : target redshift of IGM application
-
-	Returns
-	-------
-	IGM attenuated flux.
-	'''
-	if cosmo == None:
-		from astropy.cosmology import WMAP9 as cosmo
-
-	tau = np.zeros(len(xtmp), dtype=float)
-	xobs = xtmp * (1.*zin)
-	ytmp_abs = np.zeros(len(ytmp), float)
-
-	NH = get_column(zin, cosmo)
-	tau = (NH/1.6e17) * (xtmp/xLL)**(3.)
-	con = (xtmp<xLL)
-	ytmp_abs[con] = ytmp[con] * np.exp(-tau[con])
-	con = (xtmp>=xLL)
-	ytmp_abs[con] = ytmp[con]
-
-	return ytmp_abs
-
-
-def get_H(x,a):
-	'''
-	Voigt function
-	'''
-	I = integrate.quad(lambda y: np.exp(-y**2)/(a**2 + (x - y)**2),-np.inf, np.inf)[0]
-	return (a/np.pi)*I
-
-
-def get_sig_lya(lam_o, z_s, T=1e4, c=3e18):
-	'''
-	Parameters
-	----------
-	lam_o : float array
-		Observed wavelength, in AA.
-
-	'''
-	nu0 = 2.466e15 #Hz. Lya freq.
-	delnuL = 9.936e7  #Hz. Natural Line Width.
-
-	nu = c / (lam_o * 1e-8)
-
-	# Assume sigma_Lya = 100km/s.
-	#sigma_lya = 100.
-	Vth = 12.85 * (T/1e4)**(1/2) * 1e5 # cm/s
-	delnuD = (Vth/c) * nu0
-
-	x = (nu - nu0)/delnuD
-	a = delnuL / (2.*delnuD)
-	H = get_H(x,a)
-
-	sig_lya = 1.041e-13 * (T/1e4)**(-1/2) * H / np.sqrt(np.pi)
-
-	return sig_lya
-
-
-def get_nH(z):
-	'''
-	Purpose
-	-------
-	Get HI density by using Cen & Haiman 2000.
-
-	Returns
-	-------
-	HI density in IGM, in cm^-3
-	'''
-	try:
-		nH = np.zeros(len(z),dtype='float')
-	except:
-		nH = 0
-
-	nH = 8.5e-5 * ((1.+z)/8)**3
-	return nH
-
-
-def get_column(zin, cosmo, Mpc_cm=3.08568025e+24, z_r=6.0, delz=0.1):
-	'''
-	Returns
-	-------
-	HI column density of IGM at zin, in cm^-2.
-	'''
-	z = np.arange(z_r, zin, delz)
-	try:
-		nH = np.zeros(len(z),dtype='float')
-	except:
-		nH = 0
-
-	# From Cen & Haiman 2000
-	nH = 8.5e-5 * ((1.+z)/8)**3 # in cm^-3
-	NH = 0
-	for zz in range(len(z)):
-		d1 = cosmo.luminosity_distance(z[zz]-delz).value#, **cosmo)
-		d2 = cosmo.luminosity_distance(z[zz]+delz).value#, **cosmo)
-		dx = (d2 - d1) * Mpc_cm
-		NH += nH[zz] * dx/(1.+z[zz])
-
-	return NH
diff --git a/build/lib/gsf/gsf.py b/build/lib/gsf/gsf.py
deleted file mode 100644
index 3b727f8..0000000
--- a/build/lib/gsf/gsf.py
+++ /dev/null
@@ -1,298 +0,0 @@
-import numpy as np
-import sys
-import matplotlib.pyplot as plt
-import os.path
-import string
-from astropy.cosmology import WMAP9 as cosmo
-import asdf
-
-# From gsf
-from .fitting import Mainbody
-from .maketmp_filt import maketemp,maketemp_tau
-from .function_class import Func,Func_tau
-from .basic_func import Basic,Basic_tau
-
-import timeit
-start = timeit.default_timer()
-
-
-def run_gsf_template(inputs, fplt=0, tau_lim=0.001, idman=None, nthin=1, delwave=10):
-    '''
-    Purpose
-    -------
-    This is only for 0 and 1, to get templates.
-    Not for fitting, nor plotting.
-
-    '''
-
-    MB = Mainbody(inputs, c=3e18, Mpc_cm=3.08568025e+24, m0set=25.0, pixelscale=0.06, cosmo=cosmo, idman=idman)
-
-    if os.path.exists(MB.DIR_TMP) == False:
-        os.mkdir(MB.DIR_TMP)
-
-    #
-    # Then load Func and Basic with param range.
-    #
-    if MB.SFH_FORM == -99:
-        MB.fnc = Func(MB) # Set up the number of Age/ZZ
-        MB.bfnc = Basic(MB)
-    else:
-        MB.fnc = Func_tau(MB) # Set up the number of Age/ZZ
-        MB.bfnc = Basic_tau(MB)
-
-
-    #
-    # 0. Make basic templates
-    #
-    if fplt == 0 or fplt == 1:
-        #
-        # 0. Make basic templates
-        #
-        if fplt==0:
-            lammax = 40000 * (1.+MB.zgal) # AA
-            if MB.f_dust:
-                lammax = 2000000 * (1.+MB.zgal) # AA
-
-            if MB.SFH_FORM == -99:
-                if MB.f_bpass == 1:
-                    from .maketmp_z0 import make_tmp_z0_bpass
-                    make_tmp_z0_bpass(MB, lammax=lammax)
-                else:
-                    from .maketmp_z0 import make_tmp_z0
-                    make_tmp_z0(MB, lammax=lammax)
-            else:
-                from .maketmp_z0_tau import make_tmp_z0
-                make_tmp_z0(MB, lammax=lammax)            
-
-    #
-    # 1. Start making redshifted templates.
-    #
-    if fplt<2:
-        #
-        # 1. Start making redshifted templates.
-        #
-        if MB.SFH_FORM == -99:
-            maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
-        else:
-            maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
-
-    # Read temp from asdf;
-    # This has to happend after fplt==1 and before fplt>=2.
-    # MB.af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
-
-    '''
-    #
-    # 2. Load templates
-    #
-    MB.lib = MB.fnc.open_spec_fits(MB, fall=0)
-    MB.lib_all = MB.fnc.open_spec_fits(MB, fall=1)
-    if MB.f_dust:
-        MB.lib_dust = MB.fnc.open_spec_dust_fits(MB, fall=0)
-        MB.lib_dust_all = MB.fnc.open_spec_dust_fits(MB, fall=1)
-    '''
-
-    # How to get SED?
-    if False:
-        import matplotlib.pyplot as plt
-        for T in MB.age[:1]:
-            y0, x0 = MB.fnc.get_template(MB.lib_all, Amp=1.0, T=T, Av=0.0, Z=-1.0, zgal=MB.zgal)
-            plt.plot(x0/(1.+MB.zgal),y0,linestyle='-',lw=1.0, label='$T=%.2f$\n$z=%.2f$'%(T,MB.zgal))
-            plt.xlim(600,20000)
-            plt.xlabel('Rest frame wavelength')
-            plt.xscale('log')
-        plt.legend(loc=0)
-        plt.show()
-
-    return MB
-
-
-def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=None, f_label=True, f_symbol=True, 
-    f_SFMS=False, f_fill=True, save_sed=True, figpdf=False, mmax=300, skip_sfh=False, f_fancyplot=False, 
-    skip_zhist=False, tau_lim=0.001, tset_SFR_SED=0.1, f_shuffle=False, amp_shuffle=1e-2, Zini=None, 
-    nthin=1, delwave=1, f_plot_resid=False, scale=1e-19, f_plot_filter=True, f_prior_sfh=False, norder_sfh_prior=3):
-    '''
-    Purpose
-    -------
-    gsf pipeline, which runs all steps.
-
-    Parameters
-    ----------
-    delwave : float
-        If >0, the input templates get smoothing to delwave. 
-        For fsps, this seems to be critical, so it has the same delwave over the template wavelength range.
-    '''
-
-    ######################
-    # Read from Input file
-    ######################
-    from .function import read_input
-    inputs = read_input(parfile)
-
-    MB = Mainbody(inputs, c=3e18, Mpc_cm=3.08568025e+24, m0set=25.0, pixelscale=0.06, cosmo=cosmo, idman=idman, zman=zman)
-    
-    # Register some params;
-    MB.tau_lim = tau_lim
-    MB.nthin = nthin
-    MB.delwave = delwave
-
-    if os.path.exists(MB.DIR_TMP) == False:
-        os.mkdir(MB.DIR_TMP)
-
-    #
-    # Then load Func and Basic with param range.
-    #
-    if MB.SFH_FORM == -99:
-        MB.fnc = Func(MB) # Set up the number of Age/ZZ
-        MB.bfnc = Basic(MB)
-    else:
-        MB.fnc = Func_tau(MB) # Set up the number of Age/ZZ
-        MB.bfnc = Basic_tau(MB)
-
-    #
-    # Make templates based on input redsfift.
-    #
-    flag_suc = True
-    if fplt == 0 or fplt == 1:
-        #
-        # 0. Make basic templates
-        #
-        if fplt==0:
-            lammax = 40000 * (1.+MB.zgal) # AA
-            if MB.f_dust:
-                lammax = 2000000 * (1.+MB.zgal) # AA
-
-            if MB.SFH_FORM == -99:
-                if MB.f_bpass == 1:
-                    from .maketmp_z0 import make_tmp_z0_bpass
-                    make_tmp_z0_bpass(MB, lammax=lammax)
-                else:
-                    from .maketmp_z0 import make_tmp_z0
-                    make_tmp_z0(MB, lammax=lammax)
-            else:
-                from .maketmp_z0_tau import make_tmp_z0
-                make_tmp_z0(MB, lammax=lammax)            
-
-    if not flag_suc:
-        sys.exit()
-
-    # Read temp from asdf;
-    # Template must be registered before fplt>=2.
-    # try:
-    #     aftmp = MB.af
-    # except:
-    #     MB.af = asdf.open(os.path.join(MB.DIR_TMP, 'spec_all_' + MB.ID + '.asdf'))
-
-    if fplt <= 2:
-        MB.zprev = MB.zgal 
-        MB.ndim_keep = MB.ndim
-
-        #
-        # 1. Start making redshifted templates, at z=MB.zgal.
-        #
-        if MB.SFH_FORM == -99:
-            flag_suc = maketemp(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
-        else:
-            flag_suc = maketemp_tau(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
-
-        #
-        # 2. Main fitting part.
-        #
-        flag_suc = MB.main(cornerplot=cornerplot, f_shuffle=f_shuffle, amp_shuffle=amp_shuffle, Zini=Zini, 
-            f_prior_sfh=f_prior_sfh, norder_sfh_prior=norder_sfh_prior)
-
-        while (flag_suc and flag_suc!=2):
-
-            MB.ndim = MB.ndim_keep
-            print('\n\n')
-            print('Making templates...')
-            print('\n\n')
-
-            # Make temp at the new z
-            if MB.SFH_FORM == -99:
-                flag_suc = maketemp(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
-            else:
-                flag_suc = maketemp_tau(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
-
-            print('\n\n')
-            print('Going into another round with updated templates and redshift.')
-            print('\n\n')
-
-            flag_suc = MB.main(cornerplot=cornerplot, f_shuffle=f_shuffle, amp_shuffle=amp_shuffle, Zini=Zini, 
-                f_prior_sfh=f_prior_sfh, norder_sfh_prior=norder_sfh_prior)
-
-        # Total calculation time
-        stop = timeit.default_timer()
-        print('The whole process took;',stop - start)
-
-    if not flag_suc:
-        sys.exit()
-
-    if fplt <= 3 and flag_suc:
-
-        if True: #fplt >= 3: # If main fitting has not been done;
-            # make redshifted templates and register data;
-            
-            # Use the final redshift;
-            from astropy.io import fits
-            hd_sum = fits.open(os.path.join(MB.DIR_OUT, 'summary_%s.fits'%MB.ID))[0].header
-            MB.zgal = hd_sum['ZMC']
-            
-            if MB.SFH_FORM == -99:
-                flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
-            else:
-                flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
-
-        if MB.SFH_FORM == -99:
-            from .plot_sfh import plot_sfh
-            from .plot_sed import plot_sed            
-        else:
-            from .plot_sfh import plot_sfh_tau as plot_sfh
-            from .plot_sed import plot_sed_tau as plot_sed            
-
-        if not skip_sfh:
-            plot_sfh(MB, fil_path=MB.DIR_FILT, mmax=mmax,
-            dust_model=MB.dust_model, DIR_TMP=MB.DIR_TMP, f_silence=True, 
-            f_SFMS=f_SFMS, f_symbol=f_symbol, skip_zhist=skip_zhist, tau_lim=tau_lim, tset_SFR_SED=tset_SFR_SED)
-
-        plot_sed(MB, fil_path=MB.DIR_FILT,
-        figpdf=figpdf, save_sed=save_sed, mmax=mmax,
-        dust_model=MB.dust_model, DIR_TMP=MB.DIR_TMP, f_label=f_label, f_fill=f_fill, 
-        f_fancyplot=f_fancyplot, f_plot_resid=f_plot_resid, scale=scale, f_plot_filter=f_plot_filter)
-
-    '''
-    if fplt == 4:
-        from .plot_sfh import get_evolv
-        get_evolv(MB, MB.ID, MB.PA, MB.Zall, MB.age, f_comp=MB.ftaucomp, fil_path=MB.DIR_FILT, inputs=MB.inputs, dust_model=MB.dust_model, DIR_TMP=MB.DIR_TMP)
-
-
-    if fplt == 5:
-        from .plot_sfh import plot_evolv
-        plot_evolv(MB, MB.ID, MB.PA, MB.Zall, MB.age, f_comp=MB.ftaucomp, fil_path=MB.DIR_FILT, inputs=MB.inputs, dust_model=MB.dust_model, DIR_TMP=MB.DIR_TMP, nmc=10)
-    '''
-
-    if fplt == 6:
-        # Use the final redshift;
-        from astropy.io import fits
-        hd_sum = fits.open(os.path.join(MB.DIR_OUT, 'summary_%s.fits'%MB.ID))[0].header
-        MB.zgal = hd_sum['ZMC']
-        
-        if MB.SFH_FORM == -99:
-            flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
-        else:
-            flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
-
-        if MB.SFH_FORM == -99:
-            from .plot_sed import plot_corner_physparam_frame,plot_corner_physparam_summary
-            plot_corner_physparam_summary(MB)
-        else:
-            #from .plot_sed_logA import plot_corner_physparam_summary_tau as plot_corner_physparam_summary
-            print('One for Tau model is TBD...')
-
-
-if __name__ == "__main__":
-    '''
-    '''
-    parfile = sys.argv[1]
-    fplt = int(sys.argv[2])
-
-    run_gsf_all(parfile, fplt)
diff --git a/build/lib/gsf/maketmp_filt.py b/build/lib/gsf/maketmp_filt.py
deleted file mode 100644
index 451bc6f..0000000
--- a/build/lib/gsf/maketmp_filt.py
+++ /dev/null
@@ -1,1517 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-import scipy
-import sys
-import os
-from scipy.integrate import simps
-import scipy.interpolate as interpolate
-import asdf
-
-from astropy.io import fits,ascii
-from astropy.modeling.models import Moffat1D
-from astropy.convolution import convolve, convolve_fft
-
-from .function import *
-from .function_igm import *
-
-
-def get_spectrum_draine(lambda_d, DL, zbest, numin, numax, ndmodel, \
-    DIR_DUST='./DL07spec/', phi=0.055, m0set=25.0):
-    '''
-    Parameters
-    ----------
-    lambda_d : array
-        Wavelength array, in AA.
-    phi : float
-        Eq.34 of Draine & Li 2007. (default: 0.055)
-        ~0.055 g / (ergs/s)
-    DL : float
-        in cm.
-
-    Returns
-    -------
-    Interpolated dust emission in Fnu of m0=25.0. In units of Fnu/Msun
-
-    Notes
-    -----
-    umins = ['0.10', '0.15', '0.20', '0.30', '0.40', '0.50', '0.70', '0.80', '1.00', '1.20',\
-            '1.50', '2.00', '2.50', '3.00', '4.00', '5.00', '7.00', '8.00', '10.0', '12.0', '15.0',\
-            '20.0', '25.0']
-    umaxs = ['1e3', '1e4', '1e5', '1e6', '1e7']
-
-    '''
-    from .function import fnutonu
-    import scipy.interpolate as interpolate
-
-    Htokg = 1.66054e-27 # kg/H atom
-    kgtomsun = 1.989e+30 # kg/Msun
-    MsunperH = Htokg / kgtomsun # Msun/H
-    HperMsun = kgtomsun / Htokg # N_Hatom per Msun 
-
-    Jytoerg = 1e-23 # erg/s/cm2/Hz / Jy
-
-    umins = ['0.10', '0.15', '0.20', '0.30', '0.40', '0.50', '0.70', '0.80', '1.00', '1.20',\
-            '1.50', '2.00', '2.50', '3.00', '4.00', '5.00', '7.00', '8.00', '12.0', '15.0',\
-            '20.0', '25.0']
-    umaxs = ['1e3', '1e4', '1e5', '1e6', '1e7']
-        
-    dust_model = DIR_DUST+'draine07_models.txt'
-    fd_model = ascii.read(dust_model)
-
-    umin = umins[numin]
-    umax = umaxs[numax]
-    dmodel = fd_model['name'][ndmodel]
-
-    if ndmodel == 6 or ndmodel == 1:
-        data_start = 55
-    else:
-        data_start = 36
-
-    file_dust = DIR_DUST + 'U%s/U%s_%s_%s.txt'%(umin, umin, umax, dmodel)
-    print(file_dust)
-    fd = ascii.read(file_dust, data_start=data_start)
-
-    wave = fd['col1'] # in mu m.
-    flux = fd['col2'] # nu*Pnu: erg/s H-1
-    flux_dens = fd['col3'] # j_nu, emissivity: Jy cm2 sr-1 H-1
-    #survey = fd['col4'] #
-
-    fobs = flux_dens * Jytoerg / (4.*np.pi*DL**2/(1.+zbest)) * HperMsun
-    # Jy cm2 sr-1 H-1 * erg/s/cm2/Hz / Jy / (cm2 * sr) * (H/Msun) = erg/s/cm2/Hz / Msun
-    # i.e. observed flux density from a dust of Msun at the distance of DL.
-
-    fnu = fnutonu(fobs, m0set=m0set, m0input=-48.6)
-    # Fnu / Msun
-
-    fint = interpolate.interp1d(wave*1e4, fnu, kind='nearest', fill_value="extrapolate")
-    yy_s = fint(lambda_d)
-    con_yys = (lambda_d<1e4) # Interpolation cause some error??
-    yy_s[con_yys] = 0
-
-    return yy_s
-
-
-def sim_spec(lmin, fin, sn):
-    '''
-    SIMULATION of SPECTRA.
-    
-    Parameters
-    ----------
-    wave_obs :
-    wave_temp : 
-    flux_temp : 
-    sn_obs
-
-    Returns
-    -------
-    frand, erand
-    '''
-
-    frand = fin * 0
-    erand = fin * 0
-    for ii in range(len(lmin)):
-        if fin[ii]>0 and sn[ii]>0:
-            erand[ii] = fin[ii]/sn[ii]
-            frand[ii] = np.random.normal(fin[ii],erand[ii],1)
-        else:
-            erand[ii] = 1e10
-            frand[ii] = np.random.normal(fin[ii],erand[ii],1)
-    return frand, erand
-
-
-def check_library(MB, af, nround=3):
-    '''
-    Check library if it has a consistency setup as input file.
-
-    Returns
-    -------
-    flag : bool
-    '''
-
-    # Z needs special care in z0 script, to avoid Zfix.
-    if False:
-        Zmax_tmp, Zmin_tmp = float(MB.inputs['ZMAX']), float(MB.inputs['ZMIN'])
-        delZ_tmp = float(MB.inputs['DELZ'])
-        if Zmax_tmp == Zmin_tmp or delZ_tmp==0:
-            delZ_tmp = 0.0001
-        Zall = np.arange(Zmin_tmp, Zmax_tmp+delZ_tmp, delZ_tmp) # in logZsun
-    else:
-        Zall = MB.Zall
-    
-    flag = True
-    print('Checking the template library...')
-    print('Speficied - Template')
-    # Matallicity:
-    for aa in range(len(Zall)):
-        if Zall[aa] != af['Z%d'%(aa)]:
-            print('Z:', Zall[aa], af['Z%d'%(aa)])
-            flag = False
-
-    if MB.SFH_FORM==-99:
-        # Age:
-        for aa in range(len(MB.age)):
-            if round(MB.age[aa],nround) != round(af['age%d'%(aa)],nround):
-                print('age:', MB.age[aa], af['age%d'%(aa)])
-                flag = False
-        # Tau (e.g. ssp/csp):
-        for aa in range(len(MB.tau0)):
-            if round(MB.tau0[aa]) != round(af['tau0%d'%(aa)]):
-                print('tau0:', MB.tau0[aa], af['tau0%d'%(aa)])
-                flag = False
-    else:
-        # Age:
-        for aa in range(len(MB.ageparam)):
-            if round(MB.ageparam[aa]) != round(af['age%d'%(aa)]):
-                print('age:', MB.ageparam[aa], af['age%d'%(aa)])
-                flag = False
-        for aa in range(len(MB.tau)):
-            if round(MB.tau[aa]) != round(af['tau%d'%(aa)]):
-                print('tau:', MB.tau[aa], af['tau%d'%(aa)])
-                flag = False
-
-    # IMF:
-    if MB.nimf != af['nimf']:
-        print('nimf:', MB.nimf, af['nimf'])
-        flag = False
-
-    return flag
-
-
-def get_LSF(inputs, DIR_EXTR, ID, lm, c=3e18):
-    '''
-    Gets Morphology params, and returns LSF
-    '''
-    Amp = 0
-    f_morp = False
-    if inputs['MORP'] == 'moffat' or inputs['MORP'] == 'gauss':
-        f_morp = True
-        try:
-            mor_file = inputs['MORP_FILE'].replace('$ID','%s'%(ID))
-            fm = ascii.read(DIR_EXTR + mor_file)
-            Amp = fm['A']
-            gamma = fm['gamma']
-            if inputs['MORP'] == 'moffat':
-                alp = fm['alp']
-            else:
-                alp = 0
-        except Exception:
-            print('Error in reading morphology params.')
-            print('No morphology convolution.')
-            pass
-    else:
-        print('MORP Keywords does not match.')
-        print('No morphology convolution.')
-
-    ############################
-    # Template convolution;
-    ############################
-    try:
-        sig_temp = float(inputs['SIG_TEMP'])
-        print('Template is set to %.1f km/s.'%(sig_temp))
-    except:
-        sig_temp = 50.
-        print('Template resolution is unknown.')
-        print('Set to %.1f km/s.'%(sig_temp))
-
-    iixlam = np.argmin(np.abs(lm-4000)) # Around 4000 AA
-    dellam = lm[iixlam+1] - lm[iixlam] # AA/pix
-    R_temp = c / (sig_temp*1e3*1e10)
-    sig_temp_pix = np.median(lm) / R_temp / dellam # delta v in pixel;
-    sig_inst = 0 #65 #km/s for Manga
-
-    # If grism;
-    if f_morp:
-        print('\nStarting templates convolution (intrinsic morphology).')
-        if gamma>sig_temp_pix:# and False:
-            sig_conv = np.sqrt(gamma**2-sig_temp_pix**2)
-        else:
-            sig_conv = 0
-            print('Template resolution is broader than Morphology.')
-            print('No convolution is applied to templates.')
-
-        xMof = np.arange(-5, 5.1, .1) # dimension must be even.
-        if inputs['MORP'] == 'moffat' and Amp>0 and alp>0:
-            LSF = moffat(xMof, Amp, 0, np.sqrt(gamma**2-sig_temp_pix**2), alp)
-            print('Template convolution with Moffat.')
-        elif inputs['MORP'] == 'gauss':
-            sigma = gamma
-            LSF = gauss(xMof, Amp, np.sqrt(sigma**2-sig_temp_pix**2))
-            print('Template convolution with Gaussian.')
-            print('params is sigma;',sigma)
-        else:
-            print('Something is wrong with the convolution file. Exiting.')
-            return False
-
-    else: # For slit spectroscopy. To be updated...
-        print('Templates convolution (intrinsic velocity).')
-        try:
-            vdisp = float(inputs['VDISP'])
-            dellam = lm[1] - lm[0] # AA/pix
-            R_disp = c/(np.sqrt(vdisp**2-sig_inst**2)*1e3*1e10)
-            vdisp_pix = np.median(lm) / R_disp / dellam # delta v in pixel;
-            print('Templates are convolved at %.2f km/s.'%(vdisp))
-            if vdisp_pix-sig_temp_pix>0:
-                sig_conv = np.sqrt(vdisp_pix**2-sig_temp_pix**2)
-            else:
-                sig_conv = 0
-        except:
-            vdisp = 0.
-            print('Templates are not convolved.')
-            sig_conv = 0 #np.sqrt(sig_temp_pix**2)
-            pass
-        xMof = np.arange(-5, 5.1, .1) # dimension must be even.
-        Amp = 1.
-        LSF = gauss(xMof, Amp, sig_conv)
-
-    return LSF, lm
-
-
-def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, 
-    tau_lim=0.001, tmp_norm=1e10, nthin=1, delwave=0, lammax=300000, f_IGM=True):
-    '''
-    Make SPECTRA at given z and filter set.
-    Also, after v1.8, through this function library and data are register to the main class object, MB.
-    
-    Parameters
-    ----------
-    inputs : str
-        Configuration file.
-    zbest : float
-        Best redshift at this iteration. Templates are generated based on this reshift.
-    Z : array
-        Stellar phase metallicity in logZsun.
-    age : array
-        Age, in Gyr.
-    fneb : int
-        flag for adding nebular emissionself.
-    tmp_norm : float
-        Normalization of the stored templated. i.e. each template is in units of tmp_norm [Lsun].
-    '''
-    inputs = MB.inputs
-    age = MB.age
-    nage = MB.nage
-    Z = MB.Zall
-    fneb = MB.fneb
-    DIR_TMP = MB.DIR_TMP
-    zbest = MB.zgal
-    tau0 = MB.tau0
-
-    delz = 1.0
-    wid_z = MB.zmcmax - MB.zmcmin
-    nz = int(wid_z/delz)
-    if nz%2 == 0:
-        nz += 1
-    MB.zbests = np.linspace(MB.zgal - wid_z/2., MB.zgal + wid_z/2., nz)
-    MB.zbests = np.arange(MB.zgal, MB.zgal + 0.01, delz)
-
-    try:
-        af = MB.af0
-    except:
-        af = asdf.open(os.path.join(DIR_TMP, 'spec_all.asdf'))
-        MB.af0 = af
-
-    mshdu = af['ML']
-    spechdu = af['spec']
-
-    # Consistency check:
-    flag = check_library(MB, af)
-    if not flag:
-        print('\n!!!\nThere is inconsistency in z0 library and input file. Exiting.\n!!!\n')
-        sys.exit()
-
-    # ASDF Big tree;
-    # Create header;
-    tree = {
-        'isochrone': af['isochrone'],
-        'library': af['library'],
-        'nimf': af['nimf'],
-        'version_gsf': af['version_gsf']
-    }
-    tree_spec = {}
-    tree_spec_full = {}
-    tree_ML = {}
-    tree_SFR = {}
-
-    try:
-        DIR_EXTR = MB.DIR_EXTR
-        if len(DIR_EXTR)==0:
-            DIR_EXTR = False
-    except:
-        DIR_EXTR = False
-    DIR_FILT = MB.DIR_FILT
-    try:
-        CAT_BB_IND = inputs['CAT_BB_IND']
-    except:
-        CAT_BB_IND = False
-    try:
-        CAT_BB = inputs['CAT_BB']
-    except:
-        CAT_BB = False
-
-    try:
-        SFILT = MB.filts
-        FWFILT = fil_fwhm(SFILT, DIR_FILT)
-        print('')
-        print('Filters in the input catalog are;')
-        print(','.join(MB.filts))
-        print('')
-    except:
-        print('########################')
-        print('Filter is not detected!!')
-        print('Make sure your \nfilter directory is correct.')
-        print('########################')
-        sys.exit()
-
-    try:
-        SKIPFILT = inputs['SKIPFILT']
-        SKIPFILT = [x.strip() for x in SKIPFILT.split(',')]
-    except:
-        SKIPFILT = []
-
-    # If FIR data;
-    if MB.f_dust:
-        DFILT = inputs['FIR_FILTER'] # filter band string.
-        DFILT = [x.strip() for x in DFILT.split(',')]
-        DFWFILT = fil_fwhm(DFILT, DIR_FILT)
-        CAT_BB_DUST = inputs['CAT_BB_DUST']
-        DT0 = float(inputs['TDUST_LOW'])
-        DT1 = float(inputs['TDUST_HIG'])
-        dDT = float(inputs['TDUST_DEL'])
-        print('FIR is implemented.\n')
-    else:
-        print('No FIR is implemented.\n')
-
-    print('############################')
-    print('Making templates at z=%.4f'%(zbest))
-    print('############################')
-    ####################################################
-    # Get extracted spectra.
-    ####################################################
-    #
-    # Get ascii data.
-    #
-    MB.f_spec = False
-    try:
-        spec_files = inputs['SPEC_FILE']
-        spec_files = [x.strip() for x in spec_files.split(',')]
-        ninp0 = np.zeros(len(spec_files), dtype='int')
-        for ff, spec_file in enumerate(spec_files):
-            try:
-                fd0 = np.loadtxt(DIR_EXTR + spec_file, comments='#')
-                lm0tmp = fd0[:,0]
-                fobs0 = fd0[:,1]
-                eobs0 = fd0[:,2]
-                ninp0[ff] = len(lm0tmp)
-            except Exception:
-                print('File, %s/%s, cannot be open.'%(DIR_EXTR,spec_file))
-                pass
-        # Constructing arrays.
-        lm = np.zeros(np.sum(ninp0[:]),dtype=float)
-        fobs = np.zeros(np.sum(ninp0[:]),dtype=float)
-        eobs = np.zeros(np.sum(ninp0[:]),dtype=float)
-        fgrs = np.zeros(np.sum(ninp0[:]),dtype='int')  # FLAG for each grism.
-        for ff, spec_file in enumerate(spec_files):
-            try:
-                fd0 = np.loadtxt(DIR_EXTR + spec_file, comments='#')
-                lm0tmp= fd0[:,0]
-                fobs0 = fd0[:,1]
-                eobs0 = fd0[:,2]
-                for ii1 in range(ninp0[ff]):
-                    if ff==0:
-                        ii = ii1
-                    else:
-                        ii = ii1 + np.sum(ninp0[:ff])
-                    fgrs[ii] = ff
-                    lm[ii]   = lm0tmp[ii1]
-                    fobs[ii] = fobs0[ii1]
-                    eobs[ii] = eobs0[ii1]
-                MB.f_spec = True
-            except Exception:
-                pass
-    except:
-        print('No spec file is provided.')
-        pass
-
-    #############################
-    # READ BB photometry from CAT_BB:
-    #############################
-    if CAT_BB:
-        key_id = 'id'
-        fd0 = ascii.read(CAT_BB)
-        try:
-            id0 = fd0[key_id].astype('str')
-        except:
-            key_id = 'ID'
-            id0 = fd0[key_id].astype('str')
-
-        ii0 = np.where(id0[:]==MB.ID)
-        try:
-            if len(ii0[0]) == 0:
-                print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(MB.ID))
-                return False
-            id = fd0[key_id][ii0]
-        except:
-            print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(MB.ID))
-            print(fd0)
-            return False
-
-        fbb = np.zeros(len(SFILT), dtype=float)
-        ebb = np.zeros(len(SFILT), dtype=float)
-
-        for ii in range(len(SFILT)):
-            try:
-                fbb[ii] = fd0['F%s'%(SFILT[ii])][ii0]
-                ebb[ii] = fd0['E%s'%(SFILT[ii])][ii0]
-            except:
-                print('Could not find flux inputs for filter %s in the input BB catalog! Exiting.'%(SFILT[ii]))
-                return False
-
-    elif CAT_BB_IND: # if individual photometric catalog; made in get_sdss.py
-        unit = 'nu'
-        fd0 = fits.open(DIR_EXTR + CAT_BB_IND)
-        hd0 = fd0[1].header
-        bunit_bb = float(hd0['bunit'][:5])
-        lmbb0 = fd0[1].data['wavelength']
-        fbb0 = fd0[1].data['flux'] * bunit_bb
-        ebb0 = 1/np.sqrt(fd0[1].data['inverse_variance']) * bunit_bb
-
-        try:
-            unit = inputs['UNIT_SPEC']
-        except:
-            print('No param for UNIT_SPEC is found.')
-            print('BB flux unit is assumed to F%s.'%unit)
-            pass
-
-        if unit == 'lambda':
-            print('#########################')
-            print('Changed BB from Flam to Fnu')
-            snbb0 = fbb0/ebb0
-            fbb = flamtonu(lmbb0, fbb0, m0set=MB.m0set)
-            ebb = fbb/snbb0
-        else:
-            snbb0 = fbb0/ebb0
-            fbb = fbb0
-            ebb = ebb0
-
-    else:
-        fbb = np.zeros(len(SFILT), dtype=float)
-        ebb = np.zeros(len(SFILT), dtype=float)
-        for ii in range(len(SFILT)):
-            fbb[ii] = 0
-            ebb[ii] = -99 #1000
-
-    # Dust flux;
-    if MB.f_dust:
-        key_id = 'id'
-        fdd = ascii.read(CAT_BB_DUST)
-        try:
-            try:
-                id0 = fdd[key_id].astype('str')
-            except:
-                key_id = 'ID'
-                id0 = fdd[key_id].astype('str')
-
-            ii0 = np.where(id0[:]==MB.ID)
-            try:
-                id = fd0[key_id][ii0]
-            except:
-                print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(MB.ID))
-                return False
-        except:
-            return False
-            
-        id = fdd[key_id]
-
-        fbb_d = np.zeros(len(DFILT), dtype=float)
-        ebb_d = np.zeros(len(DFILT), dtype=float)
-        for ii in range(len(DFILT)):
-            fbb_d[ii] = fdd['F%s'%(DFILT[ii])][ii0]
-            ebb_d[ii] = fdd['E%s'%(DFILT[ii])][ii0]
-
-    #################
-    # Get morphology;
-    #################
-    if MB.f_spec:
-        LSF, lm = get_LSF(inputs, DIR_EXTR, MB.ID, lm)
-    else:
-        lm = []
-
-    if MB.SFH_FORM == -99:
-        ####################################
-        # Start generating templates
-        ####################################
-        for zz in range(len(Z)):
-            for pp in range(len(tau0)):
-                Zbest = Z[zz]
-                Na = len(age)
-                Ntmp = 1
-
-                for nzz, zbest in enumerate(MB.zbests):
-
-                    age_univ= MB.cosmo.age(zbest).value
-
-                    if zz == 0 and pp == 0:
-                        if delwave>0:
-                            lm0_orig = spechdu['wavelength'][::nthin]
-                            lm0 = np.arange(lm0_orig.min(), lm0_orig.max(), delwave)
-                        else:
-                            lm0 = spechdu['wavelength'][::nthin]
-                        if not lammax == None and not MB.f_dust:
-                            lm0 = lm0[(lm0 * (zbest+1) < lammax)]
-
-                    lmbest = np.zeros((Ntmp, len(lm0)), dtype=float)
-                    fbest = np.zeros((Ntmp, len(lm0)), dtype=float)
-                    lmbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
-                    fbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
-
-                    spec_mul = np.zeros((Na, len(lm0)), dtype=float)
-                    spec_mul_nu = np.zeros((Na, len(lm0)), dtype=float)
-                    spec_mul_nu_conv = np.zeros((Na, len(lm0)), dtype=float)
-
-                    ftmpbb = np.zeros((Na, len(SFILT)), dtype=float)
-                    ltmpbb = np.zeros((Na, len(SFILT)), dtype=float)
-
-                    ftmp_nu_int = np.zeros((Na, len(lm)), dtype=float)
-                    spec_av_tmp = np.zeros((Na, len(lm)), dtype=float)
-
-                    ms = np.zeros(Na, dtype=float)
-                    Ls = np.zeros(Na, dtype=float)
-                    tau = np.zeros(Na, dtype=float)
-                    sfr = np.zeros(Na, dtype=float)
-                    ms[:] = mshdu['ms_'+str(zz)][:] # [:] is necessary.
-                    Ls[:] = mshdu['Ls_'+str(zz)][:]
-                    Fuv = np.zeros(Na, dtype=float)
-
-                    for ss in range(Na):
-                        wave = lm0
-                        if delwave>0:
-                            fint = interpolate.interp1d(lm0_orig, spechdu['fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin], kind='nearest', fill_value="extrapolate")
-                            spec_mul[ss] = fint(lm0)
-                        else:
-                            spec_mul[ss] = spechdu['fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin] # Lsun/A
-
-                        ###################
-                        # IGM attenuation.
-                        ###################
-                        if f_IGM:
-                            # spec_av_tmp = madau_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
-                            spec_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
-                            MB.x_HI = x_HI
-                            spec_mul[ss,:] = spec_av_tmp
-
-                        # Distance;
-                        DL = MB.cosmo.luminosity_distance(zbest).value * MB.Mpc_cm # Luminositydistance in cm
-                        wavetmp = wave*(1.+zbest)
-
-                        # Flam to Fnu
-                        spec_mul_nu[ss,:] = flamtonu(wave, spec_mul[ss,:], m0set=MB.m0set)
-                        spec_mul_nu[ss,:] *= MB.Lsun / (4.*np.pi*DL**2/(1.+zbest))
-                        # (1.+zbest) takes acount of the change in delta lam by redshifting.
-                        # Note that this is valid only when F_nu.
-                        # When Flambda, /(1.+zbest) will be *(1.+zbest).
-
-                        spec_mul_nu[ss,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
-                        ms[ss] *= (1./Ls[ss])*tmp_norm # M/L; 1 unit template has this mass/solar.
-                        try:
-                            tautmp = af['ML']['realtau_%d'%int(zz)]
-                            sfr[ss] = ms[ss] / (tautmp[ss]*1e9) # SFR per unit template, in units of Msolar/yr.
-                        except:
-                            if zz == 0 and ss == 0:
-                                print('realtau entry not found. SFR is set to 0. (Or rerun maketmp_z0.py)')
-                            sfr[ss] = 0
-
-                        if MB.f_spec:
-                            ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu[ss,:])
-
-                        ltmpbb[ss,:], ftmpbb[ss,:] = filconv(SFILT, wavetmp, spec_mul_nu[ss,:], DIR_FILT, MB=MB, f_regist=False)
-
-                        # Convolution has to come after this?
-                        if len(lm)>0:
-                            try:
-                                spec_mul_nu_conv[ss,:] = convolve(spec_mul_nu[ss], LSF, boundary='extend')
-                            except:
-                                #print('Error. No convolution is happening...')
-                                spec_mul_nu_conv[ss,:] = spec_mul_nu[ss]
-                                if zz==0 and ss==0:
-                                    print('Kernel is too small. No convolution.')
-                        else:
-                            spec_mul_nu_conv[ss,:] = spec_mul_nu[ss]
-
-                        ##########################################
-                        # Writing out the templates to fits table.
-                        ##########################################
-                        if ss == 0 and pp == 0 and zz == 0:
-                            # First file
-                            nd1 = np.arange(0,len(lm),1)
-                            nd3 = np.arange(10000,10000+len(ltmpbb[ss,:]),1)
-                            nd_ap = np.append(nd1,nd3)
-                            lm_ap = np.append(lm, ltmpbb[ss,:])
-
-                            # ASDF
-                            tree_spec.update({'wavelength':lm_ap})
-                            tree_spec.update({'colnum':nd_ap})
-
-                            # Second file
-                            # ASDF
-                            nd = np.arange(0,len(wavetmp),1)
-                            if zbest == MB.zgal:
-                                tree_spec_full.update({'wavelength':wavetmp})
-                                tree_spec_full.update({'colnum':nd})
-
-                        # ASDF
-                        spec_ap = np.append(ftmp_nu_int[ss,:], ftmpbb[ss,:])
-                        tree_spec.update({'fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp): spec_ap})
-                        if zbest == MB.zgal:
-                            tree_spec_full.update({'fspec_orig_'+str(zz)+'_'+str(ss)+'_'+str(pp): spec_mul_nu[ss,:]})
-
-                        # For nebular library;
-                        # For every Z, but not for ss and pp.
-                        if fneb == 1 and MB.f_bpass==0 and ss==0 and pp==0:
-                            if zz==0:
-                                spec_mul_neb = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
-                                spec_mul_neb_nu = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
-                                spec_mul_neb_nu_conv = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
-                                ftmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
-                                ltmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
-                                ftmp_neb_nu_int = np.zeros((len(Z), len(MB.logUs), len(lm)), dtype=float)
-
-                            for uu in range(len(MB.logUs)):
-                                if delwave>0:
-                                    fint = interpolate.interp1d(lm0_orig, spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin], kind='nearest', fill_value="extrapolate")
-                                    spec_mul_neb[zz,uu,:] = fint(lm0)
-                                else:
-                                    spec_mul_neb[zz,uu,:] = spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin]
-                                
-                                con_neb = (spec_mul_neb[zz,uu,:]<0)
-                                spec_mul_neb[zz,uu,:][con_neb] = 0
-                                
-                                if f_IGM:
-                                    # spec_neb_av_tmp = madau_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
-                                    spec_neb_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
-                                    # spec_neb_av_tmp = masongronke_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
-                                    spec_mul_neb[zz,uu,:] = spec_neb_av_tmp
-
-                                spec_mul_neb_nu[zz,uu,:] = flamtonu(wave, spec_mul_neb[zz,uu,:], m0set=MB.m0set)
-                                
-                                spec_mul_neb_nu[zz,uu,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
-                                
-                                spec_mul_neb_nu[zz,uu,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
-                                ltmpbb_neb[zz,uu,:], ftmpbb_neb[zz,uu,:] = filconv(SFILT, wavetmp, spec_mul_neb_nu[zz,uu,:], DIR_FILT, MB=MB, f_regist=False)
-
-                                if MB.f_spec:
-                                    ftmp_neb_nu_int[zz,uu,:] = data_int(lm, wavetmp, spec_mul_neb_nu[zz,uu,:])
-
-                                if len(lm)>0:
-                                    try:
-                                        spec_mul_neb_nu_conv[zz,uu,:] = convolve(spec_mul_neb_nu[zz,uu,:], LSF, boundary='extend')
-                                    except:
-                                        spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
-                                else:
-                                    spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
-
-                                if zbest == MB.zgal:
-                                    tree_spec_full.update({'fspec_orig_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu[zz,uu,:]})
-                                    tree_spec_full.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu_conv[zz,uu,:]})
-
-                                spec_neb_ap = np.append(ftmp_neb_nu_int[zz,uu,:], ftmpbb_neb[zz,uu,:])
-                                tree_spec.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_neb_ap})
-
-
-                    #########################
-                    # Summarize the ML
-                    #########################
-                    if pp == 0:
-                        # ML
-                        tree_ML.update({'ML_'+str(zz): ms})
-                        # SFR
-                        tree_SFR.update({'SFR_'+str(zz): sfr})
-
-    else:
-        print('Tau model comes here;')
-
-
-
-    #########################
-    # Summarize the templates
-    #########################
-    tree['id'] = MB.ID
-    tree['z'] = zbest
-    try:
-        tree['x_HI'] = x_HI
-    except:
-        pass
-
-    tree.update({'spec' : tree_spec})
-    tree.update({'spec_full' : tree_spec_full})
-    tree.update({'ML' : tree_ML})
-    tree.update({'SFR' : tree_SFR})
-
-    ######################
-    # Add dust component;
-    ######################
-    if MB.f_dust:
-        tree_spec_dust = {}
-        tree_spec_dust_full = {}
-
-        if DT0 == DT1:
-            Temp = [DT0]
-        else:
-            Temp = np.arange(DT0,DT1,dDT)
-
-        Mdust_temp = np.zeros(len(Temp),float)
-        dellam_d = 1e1
-        lambda_d = np.arange(1e3, 1e7, dellam_d)
-        
-        print('Reading dust table...')
-        for tt in range(len(Temp)):
-            if tt == 0:
-                # For full;
-                nd_d  = np.arange(0,len(lambda_d),1)
-
-                # ASDF
-                tree_spec_dust_full.update({'wavelength': lambda_d*(1.+zbest)})
-                tree_spec_dust_full.update({'colnum': nd_d})
-
-            f_drain = False #True #
-            if f_drain:
-                #numin, numax, nmodel = 8, 3, 9
-                numin, numax, nmodel = tt, MB.dust_numax, MB.dust_nmodel #3, 9
-                fnu_d = get_spectrum_draine(lambda_d, DL, zbest, numin, numax, nmodel, DIR_DUST=MB.DIR_DUST, m0set=MB.m0set)
-                # This should be in fnu w mzp=25.0
-                Mdust_temp[tt] = 1.0
-            else:
-                if tt == 0:
-                    from astropy.modeling import models
-                    from astropy import units as u
-                    print('Dust emission based on Modified Blackbody')
-                    '''
-                    # from Eq.3 of Bianchi 13
-                    kabs0 = 4.0 # in cm2/g
-                    beta_d = 2.08 #
-                    lam0 = 250.*1e4 # mu m to AA
-                    '''
-                    kb = 1.380649e-23 # Boltzmann constant, in J/K
-                    hp = 6.62607015e-34 # Planck constant, in J*s
-                    wav = lambda_d * u.AA
-                    nures = c / wav.value * 1e-9 # GHz
-
-                    Tcmb = 2.726 * (1+zbest)
-                    beta = 1.8
-                    kappa = 0.0484 * (nures/345.)**beta # m^2 / kg
-                    kappa *= (100)**2 / (1e3) # cm2/g
-                    nurest = c / wav.value # Hz
-
-                BT_nu = (2.0 * hp * (nurest)**3. * c**(-2.) / (np.exp(hp*(nurest)/(kb * Temp[tt])) - 1.0))
-                BT_nu_cmb = (2.0 * hp * (nurest)**3. * c**(-2.) / (np.exp(hp*(nurest)/(kb * Temp[tt])) - 1.0))
-                # J s * (Hz)3 * (AA/s)^-2 = 1e+7 erg * (AA)-2 /s
-                denom = BT_nu-BT_nu_cmb
-
-                fnu_d = (1+zbest) / (DL*MB.Mpc_cm)**2 * (kappa * denom)
-                # 1/cm2 * cm2/g * (1e+7 erg * (AA)-2 / s) = 1e7 erg /s / g / AA / AA
-                fnu_d *= 1e7 * 1e16 # erg /s / g / cm2
-                fnu_d *= 1.989e+33 # erg /s / Msun / cm2
-
-                # Into magzp;
-                fnu_d = fnutonu(fnu_d, m0set=MB.m0set, m0input=-48.6)
-                Mdust_temp[tt] = 1. # Msun/temp, like Mass to light ratio.
-
-            # ASDF
-            tree_spec_dust_full.update({'fspec_'+str(tt): fnu_d})
-
-            # Convolution;
-            ALLFILT = np.append(SFILT,DFILT)
-            ltmpbb_d, ftmpbb_d = filconv(ALLFILT,lambda_d*(1.+zbest),fnu_d,DIR_FILT)
-            nd_db = np.arange(0, len(ftmpbb_d), 1)
-
-            if MB.f_spec:
-                ftmp_nu_int_d = data_int(lm, lambda_d*(1.+zbest), fnu_d)
-                ltmpbb_d = np.append(lm, ltmpbb_d)
-                ftmpbb_d = np.append(ftmp_nu_int_d, ftmpbb_d)
-                nd_db = np.arange(0, len(ftmpbb_d), 1)
-
-            if tt == 0:
-                # For conv;
-                # ASDF
-                tree_spec_dust.update({'wavelength': ltmpbb_d})
-                tree_spec_dust.update({'colnum': nd_db})
-
-            tree_spec_dust.update({'fspec_'+str(tt): ftmpbb_d})
-        # Md/temp;
-        tree_spec_dust.update({'Mdust': Mdust_temp})
-        tree.update({'spec_dust' : tree_spec_dust})
-        tree.update({'spec_dust_full' : tree_spec_dust_full})
-        print('dust updated.')
-
-    # Save;
-    af = asdf.AsdfFile(tree)
-    af.write_to(DIR_TMP + 'spec_all_' + MB.ID + '.asdf', all_array_compression='zlib')
-    # Loading the redshifted template;
-    MB.af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
-
-    ##########################################
-    # For observation.
-    # Write out for the Multi-component fitting.
-    ##########################################
-    if True:
-        MB.data = {}
-        file_tmp = 'tmp_library_%s.txt'%MB.ID
-        file_tmp2 = 'tmp_library2%s.txt'%MB.ID
-        fw = open(file_tmp,'w')#
-        fw.write('# BB data (>%d) in this file are not used in fitting.\n'%(ncolbb))
-        for ii in range(len(lm)):
-            g_offset = 1000 * fgrs[ii]
-            if lm[ii]/(1.+zbest) > lamliml and lm[ii]/(1.+zbest) < lamlimu:
-                fw.write('%d %.5f %.5e %.5e\n'%(ii+g_offset, lm[ii], fobs[ii], eobs[ii]))
-            else:
-                fw.write('%d %.5f 0 1000\n'%(ii+g_offset, lm[ii]))
-
-        for ii in range(len(ltmpbb[0,:])):
-            if SFILT[ii] in SKIPFILT:# data point to be skiped;
-                fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii]))
-            elif  ebb[ii]>ebblim:
-                fw.write('%d %.5f 0 1000\n'%(ii+ncolbb, ltmpbb[0,ii]))
-            else:
-                fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii]))
-        fw.close()
-
-        # register;
-        dat = ascii.read(file_tmp, format='no_header')
-        NR = dat['col1']
-        x = dat['col2']
-        fy00 = dat['col3']
-        ey00 = dat['col4']
-        dict_spec_obs = {'NR':NR, 'x':x, 'fy':fy00, 'ey':ey00}
-        MB.data['spec_obs'] = dict_spec_obs
-
-        fw = open(file_tmp,'w')
-        if MB.f_dust:
-            nbblast = len(ltmpbb[0,:])+len(lm)
-            for ii in range(len(ebb_d[:])):
-                if ebb_d[ii]>ebblim:
-                    fw.write('%d %.5f 0 1000\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast]))
-                else:
-                    fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii]))
-        fw.close()
-        if MB.f_dust:
-            dat = ascii.read(file_tmp, format='no_header')
-            nr_d = dat['col1']
-            x_d = dat['col2']
-            fy_d = dat['col3']
-            ey_d = dat['col4']
-            dict_spec_fir_obs = {'NR':nr_d, 'x':x_d, 'fy':fy_d, 'ey':ey_d}
-            MB.data['spec_fir_obs'] = dict_spec_fir_obs
-
-        # BB phot
-        fw = open(file_tmp,'w')
-        fw_rem = open(file_tmp2, 'w')
-        for ii in range(len(ltmpbb[0,:])):
-            if SFILT[ii] in SKIPFILT:# data point to be skiped;
-                fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii], FWFILT[ii]/2., SFILT[ii]))
-                fw_rem.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
-            elif ebb[ii]>ebblim:
-                fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
-            elif ebb[ii]<=0:
-                fw.write('%d %.5f 0 -99 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
-            else:
-                fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
-        fw.close()
-        fw_rem.close()
-        # register;
-        dat = ascii.read(file_tmp, format='no_header')
-        NRbb = dat['col1']
-        xbb  = dat['col2']
-        fybb = dat['col3']
-        eybb = dat['col4']
-        exbb = dat['col5']
-        dict_bb_obs = {'NR':NRbb, 'x':xbb, 'fy':fybb, 'ey':eybb, 'ex':exbb}
-        MB.data['bb_obs'] = dict_bb_obs
-        try:
-            dat = ascii.read(file_tmp2, format='no_header')
-            NR_ex = dat['col1']
-            x_ex = dat['col2']
-            fy_ex = dat['col3']
-            ey_ex = dat['col4']
-            ex_ex = dat['col5']
-            dict_bb_obs_removed = {'NR':NR_ex, 'x':x_ex, 'fy':fy_ex, 'ey':ey_ex, 'ex':ex_ex}
-            MB.data['bb_obs_removed'] = dict_bb_obs_removed
-        except:
-            pass
-
-        # Dust; Not sure where this is being used...
-        fw = open(file_tmp,'w')
-        if MB.f_dust:
-            for ii in range(len(ebb_d[:])):
-                if ebb_d[ii]>ebblim:
-                    fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], DFWFILT[ii]/2., DFILT[ii]))
-                else:
-                    fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii], DFWFILT[ii]/2., DFILT[ii]))
-        fw.close()
-        if MB.f_dust:
-            dat = ascii.read(file_tmp, format='no_header')
-            nr_bb_d = dat['col1']
-            x_bb_d = dat['col2']
-            fy_bb_d = dat['col3']
-            ey_bb_d = dat['col4']
-            dict_bb_fir_obs = {'NR':nr_bb_d, 'x':x_bb_d, 'fy':fy_bb_d, 'ey':ey_bb_d}
-            MB.data['bb_fir_obs'] = dict_bb_fir_obs
-
-        print('Done making templates at z=%.2f.\n'%zbest)
-        os.system('rm %s %s'%(file_tmp, file_tmp2))
-
-    return True
-
-
-def maketemp_tau(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000, tau_lim=0.001,
-    f_IGM=True, nthin=1, tmp_norm=1e10, delwave=0, lammax=300000):
-    '''
-    Make SPECTRA at given z and filter set.
-    
-    Parameters
-    ----------
-    inputs : str
-        Configuration file.
-    zbest :float
-        Best redshift at this iteration. Templates are generated based on this reshift.
-    Z : array
-        Stellar phase metallicity in logZsun.
-    age : array
-        Age, in Gyr.
-    fneb : int
-        flag for adding nebular emissionself.
-    f_IGM : bool
-        IGM attenuation. Madau.
-    nthin : int
-        Thinning templates.
-    lammax : float
-        Maximum wavelength in RF.
-    '''    
-
-    inputs = MB.inputs
-    ID = MB.ID
-    age = MB.ageparam
-    nage = MB.nage
-    tau = MB.tau
-    Z = MB.Zall
-    fneb = MB.fneb
-    DIR_TMP = MB.DIR_TMP
-    zbest = MB.zgal
-
-    af = asdf.open(DIR_TMP + 'spec_all.asdf')
-    mshdu = af['ML']
-    spechdu = af['spec']
-
-    # Consistency check:
-    flag = check_library(MB, af)
-    if not flag:
-        print('\n!!!\nThere is inconsistency in z0 library and input file. Exiting.\n!!!\n')
-        sys.exit()
-
-    # ASDF Big tree;
-    # Create header;
-    tree = {
-        'isochrone': af['isochrone'],
-        'library': af['library'],
-        'nimf': af['nimf'],
-        'version_gsf': af['version_gsf']
-    }
-    tree_spec = {}
-    tree_spec_full = {}
-    tree_ML = {}
-
-    try:
-        DIR_EXTR = MB.DIR_EXTR #inputs['DIR_EXTR']
-        if len(DIR_EXTR)==0:
-            DIR_EXTR = False
-    except:
-        DIR_EXTR = False
-    DIR_FILT = MB.DIR_FILT #inputs['DIR_FILT']
-    try:
-        CAT_BB_IND = inputs['CAT_BB_IND']
-    except:
-        CAT_BB_IND = False
-    try:
-        CAT_BB = MB.CAT_BB #inputs['CAT_BB']
-    except:
-        CAT_BB = False
-
-    try:
-        SFILT = MB.filts #inputs['FILTER'] # filter band string.
-        FWFILT = fil_fwhm(SFILT, DIR_FILT)
-    except:
-        print('########################')
-        print('Filter is not detected!!')
-        print('Make sure your \nfilter directory is correct.')
-        print('########################')
-        sys.exit()
-    try:
-        SKIPFILT = inputs['SKIPFILT']
-        SKIPFILT = [x.strip() for x in SKIPFILT.split(',')]
-    except:
-        SKIPFILT = []
-
-    # If FIR data;
-    if MB.f_dust:
-        DFILT = inputs['FIR_FILTER'] # filter band string.
-        DFILT = [x.strip() for x in DFILT.split(',')]
-        DFWFILT = fil_fwhm(DFILT, DIR_FILT)
-        CAT_BB_DUST = inputs['CAT_BB_DUST']
-        DT0 = float(inputs['TDUST_LOW'])
-        DT1 = float(inputs['TDUST_HIG'])
-        dDT = float(inputs['TDUST_DEL'])
-        print('FIR is implemented.\n')
-    else:
-        print('No FIR is implemented.\n')
-
-
-    print('############################')
-    print('Making templates at z=%.4f'%(zbest))
-    print('############################')
-    ####################################################
-    # Get extracted spectra.
-    ####################################################
-    MB.f_spec = False
-    try:
-        spec_files = inputs['SPEC_FILE'] #.replace('$ID','%s'%(ID))
-        spec_files = [x.strip() for x in spec_files.split(',')]
-        ninp0 = np.zeros(len(spec_files), dtype='int')
-        for ff, spec_file in enumerate(spec_files):
-            try:
-                fd0 = np.loadtxt(DIR_EXTR + spec_file, comments='#')
-                lm0tmp = fd0[:,0]
-                fobs0 = fd0[:,1]
-                eobs0 = fd0[:,2]
-                ninp0[ff] = len(lm0tmp)#[con_tmp])
-            except Exception:
-                print('File, %s/%s, cannot be open.'%(DIR_EXTR,spec_file))
-                pass
-        # Constructing arrays.
-        lm = np.zeros(np.sum(ninp0[:]),dtype=float)
-        fobs = np.zeros(np.sum(ninp0[:]),dtype=float)
-        eobs = np.zeros(np.sum(ninp0[:]),dtype=float)
-        fgrs = np.zeros(np.sum(ninp0[:]),dtype='int')  # FLAG for each grism.
-        for ff, spec_file in enumerate(spec_files):
-            try:
-                fd0 = np.loadtxt(DIR_EXTR + spec_file, comments='#')
-                lm0tmp = fd0[:,0]
-                fobs0 = fd0[:,1]
-                eobs0 = fd0[:,2]
-                for ii1 in range(ninp0[ff]):
-                    if ff==0:
-                        ii = ii1
-                    else:
-                        ii = ii1 + np.sum(ninp0[:ff])
-                    fgrs[ii] = ff
-                    lm[ii] = lm0tmp[ii1]
-                    fobs[ii] = fobs0[ii1]
-                    eobs[ii] = eobs0[ii1]
-                MB.f_spec = True
-            except Exception:
-                pass
-    except:
-        print('No spec file is provided.')
-        pass
-
-    if MB.f_spec:
-        nthin = 1
-
-    #############################
-    # READ BB photometry from CAT_BB:
-    #############################
-    if CAT_BB:
-        fd0 = ascii.read(CAT_BB)
-        id0 = fd0['id'].astype('str')
-        ii0 = np.where(id0[:]==ID)
-        try:
-            id = fd0['id'][ii0]
-        except:
-            print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(ID))
-            return False        
-        if len(ii0) == 0:
-            print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(ID))
-            return False
-
-        fbb = np.zeros(len(SFILT), dtype=float)
-        ebb = np.zeros(len(SFILT), dtype=float)
-
-        for ii in range(len(SFILT)):
-            fbb[ii] = fd0['F%s'%(SFILT[ii])][ii0]
-            ebb[ii] = fd0['E%s'%(SFILT[ii])][ii0]
-
-    elif CAT_BB_IND: # if individual photometric catalog; made in get_sdss.py
-        fd0 = fits.open(DIR_EXTR + CAT_BB_IND)
-        hd0 = fd0[1].header
-        bunit_bb = float(hd0['bunit'][:5])
-        lmbb0= fd0[1].data['wavelength']
-        fbb0 = fd0[1].data['flux'] * bunit_bb
-        ebb0 = 1/np.sqrt(fd0[1].data['inverse_variance']) * bunit_bb
-
-        unit  = 'nu'
-        try:
-            unit = inputs['UNIT_SPEC']
-        except:
-            print('No param for UNIT_SPEC is found.')
-            print('BB flux unit is assumed to Fnu.')
-            pass
-
-        if unit == 'lambda':
-            print('#########################')
-            print('Changed BB from Flam to Fnu')
-            snbb0 = fbb0/ebb0
-            fbb = flamtonu(lmbb0, fbb0, m0set=MB.m0set)
-            ebb = fbb/snbb0
-        else:
-            snbb0 = fbb0/ebb0
-            fbb = fbb0
-            ebb = ebb0
-
-    else:
-        fbb = np.zeros(len(SFILT), dtype=float)
-        ebb = np.zeros(len(SFILT), dtype=float)
-        for ii in range(len(SFILT)):
-            fbb[ii] = 0
-            ebb[ii] = -99
-
-    # Dust flux;
-    if MB.f_dust:
-        fdd = ascii.read(CAT_BB_DUST)
-        id0 = fdd['id'].astype('str')
-        ii0 = np.where(id0[:]==ID)
-        try:
-            id = fd0['id'][ii0]
-        except:
-            print('Could not find the column for [ID: %s] in the input BB catalog! Exiting.'%(ID))
-            return False
-
-        fbb_d = np.zeros(len(DFILT), dtype=float)
-        ebb_d = np.zeros(len(DFILT), dtype=float)
-        for ii in range(len(DFILT)):
-            fbb_d[ii] = fdd['F%s'%(DFILT[ii])][ii0]
-            ebb_d[ii] = fdd['E%s'%(DFILT[ii])][ii0]
-
-    #############################
-    # Getting Morphology params.
-    #############################
-    Amp = 0
-    f_morp = False
-    if MB.f_spec:
-        LSF, lm = get_LSF(inputs, DIR_EXTR, ID, lm)
-    else:
-        lm = []
-
-    ####################################
-    # Start generating templates
-    ####################################
-    for zz in range(len(Z)):
-        Zbest = Z[zz]
-        Na = len(age)
-        Ntmp = 1
-        age_univ= MB.cosmo.age(zbest).value #, use_flat=True, **cosmo)
-
-        for tt in range(len(tau)): # tau
-            if zz == 0 and tt == 0:
-                if delwave>0:
-                    lm0_orig = spechdu['wavelength'][::nthin]
-                    lm0 = np.arange(lm0_orig.min(), lm0_orig.max(), delwave)
-                else:
-                    lm0 = spechdu['wavelength'][::nthin]
-                if not lammax == None and not MB.f_dust:
-                    lm0 = lm0[(lm0 * (zbest+1) < lammax)]
-                wave = lm0
-
-            lmbest = np.zeros((Ntmp, len(lm0)), dtype=float)
-            fbest = np.zeros((Ntmp, len(lm0)), dtype=float)
-            lmbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
-            fbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
-
-            spec_mul = np.zeros((Na, len(lm0)), dtype=float)
-            spec_mul_nu = np.zeros((Na, len(lm0)), dtype=float)
-            spec_mul_nu_conv = np.zeros((Na, len(lm0)), dtype=float)
-
-            ftmpbb = np.zeros((Na, len(SFILT)), dtype=float)
-            ltmpbb = np.zeros((Na, len(SFILT)), dtype=float)
-
-            ftmp_nu_int = np.zeros((Na, len(lm)), dtype=float)
-            spec_av_tmp = np.zeros((Na, len(lm)), dtype=float)
-
-            ms = np.zeros(Na, dtype=float)
-            Ls = np.zeros(Na, dtype=float)
-            ms[:] = mshdu['ms_'+str(zz)+'_'+str(tt)][:] # [:] is necessary.
-            Ls[:] = mshdu['Ls_'+str(zz)+'_'+str(tt)][:]
-            Fuv = np.zeros(Na, dtype=float)
-
-            for ss in range(Na):
-                #print(ss,tt,zz)
-                if ss == 0 and tt == 0 and zz == 0:
-                    DL = MB.cosmo.luminosity_distance(zbest).value * MB.Mpc_cm # Luminositydistance in cm
-                    wavetmp = wave*(1.+zbest)
-
-                if delwave>0:
-                    fint = interpolate.interp1d(lm0_orig, spechdu['fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss)][::nthin], kind='nearest', fill_value="extrapolate")
-                    spec_mul[ss] = fint(lm0)
-                else:
-                    spec_mul[ss] = spechdu['fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss)][::nthin]
-
-                ##################
-                # IGM attenuation.
-                ##################
-                if f_IGM:
-                    # spec_av_tmp = madau_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
-                    spec_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
-                else:
-                    spec_av_tmp = spec_mul[ss,:]
-
-                spec_mul_nu[ss,:] = flamtonu(wave, spec_av_tmp, m0set=MB.m0set)
-
-                spec_mul_nu[ss,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
-                spec_mul_nu[ss,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
-                ms[ss] *= (1./Ls[ss])*tmp_norm # M/L; 1 unit template has this mass in [Msolar].
-
-                if len(lm)>0:
-                    try:
-                        spec_mul_nu_conv[ss,:] = convolve(spec_mul_nu[ss,:], LSF, boundary='extend')
-                    except:
-                        spec_mul_nu_conv[ss,:] = spec_mul_nu[ss,:]
-                        if zz==0 and ss==0:
-                            print('Kernel is too small. No convolution.')
-                else:
-                    spec_mul_nu_conv[ss,:] = spec_mul_nu[ss,:]
-
-                if MB.f_spec:
-                    #ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu_conv[ss,:])
-                    ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu[ss,:])
-                
-                # Register filter response;
-                ltmpbb[ss,:], ftmpbb[ss,:] = filconv(SFILT, wavetmp, spec_mul_nu[ss,:], DIR_FILT, MB=MB, f_regist=False)
-
-
-                ##########################################
-                # Writing out the templates to fits table.
-                ##########################################
-                if ss == 0 and tt == 0 and zz == 0:
-                    # First file
-                    nd1    = np.arange(0,len(lm),1)
-                    nd3    = np.arange(10000,10000+len(ltmpbb[ss,:]),1)
-                    nd_ap  = np.append(nd1,nd3)
-                    lm_ap  = np.append(lm, ltmpbb[ss,:])
-
-                    # ASDF
-                    tree_spec.update({'wavelength':lm_ap})
-                    tree_spec.update({'colnum':nd_ap})
-
-                    # Second file
-                    # ASDF
-                    nd = np.arange(0,len(wavetmp),1)
-                    tree_spec_full.update({'wavelength':wavetmp})
-                    tree_spec_full.update({'colnum':nd})
-
-                # ASDF
-                # ???
-                spec_ap = np.append(ftmp_nu_int[ss,:], ftmpbb[ss,:])
-                tree_spec.update({'fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss): spec_ap})
-                tree_spec_full.update({'fspec_orig_'+str(zz)+'_'+str(tt)+'_'+str(ss): spec_mul_nu[ss,:]})                
-                #tree_spec_full.update({'fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss): spec_mul_nu_conv[ss,:]})
-
-                # Nebular library;
-                if fneb == 1 and MB.f_bpass==0 and ss==0 and tt==0:
-                    if zz==0:
-                        spec_mul_neb = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
-                        spec_mul_neb_nu = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
-                        spec_mul_neb_nu_conv = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
-                        ftmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
-                        ltmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
-                        ftmp_neb_nu_int = np.zeros((len(Z), len(MB.logUs), len(lm)), dtype=float)
-
-                    for uu in range(len(MB.logUs)):
-                        if delwave>0:
-                            fint = interpolate.interp1d(lm0_orig, spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin], kind='nearest', fill_value="extrapolate")
-                            spec_mul_neb[zz,uu,:] = fint(lm0)
-                        else:
-                            spec_mul_neb[zz,uu,:] = spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin]
-                        
-                        con_neb = (spec_mul_neb[zz,uu,:]<0)
-                        spec_mul_neb[zz,uu,:][con_neb] = 0
-                        
-                        if f_IGM:
-                            # spec_neb_av_tmp = madau_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
-                            spec_neb_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
-                            spec_mul_neb[zz,uu,:] = spec_neb_av_tmp
-
-                        spec_mul_neb_nu[zz,uu,:] = flamtonu(wave, spec_mul_neb[zz,uu,:], m0set=MB.m0set)
-                        spec_mul_neb_nu[zz,uu,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
-                        spec_mul_neb_nu[zz,uu,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
-                        ltmpbb_neb[zz,uu,:], ftmpbb_neb[zz,uu,:] = filconv(SFILT, wavetmp, spec_mul_neb_nu[zz,uu,:], DIR_FILT, MB=MB, f_regist=False)
-
-                        if MB.f_spec:
-                            ftmp_neb_nu_int[zz,uu,:] = data_int(lm, wavetmp, spec_mul_neb_nu[zz,uu,:])
-
-                        if len(lm)>0:
-                            try:
-                                spec_mul_neb_nu_conv[zz,uu,:] = convolve(spec_mul_neb_nu[zz,uu,:], LSF, boundary='extend')
-                            except:
-                                spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
-                        else:
-                            spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
-
-                        tree_spec_full.update({'fspec_orig_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu[zz,uu,:]})
-                        tree_spec_full.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu_conv[zz,uu,:]})
-
-                        spec_neb_ap = np.append(ftmp_neb_nu_int[zz,uu,:], ftmpbb_neb[zz,uu,:])
-                        tree_spec.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_neb_ap})
-
-            #########################
-            # Summarize the ML
-            #########################
-            # ASDF
-            tree_ML.update({'ML_'+str(zz)+'_'+str(tt): ms})
-
-    #########################
-    # Summarize the templates
-    #########################
-    tree['id'] = ID
-    tree['z'] = zbest
-    try:
-        tree['x_HI'] = x_HI
-    except:
-        pass
-    tree.update({'spec' : tree_spec})
-    tree.update({'spec_full' : tree_spec_full})
-    tree.update({'ML' : tree_ML})
-
-    ######################
-    # Add dust component;
-    ######################
-    if MB.f_dust:
-        tree_spec_dust = {}
-        tree_spec_dust_full = {}
-
-        if DT0 == DT1:
-            Temp = [DT0]
-        else:
-            Temp = np.arange(DT0,DT1,dDT)
-
-        dellam_d = 1e3
-        lambda_d = np.arange(1e3, 1e7, dellam_d) # RF wavelength, in AA. #* (1.+zbest) # 1um to 1000um; This has to be wide enough, to cut dust contribution at <1um.
-
-        print('Reading dust table...')
-        for tt in range(len(Temp)):
-            if tt == 0:
-                # For full;
-                nd_d  = np.arange(0,len(lambda_d),1)
-
-                # ASDF
-                tree_spec_dust_full.update({'wavelength': lambda_d*(1.+zbest)})
-                tree_spec_dust_full.update({'colnum': nd_d})
-
-            #numin, numax, nmodel = 8, 3, 9
-            numin, numax, nmodel = tt, 3, 9
-            fnu_d = get_spectrum_draine(lambda_d, DL, zbest, numin, numax, nmodel, DIR_DUST=MB.DIR_DUST, m0set=MB.m0set)
-
-            if False:
-                for nn in range(0,11,1):
-                    try:
-                        fnu_d_tmp = get_spectrum_draine(lambda_d, DL, zbest, numin, numax, nn, DIR_DUST=MB.DIR_DUST, m0set=MB.m0set)
-                        plt.plot(lambda_d * (1+zbest), fnu_d_tmp, label='%d'%nn)
-                        plt.xlim(2000, 5000000)
-                        plt.xscale('log')
-                        plt.yscale('log')
-                    except:
-                        print('Errir in ',nn)
-                plt.legend()
-                plt.show()
-
-            # ASDF
-            tree_spec_dust_full.update({'fspec_'+str(tt): fnu_d})
-
-            # Convolution;
-            ALLFILT = np.append(SFILT,DFILT)
-            ltmpbb_d, ftmpbb_d = filconv(ALLFILT,lambda_d*(1.+zbest),fnu_d,DIR_FILT)
-
-            if MB.f_spec:
-                ftmp_nu_int_d = data_int(lm, lambda_d*(1.+zbest), fnu_d)
-                ltmpbb_d = np.append(lm, ltmpbb_d)
-                ftmpbb_d = np.append(ftmp_nu_int_d, ftmpbb_d)
-                nd_db = np.arange(0, len(ftmpbb_d), 1)
-
-            if tt == 0:
-                # For conv;
-                # ASDF
-                tree_spec_dust.update({'wavelength': ltmpbb_d})
-                tree_spec_dust.update({'colnum': nd_db})
-
-            tree_spec_dust.update({'fspec_'+str(tt): ftmpbb_d})
-
-        tree.update({'spec_dust' : tree_spec_dust})
-        tree.update({'spec_dust_full' : tree_spec_dust_full})
-        print('dust updated.')
-
-    # Save;
-    af = asdf.AsdfFile(tree)
-    af.write_to(DIR_TMP + 'spec_all_' + ID + '.asdf', all_array_compression='zlib')
-
-    # Re-register
-    MB.af = af
-
-    ##########################################
-    # For observation.
-    # Write out for the Multi-component fitting.
-    ##########################################
-    fw = open(DIR_TMP + 'spec_obs_' + ID + '.cat', 'w')
-    fw.write('# BB data (>%d) in this file are not used in fitting.\n'%(ncolbb))
-
-    for ii in range(len(lm)):
-        g_offset = 1000 * fgrs[ii]
-        if lm[ii]/(1.+zbest) > lamliml and lm[ii]/(1.+zbest) < lamlimu:
-            fw.write('%d %.5f %.5e %.5e\n'%(ii+g_offset, lm[ii], fobs[ii], eobs[ii]))
-        else:
-            fw.write('%d %.5f 0 1000\n'%(ii+g_offset, lm[ii]))
-
-    for ii in range(len(ltmpbb[0,:])):
-        if SFILT[ii] in SKIPFILT:# data point to be skiped;
-            fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii]))
-        elif  ebb[ii]>ebblim:
-            fw.write('%d %.5f 0 1000\n'%(ii+ncolbb, ltmpbb[0,ii]))
-        else:
-            fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii]))
-
-    fw.close()    
-
-
-    fw = open(DIR_TMP + 'spec_dust_obs_' + ID + '.cat', 'w')
-    if MB.f_dust:
-        nbblast = len(ltmpbb[0,:])+len(lm)
-        for ii in range(len(ebb_d[:])):
-            if ebb_d[ii]>ebblim:
-                fw.write('%d %.5f 0 1000\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast]))
-            else:
-                fw.write('%d %.5f %.5e %.5e\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii]))
-    fw.close()
-
-    # BB phot
-    fw = open(DIR_TMP + 'bb_obs_' + ID + '.cat', 'w')
-    fw_rem = open(DIR_TMP + 'bb_obs_' + ID + '_removed.cat', 'w')
-    for ii in range(len(ltmpbb[0,:])):
-        if SFILT[ii] in SKIPFILT:# data point to be skiped;
-            fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], 0.0, fbb[ii], FWFILT[ii]/2., SFILT[ii]))
-            fw_rem.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
-        elif ebb[ii]>ebblim:
-            fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
-        elif ebb[ii]<=0:
-            fw.write('%d %.5f 0 -99 %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], FWFILT[ii]/2., SFILT[ii]))
-        else:
-            fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb, ltmpbb[0,ii], fbb[ii], ebb[ii], FWFILT[ii]/2., SFILT[ii]))
-    fw.close()
-    fw_rem.close()
-
-    # Dust
-    fw = open(DIR_TMP + 'bb_dust_obs_' + ID + '.cat', 'w')
-    if MB.f_dust:
-        for ii in range(len(ebb_d[:])):
-            if  ebb_d[ii]>ebblim:
-                fw.write('%d %.5f 0 1000 %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], DFWFILT[ii]/2., DFILT[ii]))
-            else:
-                fw.write('%d %.5f %.5e %.5e %.1f %s\n'%(ii+ncolbb+nbblast, ltmpbb_d[ii+nbblast], fbb_d[ii], ebb_d[ii], DFWFILT[ii]/2., DFILT[ii]))
-    fw.close()
-
-    print('Done making templates at z=%.2f.\n'%zbest)
-
-    return True
diff --git a/build/lib/gsf/maketmp_z0.py b/build/lib/gsf/maketmp_z0.py
deleted file mode 100644
index 249bf17..0000000
--- a/build/lib/gsf/maketmp_z0.py
+++ /dev/null
@@ -1,503 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import os
-from astropy.io import ascii,fits
-
-from .function import get_ind
-
-INDICES = ['G4300', 'Mgb', 'Fe5270', 'Fe5335', 'NaD', 'Hb', 'Fe4668', 'Fe5015', 'Fe5709', 'Fe5782', 'Mg1', 'Mg2', 'TiO1', 'TiO2']
-
-
-def make_tmp_z0(MB, lammin=100, lammax=160000, tau_lim=0.001, force_no_neb=False, Zforce=None, f_mp=True):
-    '''
-    This is for the preparation of default template, with FSPS, at z=0.
-    Should be run before SED fitting.
-
-    Parameters
-    ----------
-    :class:`gsf.fitting.Mainbody` : class
-        Mainbody class, that contains attributes.
-
-    lammin : float, optional
-        Minimum value of the rest-frame wavelength of the template, in AA.
-
-    lammax : float, optional
-        Maximum value of the rest-frame wavelength of the template, in AA.
-
-    tau_lim : float, optional
-        Maximum value of tau of the template, in Gyr. Tau smaller than this 
-        value would be approximated by SSP.
-
-    force_no_neb : bool
-        Turn this on that you are very much sure do not want to include emission line templates, 
-        maybe to save some time running z0 module.
-
-    f_mp : bool
-        Multiprocessing.
-    '''
-    import asdf
-    import fsps
-    import gsf
-
-    nimf = MB.nimf
-    age = MB.age
-    tau0 = MB.tau0
-    fneb = MB.fneb
-    if not force_no_neb:
-        fneb = True
-    DIR_TMP = MB.DIR_TMP
-    Na = len(age)
-
-    if not Zforce == None:
-        file_out = 'spec_all_Z%.1f.asdf'%Zforce
-    else:
-        file_out = 'spec_all.asdf'
-    Z = MB.Zall
-    NZ = len(Z)
-    
-    # Current age in Gyr;
-    age_univ = MB.cosmo.age(0).value
-
-    print('#######################################')
-    print('Making templates at z=0, IMF=%d'%(nimf))
-    print('#######################################')
-    
-    tree_spec = {}
-    tree_ML = {}
-    tree_lick = {}
-
-    print('tau is the width of each age bin.')
-    tau_age = np.zeros(Na,dtype='float')
-    age_age = np.zeros(Na,dtype='float')
-    flagz = True
-    for zz in range(len(Z)):
-        if not Zforce == None and Z[zz] != Zforce:
-            continue
-        for pp in range(len(tau0)):
-            spall = [] # For ssp model
-            ms = np.zeros(Na, dtype='float')
-            Ls = np.zeros(Na, dtype='float')
-            mlost = np.zeros(Na, dtype='float')
-            LICK = np.zeros((Na,len(INDICES)), dtype='float')
-            tau0_old = 0
-            for ss in range(Na):
-                #
-                # Determining tau for each age bin;
-                #
-
-                # 1.Continuous age bin;
-                if int(tau0[pp]) == 99:
-                    if ss==0:
-                        delTl = age[ss]
-                        delTu = (age[ss+1]-age[ss])/2.
-                        delT = delTu + delTl
-                        tautmp = delT
-                        agetmp = age[ss]+delTu
-                    elif ss == len(age)-1:
-                        delTl = (age[ss]-age[ss-1])/2.
-                        delTu = delTl
-                        delT = delTu + delTl
-                        tautmp = delT
-                        agetmp = age[ss]+delTu
-                    else:
-                        delTl = (age[ss]-age[ss-1])/2.
-                        delTu = (age[ss+1]-age[ss])/2.
-                        delT = delTu + delTl
-                        tautmp = delT
-                        agetmp = age[ss]+delTu
-                # 2.A fixed-age bin;
-                elif tau0[pp] > 0.0:
-                    tautmp = tau0[pp]
-                    agetmp = age[ss] + tautmp/2.
-                # 3.SSP;
-                else: # =Negative tau;
-                    tautmp = tau_lim
-                    agetmp = age[ss]
-
-                # Keep tau in header;
-                tau_age[ss] = tautmp
-                age_age[ss] = agetmp
-
-                #
-                # Then, make sps.
-                #
-                f_add_dust = False # Not yet. Or never...
-                if tautmp != tau0_old:
-                    if int(tau0[pp]) == 99:
-                        print('CSP is applied.')
-                        print('At t=%.3f, tau is %.3f Gyr' %(age[ss],tautmp))
-                        sptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0) # Lsun/Hz
-                        if fneb:
-                            esptmp = fsps.StellarPopulation(zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0, add_neb_emission=1)
-                        if f_add_dust:
-                            dsptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz],
-                            dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0,
-                            duste_gamma=0.01, duste_umin=1.0, duste_qpah=3.5, fagn=0.0)
-                    elif tau0[pp] > 0.0:
-                        print('At t=%.3f, fixed tau, %.3f, is applied.'%(age[ss],tautmp))
-                        sptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0) # Lsun/Hz
-                        if fneb:
-                            esptmp = fsps.StellarPopulation(zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0, add_neb_emission=1)
-                        if f_add_dust:
-                            dsptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=1, logzsol=Z[zz], 
-                            dust_type=2, dust2=0.0, tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0,
-                            duste_gamma=0.01, duste_umin=1.0, duste_qpah=3.5, fagn=0.0)
-                    else: # =Negative tau;
-                        print('At t=%.3f, SSP (%.3f) is applied.'%(age[ss],tautmp))
-                        sptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=0, logzsol=Z[zz], dust_type=2, dust2=0.0) # Lsun/Hz
-                        if fneb:
-                            esptmp = fsps.StellarPopulation(zcontinuous=1, imf_type=nimf, sfh=0, logzsol=Z[zz], dust_type=2, dust2=0.0, add_neb_emission=1)
-                        if f_add_dust:
-                            dsptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=0, logzsol=Z[zz], dust_type=2, dust2=1.0,
-                            tau=20, const=0, sf_start=0, sf_trunc=tautmp, tburst=13, fburst=0,
-                            duste_gamma=0.01, duste_umin=1.0, duste_qpah=3.5, fagn=0.0)
-                else:
-                    print('At t=%.3f, tau is %.3f Gyr' %(age[ss],tautmp))
-                    print('Skip fsps, by using previous library.')
-
-                tau0_old = tautmp
-                sp = sptmp
-                print('Z:%d/%d, t:%d/%d, %s, %s'%(zz+1, len(Z), pp+1, len(tau0), sp.libraries[0].decode("utf-8") , sp.libraries[1].decode("utf-8")))
-
-                wave0, flux0 = sp.get_spectrum(tage=age[ss], peraa=True) # Lsun/AA
-                con = (wave0>lammin) & (wave0<lammax)
-                wave, flux = wave0[con], flux0[con]
-                mlost[ss] = sp.stellar_mass / sp.formed_mass
-
-                if fneb and pp == 0 and ss == 0:
-                    esptmp.params['gas_logz'] = Z[zz] # gas metallicity, assuming = Zstel
-                    # Loop within logU;
-                    for nlogU, logUtmp in enumerate(MB.logUs):
-                        esptmp.params['gas_logu'] = logUtmp
-                        esp = esptmp
-                        ewave0, eflux0 = esp.get_spectrum(tage=0.001, peraa=True)
-                        con = (ewave0>lammin) & (ewave0<lammax)
-                        flux_nebular = eflux0[con]-flux
-                        tree_spec.update({'flux_nebular_Z%d'%zz+'_logU%d'%nlogU: flux_nebular})
-                
-                if f_add_dust:
-                    wave0_d, flux0_d = dsptmp.get_spectrum(tage=age[ss], peraa=True)
-                    con = (wave0_d>lammin) & (wave0_d<lammax)
-                    wave_d, flux_d = wave0[con], flux0[con]
-                    plt.plot(wave0, flux0, linestyle='--')
-                    plt.plot(wave0_d, flux0_d, linestyle='--')
-                    plt.xlim(4000,100000)
-                    plt.xscale('log')
-                    plt.show()
-
-                # Mass-Luminosity
-                ms[ss] = sp.stellar_mass # Survived mass
-                Ls[ss] = 10**sp.log_lbol
-                LICK[ss,:] = get_ind(wave, flux)
-
-                if flagz and ss == 0 and pp == 0:
-                    # ASDF Big tree;
-                    # Create header;
-                    tree = {
-                        'isochrone': '%s'%(sp.libraries[0].decode("utf-8")),
-                        'library': '%s'%(sp.libraries[1].decode("utf-8")),
-                        'nimf': nimf,
-                        'version_gsf': gsf.__version__
-                    }
-                    if fneb:
-                        tree.update({'logUMIN': MB.logUMIN})
-                        tree.update({'logUMAX': MB.logUMAX})
-                        tree.update({'DELlogU': MB.DELlogU})
-
-                    tree_spec.update({'wavelength': wave})
-                    flagz = False
-
-                tree_spec.update({'fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp): flux})
-
-            if pp == 0:
-                # use tau0[0] as representative for M/L and index.
-                for ll in range(len(INDICES)):
-                    # ASDF
-                    tree_lick.update({INDICES[ll]+'_'+str(zz): LICK[:,ll]})
-
-                col1 = fits.Column(name='ms_'+str(zz), format='E', unit='Msun', array=ms)
-                tree_ML.update({'ms_'+str(zz): ms})
-                col2 = fits.Column(name='Ls_'+str(zz), format='E', unit='Lsun', array=Ls)
-                tree_ML.update({'Ls_'+str(zz): Ls})
-                col3 = fits.Column(name='fm_'+str(zz), format='E', unit='', array=mlost)
-                tree_ML.update({'frac_mass_survive_'+str(zz): mlost})
-                col4 = fits.Column(name='tau_'+str(zz), format='E', unit='Gyr', array=tau_age)
-                tree_ML.update({'realtau_'+str(zz): ms})
-
-
-    # Write;
-    for aa in range(len(age)):
-        tree.update({'realtau%d(Gyr)'%(aa): tau_age[aa]})
-    for aa in range(len(age)):
-        tree.update({'realage%d(Gyr)'%(aa): age_age[aa]})
-    for aa in range(len(age)):
-        tree.update({'age%d'%(aa): age[aa]})
-    for aa in range(len(Z)):
-        tree.update({'Z%d'%(aa): Z[aa]})
-    for aa in range(len(tau0)):
-        tree.update({'tau0%d'%(aa): tau0[aa]})
-
-    # Index, Mass-to-light;
-    tree.update({'spec' : tree_spec})
-    tree.update({'ML' : tree_ML})
-    tree.update({'lick' : tree_lick})
-
-    # Save
-    af = asdf.AsdfFile(tree)
-    af.write_to(DIR_TMP + file_out, all_array_compression='zlib')
-
-
-def make_tmp_z0_bpass(MB, lammin=100, lammax=160000, Zforce=None, Zsun=0.02):
-    '''
-    This is for the preparation of default template, with BPASS templates, at z=0.
-    Should be run before SED fitting.
-
-    Parameters
-    ----------
-    :class:`gsf.fitting.Mainbody` : class
-        Mainbody class, that contains attributes.
-
-    lammin : float, optional
-        Minimum value of the rest-frame wavelength of the template, in AA.
-
-    lammax : float, optional
-        Maximum value of the rest-frame wavelength of the template, in AA.
-
-    BPASS_DIR : str, optional
-        Path to the ditectory where BPASS templates are storesd.
-
-    BPASS_ver : str, optional
-        Version of BPASS. Used to identify template files.
-
-    Zsun : float, optional
-        Metallicity of templates, in units of absolute value (e.g. Zsun=0.02 for BPASS).
-    '''
-    import asdf
-    import gsf
-
-    nimf = MB.nimf
-    if nimf == 0: # Salpeter
-        imf_str = '135all_100'
-    elif nimf == 1:
-        imf_str = '_chab100'
-    else:
-        imf_str = ''
-
-    if not Zforce == None:
-        file_out = 'spec_all_Z%.1f.asdf'%Zforce
-    else:
-        file_out = 'spec_all.asdf'
-
-    Z = MB.Zall
-    age = MB.age
-    tau0 = MB.tau0
-    fneb = MB.fneb
-    if fneb:
-        print('Currently, BPASS does not have option of nebular emission.')
-        fneb = False
-    DIR_TMP= MB.DIR_TMP
-
-    # binary?
-    f_bin = MB.f_bin
-    #f_bin = True
-    if f_bin==1:
-        bin_str = 'bin'
-    else:
-        bin_str = 'str'
-
-    DIR_LIB = MB.BPASS_DIR + 'BPASS%s/BPASS%s_%s-imf%s/'%(MB.BPASS_ver,MB.BPASS_ver,bin_str,imf_str)
-
-    NZ = len(Z)
-    Na = len(age)
-
-    # Current age in Gyr;
-    age_univ = MB.cosmo.age(0).value
-
-    print('#######################################')
-    print('Making templates at z=0, IMF=%d'%(nimf))
-    print('#######################################')
-
-    tree_spec = {}
-    tree_ML = {}
-    tree_lick = {}
-
-    tau_age = np.zeros(Na,dtype='float')
-    age_age = np.zeros(Na,dtype='float')
-
-    flagz = True
-    for zz in range(len(Z)):
-        if not Zforce == None and Z[zz] != Zforce:
-            continue
-        #
-        # open spectral file;
-        #
-        if 10**(Z[zz])*Zsun>1e-4:
-            zstrtmp = round(10**(Z[zz])*Zsun/10,5)
-            zstrtmp2 = '%.6s'%(zstrtmp)
-            z_str = zstrtmp2[3:]
-            if len(z_str)<3:
-                z_str = z_str+'0'
-        elif 10**(Z[zz])*Zsun>1e-5:
-            z_str = 'em4'
-        else:
-            z_str = 'em5'
-
-        file_sed = '%sspectra-%s-imf%s.z%s.dat'%(DIR_LIB,bin_str,imf_str,z_str)
-        file_stm = '%sstarmass-%s-imf%s.z%s.dat'%(DIR_LIB,bin_str,imf_str,z_str)
-        fd_sed = ascii.read(file_sed)
-        fd_stm = ascii.read(file_stm)
-
-        wave0 = fd_sed['col1']
-        age_stm = fd_stm['col1']
-        mass_formed = fd_stm['col2'][0]
-
-        ncols = 52
-        nage_temp = np.arange(2,ncols+1,1)
-        lage_temp = (6+0.1*(nage_temp-2))
-        age_temp = 10**(6+0.1*(nage_temp-2)) # in yr
-
-        # 'tau is the width of each age bin.'
-        for pp in range(len(tau0)):
-            spall = [] # For sps model
-            ms = np.zeros(Na, dtype='float')
-            Ls = np.zeros(Na, dtype='float')
-            mlost = np.zeros(Na, dtype='float')
-            LICK = np.zeros((Na,len(INDICES)), dtype='float')
-            tau0_old = 0
-
-            for ss in range(Na):
-                mass_formed_tot = 0
-                flux0 = np.zeros(len(wave0),'float')
-                #
-                # Determining tau for each age bin;
-                #
-                if int(tau0[pp]) == 99:
-                    if ss==0:
-                        tautmp = age[ss]
-                        agetmp = age[ss]/2.
-                        con_tau= np.where((age_temp[:]<=age[ss]*1e9) & (age_temp[:]>0))
-                        for sstmp in con_tau[0][-1:]:
-                            flux0  += fd_sed['col%d'%(sstmp+2)][:]
-                            ms[ss] += fd_stm['col2'][sstmp]
-                            mass_formed_tot += mass_formed
-                    else:
-                        tautmp = age[ss] - age[ss-1]
-                        agetmp = age[ss] - (age[ss]-age[ss-1])/2.
-                        con_tau= np.where((age_temp[:]<=age[ss]*1e9) & (age_temp[:]>age[ss-1]*1e9))
-                        for sstmp in con_tau[0][-1:]:
-                            flux0  += fd_sed['col%d'%(sstmp+2)][:]
-                            ms[ss] += fd_stm['col2'][sstmp]
-                            mass_formed_tot += mass_formed
-
-                elif tau0[pp] > 0.0:
-                    if ss==0 and age[ss]<tau0[pp]:
-                        tautmp = age[ss]
-                        agetmp = age[ss]/2.
-                        con_tau= np.where((age_temp[:]<=age[ss]*1e9) & (age_temp[:]>0))
-                        for sstmp in con_tau[0]:
-                            flux0  += fd_sed['col%d'%(sstmp+2)][:]
-                            ms[ss] += fd_stm['col2'][sstmp]
-                            mass_formed_tot += mass_formed
-
-                    elif (age[ss]-age[ss-1]) < tau0[pp]:
-                        tautmp = age[ss] - age[ss-1]
-                        agetmp = age[ss] - (age[ss]-age[ss-1])/2.
-                        con_tau= np.where((age_temp[:]<=age[ss]*1e9) & (age_temp[:]>age[ss-1]*1e9))
-                        for sstmp in con_tau[0]:
-                            flux0  += fd_sed['col%d'%(sstmp+2)][:]
-                            ms[ss] += fd_stm['col2'][sstmp]
-                            mass_formed_tot += mass_formed
-
-                    else:
-                        tautmp = tau0[pp]
-                        agetmp = age[ss] - tau0[pp]/2.
-                        con_tau= np.where((age_temp[:]<=age[ss]*1e9) & (age_temp[:]>(age[ss] - tau0[pp])*1e9))
-                        for sstmp in con_tau[0]:
-                            flux0  += fd_sed['col%d'%(sstmp+2)][:]
-                            ms[ss] += fd_stm['col2'][sstmp]
-                            mass_formed_tot += mass_formed
-
-                else: # =Negative tau; SSP
-                    iis   = np.argmin(np.abs(age[ss] - age_temp[:]/1e9))
-                    iistm = np.argmin(np.abs(age[ss] - 10**age_stm[:]/1e9))
-                    if ss==0:
-                        tautmp = 10**6.05 / 1e9 # in Gyr
-                        agetmp = age[ss]/2.
-                    else:
-                        tautmp = ( 10**(lage_temp[iis]+0.05) - 10**(lage_temp[iis]-0.05) ) / 1e9 # Gyr
-                        agetmp = (age[ss]+age[ss-1])/2.
-
-                    flux0  = fd_sed['col%d'%(iis+2)]
-                    ms[ss] = fd_stm['col2'][iistm]
-                    mass_formed_tot += mass_formed
-
-                # Keep tau in header;
-                tau_age[ss] = tautmp
-                age_age[ss] = agetmp
-
-                # Then. add flux if tau > 0.
-                con = (wave0>lammin) & (wave0<lammax)
-                wave, flux = wave0[con], flux0[con]
-                # Temp
-                mlost[ss] =  ms[ss] / mass_formed_tot
-
-                Ls[ss] = np.sum(flux0) # BPASS sed is in Lsun.
-                LICK[ss,:] = get_ind(wave, flux)
-
-                if flagz and ss == 0 and pp == 0:
-                    # ASDF Big tree;
-                    # Create header;
-                    tree = {
-                        'isochrone': 'BPASS',
-                        'library': 'BPASS',
-                        'nimf': nimf,
-                        'version_gsf': gsf.__version__
-                    }
-
-                    # ASDF
-                    tree_spec.update({'wavelength': wave})
-                    flagz = True
-
-                # ASDF
-                tree_spec.update({'fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp): flux})
-
-                if fneb:
-                    # ASDF
-                    tree_spec.update({'efspec_'+str(zz)+'_'+str(ss)+'_'+str(pp): eflux})
-
-            if pp == 0:
-                # use tau0[0] as representative for M/L and index.
-                for ll in range(len(INDICES)):
-                    # ASDF
-                    tree_lick.update({INDICES[ll]+'_'+str(zz): LICK[:,ll]})
-
-                col1 = fits.Column(name='ms_'+str(zz), format='E', unit='Msun', array=ms)
-                tree_ML.update({'ms_'+str(zz): ms})
-                col2 = fits.Column(name='Ls_'+str(zz), format='E', unit='Lsun', array=Ls)
-                tree_ML.update({'Ls_'+str(zz): Ls})
-                col3 = fits.Column(name='fm_'+str(zz), format='E', unit='', array=mlost)
-                tree_ML.update({'frac_mass_survive_'+str(zz): mlost})
-                col4 = fits.Column(name='tau_'+str(zz), format='E', unit='Gyr', array=tau_age)
-                tree_ML.update({'realtau_'+str(zz): ms})
-
-    # Write;
-    for aa in range(len(age)):
-        tree.update({'realtau%d(Gyr)'%(aa): tau_age[aa]})
-    for aa in range(len(age)):
-        tree.update({'realage%d(Gyr)'%(aa): age_age[aa]})
-    for aa in range(len(age)):
-        tree.update({'age%d'%(aa): age[aa]})
-    for aa in range(len(Z)):
-        tree.update({'Z%d'%(aa): Z[aa]})
-    for aa in range(len(tau0)):
-        tree.update({'tau0%d'%(aa): tau0[aa]})
-
-    # Index, Mass-to-light;
-    tree.update({'spec' : tree_spec})
-    tree.update({'ML' : tree_ML})
-    tree.update({'lick' : tree_lick})
-
-    # Save
-    af = asdf.AsdfFile(tree)
-    af.write_to(DIR_TMP + file_out, all_array_compression='zlib')
-
diff --git a/build/lib/gsf/maketmp_z0_tau.py b/build/lib/gsf/maketmp_z0_tau.py
deleted file mode 100644
index fa66b40..0000000
--- a/build/lib/gsf/maketmp_z0_tau.py
+++ /dev/null
@@ -1,187 +0,0 @@
-import numpy as np
-from astropy.io import fits
-import matplotlib.pyplot as plt
-import os
-import sys
-from astropy.io import ascii
-
-from .function import get_ind
-
-INDICES = ['G4300', 'Mgb', 'Fe5270', 'Fe5335', 'NaD', 'Hb', 'Fe4668', 'Fe5015', 'Fe5709', 'Fe5782', 'Mg1', 'Mg2', 'TiO1', 'TiO2']
-
-
-def get_lognorm(t, ltau0, T0=-10):
-    A   = 1
-    tau0= 10**ltau0
-    SFR = t * 0 + 1e-20
-    conlogn = (t>0)
-    SFR[conlogn] = A / np.sqrt(2*np.pi*tau0**2) * np.exp(-(np.log(t[conlogn])-T0)**2/(2*tau0**2)) / t[conlogn]
-    return SFR
-
-
-def make_tmp_z0(MB, lammin=100, lammax=160000, Zforce=None): 
-    '''
-    This is for the preparation of default template, with FSPS, at z=0.
-    Should be run before SED fitting.
-
-    Parameters
-    ----------
-    :class:`gsf.fitting.Mainbody` : class
-        Mainbody class, that contains attributes.
-
-    lammin : float, optional
-        Minimum value of the rest-frame wavelength of the template, in AA.
-
-    lammax : float, optional
-        Maximum value of the rest-frame wavelength of the template, in AA.
-
-    tau_lim : float, optional
-        Maximum value of tau of the template, in Gyr. Tau smaller than this 
-        value would be approximated by SSP.
-    '''
-    import asdf
-    import fsps
-    import gsf
-    
-    nimf = MB.nimf
-    age = MB.ageparam
-    fneb = MB.fneb
-    DIR_TMP = MB.DIR_TMP
-    Na = len(age)
-
-    tau = MB.tau
-    sfh = MB.SFH_FORM
-
-    if not Zforce == None:
-        file_out = 'spec_all_Z%.1f.asdf'%Zforce
-    else:
-        file_out = 'spec_all.asdf'
-    Z = MB.Zall
-    NZ = len(Z)
-    
-    # Current age in Gyr;
-    age_univ = MB.cosmo.age(0).value
-
-    NZ = len(Z)
-    Nt = len(tau)
-    Na = len(age)
-
-    ms = np.zeros(Na, dtype='float')
-    Ls = np.zeros(Na, dtype='float')
-
-    print('#######################################')
-    print('Making templates at z=0, IMF=%d'%(nimf))
-    print('#######################################')
-    col01 = [] # For M/L ratio.
-    col02 = [] # For templates
-    col05 = [] # For spectral indices.
-
-    tree_spec = {}
-    tree_ML = {}
-    tree_lick = {}
-
-    print('tau is the width of each age bin.')
-    flagz = True
-    for zz in range(len(Z)):
-        if not Zforce == None and Z[zz] != Zforce:
-            continue
-        for ss in range(len(tau)):
-            if 10**tau[ss]<0.01:
-                # then do SSP
-                print('!! tau is <0.01Gyr. SSP is applied. !!') # This corresponds to the min-tau of fsps.
-                sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=0, logzsol=Z[zz], dust_type=2, dust2=0.0) # Lsun/Hz
-                if fneb == 1:
-                    esptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=0, logzsol=Z[zz], dust_type=2, dust2=0.0, add_neb_emission=1) # Lsun/Hz
-            elif sfh<5:
-                sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=sfh, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=10**tau[ss], const=0, sf_start=0, sf_trunc=0, tburst=13, fburst=0) # Lsun/Hz
-                if fneb == 1:
-                    esptmp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, sfh=sfh, logzsol=Z[zz], dust_type=2, dust2=0.0, tau=10**tau[ss], const=0, sf_start=0, sf_trunc=0, tburst=13, fburst=0, add_neb_emission=1) # Lsun/Hz
-            elif sfh==6: # Custom SFH
-                sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=3, imf_type=nimf, sfh=3, dust_type=2, dust2=0.0)
-                if fneb == 1:
-                    sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=3, imf_type=nimf, sfh=3, dust_type=2, dust2=0.0, add_neb_emission=1)
-                print('Log normal is used. !!')
-
-            print('Z:%d/%d, t:%d/%d, %s, %s'%(zz+1, len(Z), ss+1, len(tau), sp.libraries[0].decode("utf-8") , sp.libraries[1].decode("utf-8")))
-            ms = np.zeros(Na)
-            Ls = np.zeros(Na)
-            LICK = np.zeros((Na,len(INDICES)), dtype='float')
-            mlost = np.zeros(Na, dtype='float')
-
-            for tt in range(len(age)):
-                if sfh==6: # Tabular SFH.
-                    tuniv_hr  = np.arange(0,age_univ,0.01) # in Gyr
-                    T0 = np.log(10**age[tt])
-                    sfh_hr_in = get_lognorm(tuniv_hr, tau[ss], T0) # tau in log Gyr
-                    zh_hr_in  = tuniv_hr*0 + 10**Z[zz] # metallicity is constant
-                    sp.set_tabular_sfh(tuniv_hr, sfh_hr_in, zh_hr_in)
-                    wave0, flux0 = sp.get_spectrum(tage=age_univ, peraa=True) # if peraa=True, in unit of L/AA
-                else:
-                    wave0, flux0 = sp.get_spectrum(tage=10**age[tt], peraa=True) # if peraa=True, in unit of L/AA
-
-                con = (wave0>lammin) & (wave0<lammax)
-                wave, flux = wave0[con], flux0[con]
-                ms[tt] = sp.stellar_mass
-                if np.isnan(ms[tt]):
-                    print('Error at tau element at',tt)
-                    sys.exit()
-
-                Ls[tt] = 10**sp.log_lbol
-                LICK[tt,:] = get_ind(wave, flux)
-                mlost[tt] = sp.stellar_mass / sp.formed_mass
-
-                if fneb and tt == 0 and ss == 0:
-                    esptmp.params['gas_logz'] = Z[zz] # gas metallicity, assuming = Zstel
-                    # Loop within logU;
-                    for nlogU, logUtmp in enumerate(MB.logUs):
-                        esptmp.params['gas_logu'] = logUtmp
-                        esp = esptmp
-                        ewave0, eflux0 = esp.get_spectrum(tage=0.001, peraa=True)
-                        con = (ewave0>lammin) & (ewave0<lammax)
-                        flux_nebular = eflux0[con]-flux
-                        tree_spec.update({'flux_nebular_Z%d'%zz+'_logU%d'%nlogU: flux_nebular})
-
-                if flagz and ss == 0 and tt == 0:
-                    # ASDF Big tree;
-                    # Create header;
-                    tree = {
-                        'isochrone': '%s'%(sp.libraries[0].decode("utf-8")),
-                        'library': '%s'%(sp.libraries[1].decode("utf-8")),
-                        'nimf': nimf,
-                        'version_gsf': gsf.__version__
-                    }
-                    if fneb == 1:
-                        tree.update({'logUMIN': MB.logUMIN})
-                        tree.update({'logUMAX': MB.logUMAX})
-                        tree.update({'DELlogU': MB.DELlogU})
-
-                    tree_spec.update({'wavelength': wave})
-                    flagz = False
-
-                tree_spec.update({'fspec_'+str(zz)+'_'+str(ss)+'_'+str(tt): flux})
-
-            for ll in range(len(INDICES)):
-                # ASDF
-                tree_lick.update({INDICES[ll]+'_'+str(zz)+'_'+str(ss): LICK[:,ll]})
-
-            tree_ML.update({'ms_'+str(zz)+'_'+str(ss): ms})
-            tree_ML.update({'Ls_'+str(zz)+'_'+str(ss): Ls})
-            tree_ML.update({'frac_mass_survive_'+str(zz)+'_'+str(ss): mlost})
-
-
-    # Write;
-    for aa in range(len(tau)):
-        tree.update({'tau%d'%(aa): tau[aa]})
-    for aa in range(len(age)):
-        tree.update({'age%d'%(aa): age[aa]})
-    for aa in range(len(Z)):
-        tree.update({'Z%d'%(aa): Z[aa]})
-
-    # Index, Mass-to-light;
-    tree.update({'spec' : tree_spec})
-    tree.update({'ML' : tree_ML})
-    tree.update({'lick' : tree_lick})
-
-    # Save
-    af = asdf.AsdfFile(tree)
-    af.write_to(DIR_TMP + file_out, all_array_compression='zlib')
diff --git a/build/lib/gsf/minimizer.py b/build/lib/gsf/minimizer.py
deleted file mode 100644
index 35ea50a..0000000
--- a/build/lib/gsf/minimizer.py
+++ /dev/null
@@ -1,2428 +0,0 @@
-"""Simple minimizer is a wrapper around scipy.leastsq, allowing a user to build
-a fitting model as a function of general purpose Fit Parameters that can be
-fixed or varied, bounded, and written as a simple expression of other Fit
-Parameters.
-
-The user sets up a model in terms of instance of Parameters and writes a
-function-to-be-minimized (residual function) in terms of these Parameters.
-
-Original copyright:
-   Copyright (c) 2011 Matthew Newville, The University of Chicago
-
-See LICENSE for more complete authorship information and license.
-
-"""
-from collections import namedtuple
-from copy import deepcopy
-import multiprocessing
-import numbers
-import warnings
-
-import numpy as np
-from numpy import ndarray, ones_like, sqrt
-from numpy.linalg import inv
-from numpy.linalg import LinAlgError
-from scipy.optimize import basinhopping as scipy_basinhopping
-from scipy.optimize import brute as scipy_brute
-from scipy.optimize import differential_evolution
-from scipy.optimize import dual_annealing as scipy_dual_annealing
-from scipy.optimize import least_squares
-from scipy.optimize import leastsq as scipy_leastsq
-from scipy.optimize import minimize as scipy_minimize
-from scipy.optimize import shgo as scipy_shgo
-from scipy.sparse import issparse
-from scipy.sparse.linalg import LinearOperator
-from scipy.stats import cauchy as cauchy_dist
-from scipy.stats import norm as norm_dist
-from scipy.version import version as scipy_version
-import uncertainties
-
-from lmfit._ampgo import ampgo
-from lmfit.parameter import Parameter, Parameters
-from lmfit.printfuncs import fitreport_html_table
-
-# check for EMCEE
-try:
-    import emcee
-    from emcee.autocorr import AutocorrError
-    HAS_EMCEE = int(emcee.__version__[0]) >= 3
-except ImportError:
-    HAS_EMCEE = False
-
-# check for pandas
-try:
-    import pandas as pd
-    from pandas import isnull
-    HAS_PANDAS = True
-except ImportError:
-    HAS_PANDAS = False
-    isnull = np.isnan
-
-# check for numdifftools
-try:
-    import numdifftools as ndt
-    HAS_NUMDIFFTOOLS = True
-except ImportError:
-    HAS_NUMDIFFTOOLS = False
-
-# check for dill
-try:
-    import dill   # noqa: F401
-    HAS_DILL = True
-except ImportError:
-    HAS_DILL = False
-
-
-# define the namedtuple here so pickle will work with the MinimizerResult
-Candidate = namedtuple('Candidate', ['params', 'score'])
-
-
-def asteval_with_uncertainties(*vals, **kwargs):
-    """Calculate object value, given values for variables.
-
-    This is used by the uncertainties package to calculate the
-    uncertainty in an object even with a complicated expression.
-
-    """
-    _obj = kwargs.get('_obj', None)
-    _pars = kwargs.get('_pars', None)
-    _names = kwargs.get('_names', None)
-    _asteval = _pars._asteval
-    if (_obj is None or _pars is None or _names is None or
-            _asteval is None or _obj._expr_ast is None):
-        return 0
-    for val, name in zip(vals, _names):
-        _asteval.symtable[name] = val
-    return _asteval.eval(_obj._expr_ast)
-
-
-wrap_ueval = uncertainties.wrap(asteval_with_uncertainties)
-
-
-def eval_stderr(obj, uvars, _names, _pars):
-    """Evaluate uncertainty and set .stderr for a parameter `obj`.
-
-    Given the uncertain values `uvars` (a list of uncertainties.ufloats), a
-    list of parameter names that matches uvars, and a dict of param objects,
-    keyed by name.
-
-    This uses the uncertainties package wrapped function to evaluate the
-    uncertainty for an arbitrary expression (in obj._expr_ast) of parameters.
-
-    """
-    if not isinstance(obj, Parameter) or getattr(obj, '_expr_ast', None) is None:
-        return
-    uval = wrap_ueval(*uvars, _obj=obj, _names=_names, _pars=_pars)
-    try:
-        obj.stderr = uval.std_dev
-    except Exception:
-        obj.stderr = 0
-
-
-class MinimizerException(Exception):
-    """General Purpose Exception."""
-
-    def __init__(self, msg):
-        Exception.__init__(self)
-        self.msg = msg
-
-    def __str__(self):
-        return "{}".format(self.msg)
-
-
-class AbortFitException(MinimizerException):
-    """Raised when a fit is aborted by the user."""
-
-    pass
-
-
-SCALAR_METHODS = {'nelder': 'Nelder-Mead',
-                  'powell': 'Powell',
-                  'cg': 'CG',
-                  'bfgs': 'BFGS',
-                  'newton': 'Newton-CG',
-                  'lbfgsb': 'L-BFGS-B',
-                  'l-bfgsb': 'L-BFGS-B',
-                  'tnc': 'TNC',
-                  'cobyla': 'COBYLA',
-                  'slsqp': 'SLSQP',
-                  'dogleg': 'dogleg',
-                  'trust-ncg': 'trust-ncg',
-                  'differential_evolution': 'differential_evolution',
-                  'trust-constr': 'trust-constr',
-                  'trust-exact': 'trust-exact',
-                  'trust-krylov': 'trust-krylov'}
-
-
-def reduce_chisquare(r):
-    """Reduce residual array to scalar (chi-square).
-
-    Calculate the chi-square value from the residual array `r`: (r*r).sum()
-
-    Parameters
-    ----------
-    r : numpy.ndarray
-        Residual array.
-
-    Returns
-    -------
-    float
-        Chi-square calculated from the residual array
-
-    """
-    return (r*r).sum()
-
-
-def reduce_negentropy(r):
-    """Reduce residual array to scalar (negentropy).
-
-    Reduce residual array `r` to scalar using negative entropy and the normal
-    (Gaussian) probability distribution of `r` as pdf:
-
-       (norm.pdf(r)*norm.logpdf(r)).sum()
-
-    since pdf(r) = exp(-r*r/2)/sqrt(2*pi), this is
-       ((r*r/2 - log(sqrt(2*pi))) * exp(-r*r/2)).sum()
-
-    Parameters
-    ----------
-    r : numpy.ndarray
-        Residual array.
-
-    Returns
-    -------
-    float
-        Negative entropy value calculated from the residual array
-
-    """
-    return (norm_dist.pdf(r)*norm_dist.logpdf(r)).sum()
-
-
-def reduce_cauchylogpdf(r):
-    """Reduce residual array to scalar (cauchylogpdf).
-
-    Reduce residual array `r` to scalar using negative log-likelihood and a
-    Cauchy (Lorentzian) distribution of `r`:
-
-       -scipy.stats.cauchy.logpdf(r)
-
-    (where the Cauchy pdf = 1/(pi*(1+r*r))). This gives greater
-    suppression of outliers compared to normal sum-of-squares.
-
-    Parameters
-    ----------
-    r : numpy.ndarray
-        Residual array.
-
-    Returns
-    -------
-    float
-        Negative entropy value calculated from the residual array
-
-    """
-    return -cauchy_dist.logpdf(r).sum()
-
-
-class MinimizerResult:
-    r"""The results of a minimization.
-
-    Minimization results include data such as status and error messages,
-    fit statistics, and the updated (i.e., best-fit) parameters themselves
-    in the :attr:`params` attribute.
-
-    The list of (possible) `MinimizerResult` attributes is given below:
-
-    Attributes
-    ----------
-    params : :class:`~lmfit.parameter.Parameters`
-        The best-fit parameters resulting from the fit.
-    status : int
-        Termination status of the optimizer. Its value depends on the
-        underlying solver. Refer to `message` for details.
-    var_names : list
-        Ordered list of variable parameter names used in optimization, and
-        useful for understanding the values in :attr:`init_vals` and
-        :attr:`covar`.
-    covar : numpy.ndarray
-        Covariance matrix from minimization, with rows and columns
-        corresponding to :attr:`var_names`.
-    init_vals : list
-        List of initial values for variable parameters using :attr:`var_names`.
-    init_values : dict
-        Dictionary of initial values for variable parameters.
-    nfev : int
-        Number of function evaluations.
-    success : bool
-        True if the fit succeeded, otherwise False.
-    errorbars : bool
-        True if uncertainties were estimated, otherwise False.
-    message : str
-        Message about fit success.
-    ier : int
-        Integer error value from :scipydoc:`optimize.leastsq` (`leastsq` only).
-    lmdif_message : str
-        Message from :scipydoc:`optimize.leastsq` (`leastsq` only).
-    nvarys : int
-        Number of variables in fit: :math:`N_{\rm varys}`.
-    ndata : int
-        Number of data points: :math:`N`.
-    nfree : int
-        Degrees of freedom in fit: :math:`N - N_{\rm varys}`.
-    residual : numpy.ndarray
-        Residual array :math:`{\rm Resid_i}`. Return value of the objective
-        function when using the best-fit values of the parameters.
-    chisqr : float
-        Chi-square: :math:`\chi^2 = \sum_i^N [{\rm Resid}_i]^2`.
-    redchi : float
-        Reduced chi-square:
-        :math:`\chi^2_{\nu}= {\chi^2} / {(N - N_{\rm varys})}`.
-    aic : float
-        Akaike Information Criterion statistic:
-        :math:`N \ln(\chi^2/N) + 2 N_{\rm varys}`.
-    bic : float
-        Bayesian Information Criterion statistic:
-        :math:`N \ln(\chi^2/N) + \ln(N) N_{\rm varys}`.
-    flatchain : pandas.DataFrame
-        A flatchain view of the sampling chain from the `emcee` method.
-
-    Methods
-    -------
-    show_candidates
-        Pretty_print() representation of candidates from the `brute` method.
-
-    """
-
-    def __init__(self, **kws):
-        for key, val in kws.items():
-            setattr(self, key, val)
-
-    @property
-    def flatchain(self):
-        """Show flatchain view of the sampling chain from `emcee` method."""
-        if hasattr(self, 'chain'):
-            if HAS_PANDAS:
-                if len(self.chain.shape) == 4:
-                    return pd.DataFrame(self.chain[0, ...].reshape((-1, self.nvarys)),
-                                        columns=self.var_names)
-                elif len(self.chain.shape) == 3:
-                    return pd.DataFrame(self.chain.reshape((-1, self.nvarys)),
-                                        columns=self.var_names)
-            else:
-                raise NotImplementedError('Please install Pandas to see the '
-                                          'flattened chain')
-        else:
-            return None
-
-    def show_candidates(self, candidate_nmb='all'):
-        """Show pretty_print() representation of candidates from `brute` method.
-
-        Showing all stored candidates (default) or the specified candidate-#
-        from the `brute` method.
-
-        Parameters
-        ----------
-        candidate_nmb : int or 'all'
-            The candidate-number to show using the :meth:`pretty_print` method.
-
-        """
-        if hasattr(self, 'candidates'):
-            if candidate_nmb == 'all':
-                for i, candidate in enumerate(self.candidates):
-                    print("\nCandidate #{}, chisqr = "
-                          "{:.3f}".format(i+1, candidate.score))
-                    candidate.params.pretty_print()
-            elif (candidate_nmb < 1 or candidate_nmb > len(self.candidates)):
-                raise ValueError("'candidate_nmb' should be between 1 and {}."
-                                 .format(len(self.candidates)))
-            else:
-                candidate = self.candidates[candidate_nmb-1]
-                print("\nCandidate #{}, chisqr = "
-                      "{:.3f}".format(candidate_nmb, candidate.score))
-                candidate.params.pretty_print()
-
-    def _calculate_statistics(self):
-        """Calculate the fitting statistics."""
-        self.nvarys = len(self.init_vals)
-        if isinstance(self.residual, ndarray):
-            self.chisqr = (self.residual**2).sum()
-            self.ndata = len(self.residual)
-            self.nfree = self.ndata - self.nvarys
-        else:
-            self.chisqr = self.residual
-            self.ndata = 1
-            self.nfree = 1
-        self.redchi = self.chisqr / max(1, self.nfree)
-        # this is -2*loglikelihood
-        _neg2_log_likel = self.ndata * np.log(self.chisqr / self.ndata)
-        self.aic = _neg2_log_likel + 2 * self.nvarys
-        self.bic = _neg2_log_likel + np.log(self.ndata) * self.nvarys
-
-    def _repr_html_(self, show_correl=True, min_correl=0.1):
-        """Returns a HTML representation of parameters data."""
-        return fitreport_html_table(self, show_correl=show_correl,
-                                    min_correl=min_correl)
-
-
-class Minimizer:
-    """A general minimizer for curve fitting and optimization."""
-
-    _err_nonparam = ("params must be a minimizer.Parameters() instance or list "
-                     "of Parameters()")
-    _err_maxfev = ("Too many function calls (max set to %i)!  Use:"
-                   " minimize(func, params, ..., maxfev=NNN)"
-                   "or set leastsq_kws['maxfev']  to increase this maximum.")
-
-    def __init__(self, userfcn, params, fcn_args=None, fcn_kws=None,
-                 iter_cb=None, scale_covar=True, nan_policy='raise',
-                 reduce_fcn=None, calc_covar=True, **kws):
-        """
-        Parameters
-        ----------
-        userfcn : callable
-            Objective function that returns the residual (difference between
-            model and data) to be minimized in a least-squares sense.  This
-            function must have the signature::
-
-                userfcn(params, *fcn_args, **fcn_kws)
-
-        params : :class:`~lmfit.parameter.Parameters`
-            Contains the Parameters for the model.
-        fcn_args : tuple, optional
-            Positional arguments to pass to `userfcn`.
-        fcn_kws : dict, optional
-            Keyword arguments to pass to `userfcn`.
-        iter_cb : callable, optional
-            Function to be called at each fit iteration. This function should
-            have the signature::
-
-                iter_cb(params, iter, resid, *fcn_args, **fcn_kws)
-
-            where `params` will have the current parameter values, `iter`
-            the iteration number, `resid` the current residual array, and `*fcn_args`
-            and `**fcn_kws` are passed to the objective function.
-        scale_covar : bool, optional
-            Whether to automatically scale the covariance matrix (default is True).
-        nan_policy : str, optional
-            Specifies action if `userfcn` (or a Jacobian) returns NaN
-            values. One of:
-
-            - 'raise' : a `ValueError` is raised
-            - 'propagate' : the values returned from `userfcn` are un-altered
-            - 'omit' : non-finite values are filtered
-
-        reduce_fcn : str or callable, optional
-            Function to convert a residual array to a scalar value for the scalar
-            minimizers. Optional values are (where `r` is the residual array):
-
-            - None : sum of squares of residual [default]
-
-               = (r*r).sum()
-
-            - 'negentropy' : neg entropy, using normal distribution
-
-               = rho*log(rho).sum()`, where  rho = exp(-r*r/2)/(sqrt(2*pi))
-
-            - 'neglogcauchy': neg log likelihood, using Cauchy distribution
-
-               = -log(1/(pi*(1+r*r))).sum()
-
-            - callable : must take one argument (`r`) and return a float.
-
-        calc_covar : bool, optional
-            Whether to calculate the covariance matrix (default is True) for
-            solvers other than `leastsq` and `least_squares`. Requires the
-            `numdifftools` package to be installed.
-        **kws : dict, optional
-            Options to pass to the minimizer being used.
-
-        Notes
-        -----
-        The objective function should return the value to be minimized. For
-        the Levenberg-Marquardt algorithm from :meth:`leastsq` or
-        :meth:`least_squares`, this returned value must be an array, with a
-        length greater than or equal to the number of fitting variables in
-        the model. For the other methods, the return value can either be a
-        scalar or an array. If an array is returned, the sum of squares of
-        the array will be sent to the underlying fitting method, effectively
-        doing a least-squares optimization of the return values. If the
-        objective function returns non-finite values then a `ValueError`
-        will be raised because the underlying solvers cannot deal with them.
-
-        A common use for the `fcn_args` and `fcn_kws` would be to pass in
-        other data needed to calculate the residual, including such things
-        as the data array, dependent variable, uncertainties in the data,
-        and other data structures for the model calculation.
-
-        """
-        self.userfcn = userfcn
-        self.userargs = fcn_args
-        if self.userargs is None:
-            self.userargs = []
-
-        self.userkws = fcn_kws
-        if self.userkws is None:
-            self.userkws = {}
-        self.kws = kws
-        self.iter_cb = iter_cb
-        self.calc_covar = calc_covar
-        self.scale_covar = scale_covar
-        self.nfev = 0
-        self.nfree = 0
-        self.ndata = 0
-        self.ier = 0
-        self._abort = False
-        self.success = True
-        self.errorbars = False
-        self.message = None
-        self.lmdif_message = None
-        self.chisqr = None
-        self.redchi = None
-        self.covar = None
-        self.residual = None
-        self.reduce_fcn = reduce_fcn
-        self.params = params
-        self.jacfcn = None
-        self.nan_policy = nan_policy
-
-    @property
-    def values(self):
-        """Return Parameter values in a simple dictionary."""
-        return {name: p.value for name, p in self.result.params.items()}
-
-    def __residual(self, fvars, apply_bounds_transformation=True):
-        """Residual function used for least-squares fit.
-
-        With the new, candidate values of `fvars` (the fitting variables),
-        this evaluates all parameters, including setting bounds and
-        evaluating constraints, and then passes those to the user-supplied
-        function to calculate the residual.
-
-        Parameters
-        ----------
-        fvars : numpy.ndarray
-            Array of new parameter values suggested by the minimizer.
-        apply_bounds_transformation : bool, optional
-            Whether to apply lmfits parameter transformation to constrain
-            parameters (default is True). This is needed for solvers without
-            inbuilt support for bounds.
-
-        Returns
-        -------
-        residual : numpy.ndarray
-             The evaluated function values for given `fvars`.
-
-        """
-        params = self.result.params
-
-        if fvars.shape == ():
-            fvars = fvars.reshape((1,))
-
-        if apply_bounds_transformation:
-            for name, val in zip(self.result.var_names, fvars):
-                params[name].value = params[name].from_internal(val)
-        else:
-            for name, val in zip(self.result.var_names, fvars):
-                params[name].value = val
-        params.update_constraints()
-
-        self.result.nfev += 1
-
-        out = self.userfcn(params, *self.userargs, **self.userkws)
-
-        if callable(self.iter_cb):
-            abort = self.iter_cb(params, self.result.nfev, out,
-                                 *self.userargs, **self.userkws)
-            self._abort = self._abort or abort
-
-        if self._abort:
-            self.result.residual = out
-            self.result.aborted = True
-            self.result.message = "Fit aborted by user callback. Could not estimate error-bars."
-            self.result.success = False
-            raise AbortFitException("fit aborted by user.")
-        else:
-            return _nan_policy(np.asarray(out).ravel(),
-                               nan_policy=self.nan_policy)
-
-    def __jacobian(self, fvars):
-        """Return analytical jacobian to be used with Levenberg-Marquardt.
-
-        modified 02-01-2012 by Glenn Jones, Aberystwyth University
-        modified 06-29-2015 M Newville to apply gradient scaling for
-        bounded variables (thanks to JJ Helmus, N Mayorov)
-
-        """
-        pars = self.result.params
-        grad_scale = ones_like(fvars)
-        for ivar, name in enumerate(self.result.var_names):
-            val = fvars[ivar]
-            pars[name].value = pars[name].from_internal(val)
-            grad_scale[ivar] = pars[name].scale_gradient(val)
-
-        pars.update_constraints()
-
-        # compute the jacobian for "internal" unbounded variables,
-        # then rescale for bounded "external" variables.
-        jac = self.jacfcn(pars, *self.userargs, **self.userkws)
-        jac = _nan_policy(jac, nan_policy=self.nan_policy)
-
-        if self.col_deriv:
-            jac = (jac.transpose()*grad_scale).transpose()
-        else:
-            jac *= grad_scale
-        return jac
-
-    def penalty(self, fvars):
-        """Penalty function for scalar minimizers.
-
-        Parameters
-        ----------
-        fvars : numpy.ndarray
-            Array of values for the variable parameters.
-
-        Returns
-        -------
-        r : float
-            The evaluated user-supplied objective function.
-
-            If the objective function is an array of size greater than 1,
-            use the scalar returned by `self.reduce_fcn`.  This defaults
-            to sum-of-squares, but can be replaced by other options.
-
-        """
-        if self.result.method in ['brute', 'shgo', 'dual_annealing']:
-            apply_bounds_transformation = False
-        else:
-            apply_bounds_transformation = True
-
-        r = self.__residual(fvars, apply_bounds_transformation)
-        if isinstance(r, ndarray) and r.size > 1:
-            r = self.reduce_fcn(r)
-            if isinstance(r, ndarray) and r.size > 1:
-                r = r.sum()
-        return r
-
-    def prepare_fit(self, params=None):
-        """Prepare parameters for fitting.
-
-        Prepares and initializes model and Parameters for subsequent
-        fitting. This routine prepares the conversion of :class:`Parameters`
-        into fit variables, organizes parameter bounds, and parses, "compiles"
-        and checks constrain expressions.   The method also creates and returns
-        a new instance of a :class:`MinimizerResult` object that contains the
-        copy of the Parameters that will actually be varied in the fit.
-
-        Parameters
-        ----------
-        params : :class:`~lmfit.parameter.Parameters`, optional
-            Contains the Parameters for the model; if None, then the
-            Parameters used to initialize the Minimizer object are used.
-
-        Returns
-        -------
-        :class:`MinimizerResult`
-
-        Notes
-        -----
-        This method is called directly by the fitting methods, and it is
-        generally not necessary to call this function explicitly.
-
-        .. versionchanged:: 0.9.0
-            Return value changed to :class:`MinimizerResult`.
-
-        """
-        # determine which parameters are actually variables
-        # and which are defined expressions.
-        self.result = MinimizerResult()
-        result = self.result
-        if params is not None:
-            self.params = params
-        if isinstance(self.params, Parameters):
-            result.params = deepcopy(self.params)
-        elif isinstance(self.params, (list, tuple)):
-            result.params = Parameters()
-            for par in self.params:
-                if not isinstance(par, Parameter):
-                    raise MinimizerException(self._err_nonparam)
-                else:
-                    result.params[par.name] = par
-        elif self.params is None:
-            raise MinimizerException(self._err_nonparam)
-
-        # determine which parameters are actually variables
-        # and which are defined expressions.
-        result.var_names = []  # note that this *does* belong to self...
-        result.init_vals = []
-        result.params.update_constraints()
-        result.nfev = 0
-        result.errorbars = False
-        result.aborted = False
-        for name, par in self.result.params.items():
-            par.stderr = None
-            par.correl = None
-            if par.expr is not None:
-                par.vary = False
-            if par.vary:
-                result.var_names.append(name)
-                result.init_vals.append(par.setup_bounds())
-
-            par.init_value = par.value
-            if par.name is None:
-                par.name = name
-        result.nvarys = len(result.var_names)
-        result.init_values = {n: v for n, v in zip(result.var_names,
-                                                   result.init_vals)}
-
-        # set up reduce function for scalar minimizers
-        #    1. user supplied callable
-        #    2. string starting with 'neglogc' or 'negent'
-        #    3. sum of squares
-        if not callable(self.reduce_fcn):
-            if isinstance(self.reduce_fcn, str):
-                if self.reduce_fcn.lower().startswith('neglogc'):
-                    self.reduce_fcn = reduce_cauchylogpdf
-                elif self.reduce_fcn.lower().startswith('negent'):
-                    self.reduce_fcn = reduce_negentropy
-            if self.reduce_fcn is None:
-                self.reduce_fcn = reduce_chisquare
-        return result
-
-    def unprepare_fit(self):
-        """Clean fit state, so that subsequent fits need to call prepare_fit().
-
-        removes AST compilations of constraint expressions.
-
-        """
-        pass
-
-    def _calculate_covariance_matrix(self, fvars):
-        """Calculate the covariance matrix.
-
-        The `numdiftoools` package is used to estimate the Hessian matrix, and
-        the covariance matrix is calculated as:
-
-        .. math::
-
-            cov_x = inverse(Hessian) * 2.0
-
-        Parameters
-        ----------
-        fvars : numpy.ndarray
-            Array of the optimal internal, freely variable parameters.
-
-        Returns
-        -------
-        cov_x : numpy.ndarray or None
-            Covariance matrix if successful, otherwise None.
-
-        """
-        warnings.filterwarnings(action="ignore", module="scipy",
-                                message="^internal gelsd")
-
-        nfev = deepcopy(self.result.nfev)
-        try:
-            Hfun = ndt.Hessian(self.penalty)
-            hessian_ndt = Hfun(fvars)
-            cov_x = inv(hessian_ndt) * 2.0
-        except (LinAlgError, ValueError):
-            return None
-        finally:
-            self.result.nfev = nfev
-
-        return cov_x
-
-    def _int2ext_cov_x(self, cov_int, fvars):
-        """Transform covariance matrix to external parameter space.
-
-        It makes use of the gradient scaling according to the MINUIT recipe:
-
-            cov_ext = np.dot(grad.T, grad) * cov_int
-
-        Parameters
-        ----------
-        cov_int : numpy.ndarray
-            Covariance matrix in the internal parameter space.
-        fvars : numpy.ndarray
-            Array of the optimal internal, freely variable, parameter values.
-
-        Returns
-        -------
-        cov_ext : numpy.ndarray
-            Covariance matrix, transformed to external parameter space.
-
-        """
-        g = [self.result.params[name].scale_gradient(fvars[i]) for i, name in
-             enumerate(self.result.var_names)]
-        grad2d = np.atleast_2d(g)
-        grad = np.dot(grad2d.T, grad2d)
-
-        cov_ext = cov_int * grad
-        return cov_ext
-
-    def _calculate_uncertainties_correlations(self):
-        """Calculate parameter uncertainties and correlations."""
-        self.result.errorbars = True
-
-        if self.scale_covar:
-            self.result.covar *= self.result.redchi
-
-        vbest = np.atleast_1d([self.result.params[name].value for name in
-                               self.result.var_names])
-
-        has_expr = False
-        for par in self.result.params.values():
-            par.stderr, par.correl = 0, None
-            has_expr = has_expr or par.expr is not None
-
-        for ivar, name in enumerate(self.result.var_names):
-            par = self.result.params[name]
-            par.stderr = sqrt(self.result.covar[ivar, ivar])
-            par.correl = {}
-            try:
-                self.result.errorbars = self.result.errorbars and (par.stderr > 0.0)
-                for jvar, varn2 in enumerate(self.result.var_names):
-                    if jvar != ivar:
-                        par.correl[varn2] = (self.result.covar[ivar, jvar] /
-                                             (par.stderr * sqrt(self.result.covar[jvar, jvar])))
-            except ZeroDivisionError:
-                self.result.errorbars = False
-
-        if has_expr:
-            try:
-                uvars = uncertainties.correlated_values(vbest, self.result.covar)
-            except (LinAlgError, ValueError):
-                uvars = None
-
-            # for uncertainties on constrained parameters, use the calculated
-            # "correlated_values", evaluate the uncertainties on the constrained
-            # parameters and reset the Parameters to best-fit value
-            if uvars is not None:
-                for par in self.result.params.values():
-                    eval_stderr(par, uvars, self.result.var_names, self.result.params)
-                # restore nominal values
-                for v, nam in zip(uvars, self.result.var_names):
-                    self.result.params[nam].value = v.nominal_value
-
-    def scalar_minimize(self, method='Nelder-Mead', params=None, **kws):
-        """Scalar minimization using :scipydoc:`optimize.minimize`.
-
-        Perform fit with any of the scalar minimization algorithms supported by
-        :scipydoc:`optimize.minimize`. Default argument values are:
-
-        +-------------------------+-----------------+-----------------------------------------------------+
-        | :meth:`scalar_minimize` | Default Value   | Description                                         |
-        | arg                     |                 |                                                     |
-        +=========================+=================+=====================================================+
-        |   method                | ``Nelder-Mead`` | fitting method                                      |
-        +-------------------------+-----------------+-----------------------------------------------------+
-        |   tol                   | 1.e-7           | fitting and parameter tolerance                     |
-        +-------------------------+-----------------+-----------------------------------------------------+
-        |   hess                  | None            | Hessian of objective function                       |
-        +-------------------------+-----------------+-----------------------------------------------------+
-
-
-        Parameters
-        ----------
-        method : str, optional
-            Name of the fitting method to use. One of:
-
-            - 'Nelder-Mead' (default)
-            - 'L-BFGS-B'
-            - 'Powell'
-            - 'CG'
-            - 'Newton-CG'
-            - 'COBYLA'
-            - 'BFGS'
-            - 'TNC'
-            - 'trust-ncg'
-            - 'trust-exact'
-            - 'trust-krylov'
-            - 'trust-constr'
-            - 'dogleg'
-            - 'SLSQP'
-            - 'differential_evolution'
-
-        params : :class:`~lmfit.parameter.Parameters`, optional
-           Parameters to use as starting point.
-        **kws : dict, optional
-            Minimizer options pass to :scipydoc:`optimize.minimize`.
-
-        Returns
-        -------
-        :class:`MinimizerResult`
-            Object containing the optimized parameter and several
-            goodness-of-fit statistics.
-
-
-        .. versionchanged:: 0.9.0
-           Return value changed to :class:`MinimizerResult`.
-
-        Notes
-        -----
-        If the objective function returns a NumPy array instead
-        of the expected scalar, the sum of squares of the array
-        will be used.
-
-        Note that bounds and constraints can be set on Parameters
-        for any of these methods, so are not supported separately
-        for those designed to use bounds. However, if you use the
-        differential_evolution method you must specify finite
-        (min, max) for each varying Parameter.
-
-        """
-        result = self.prepare_fit(params=params)
-        result.method = method
-        variables = result.init_vals
-        params = result.params
-
-        fmin_kws = dict(method=method,
-                        options={'maxiter': 1000 * (len(variables) + 1)})
-        fmin_kws.update(self.kws)
-        fmin_kws.update(kws)
-
-        # hess supported only in some methods
-        if 'hess' in fmin_kws and method not in ('Newton-CG', 'dogleg',
-                                                 'trust-constr', 'trust-ncg',
-                                                 'trust-krylov', 'trust-exact'):
-            fmin_kws.pop('hess')
-
-        # jac supported only in some methods (and Dfun could be used...)
-        if 'jac' not in fmin_kws and fmin_kws.get('Dfun', None) is not None:
-            self.jacfcn = fmin_kws.pop('jac')
-            fmin_kws['jac'] = self.__jacobian
-
-        if 'jac' in fmin_kws and method not in ('CG', 'BFGS', 'Newton-CG',
-                                                'L-BFGS-B', 'TNC', 'SLSQP',
-                                                'dogleg', 'trust-ncg',
-                                                'trust-krylov', 'trust-exact'):
-            self.jacfcn = None
-            fmin_kws.pop('jac')
-
-        # workers / updating keywords only supported in differential_evolution
-        for kwd in ('workers', 'updating'):
-            if kwd in fmin_kws and method != 'differential_evolution':
-                fmin_kws.pop(kwd)
-
-        if method == 'differential_evolution':
-            for par in params.values():
-                if (par.vary and
-                        not (np.isfinite(par.min) and np.isfinite(par.max))):
-                    raise ValueError('differential_evolution requires finite '
-                                     'bound for all varying parameters')
-
-            _bounds = [(-np.pi / 2., np.pi / 2.)] * len(variables)
-            kwargs = dict(args=(), strategy='best1bin', maxiter=None,
-                          popsize=15, tol=0.01, mutation=(0.5, 1),
-                          recombination=0.7, seed=None, callback=None,
-                          disp=False, polish=True, init='latinhypercube',
-                          atol=0, updating='immediate', workers=1)
-
-            for k, v in fmin_kws.items():
-                if k in kwargs:
-                    kwargs[k] = v
-
-            try:
-                ret = differential_evolution(self.penalty, _bounds, **kwargs)
-            except AbortFitException:
-                pass
-
-        else:
-            try:
-                ret = scipy_minimize(self.penalty, variables, **fmin_kws)
-            except AbortFitException:
-                pass
-
-        if not result.aborted:
-            if isinstance(ret, dict):
-                for attr, value in ret.items():
-                    setattr(result, attr, value)
-            else:
-                for attr in dir(ret):
-                    if not attr.startswith('_'):
-                        setattr(result, attr, getattr(ret, attr))
-
-            result.x = np.atleast_1d(result.x)
-            result.residual = self.__residual(result.x)
-            result.nfev -= 1
-
-        result._calculate_statistics()
-
-        # calculate the cov_x and estimate uncertanties/correlations
-        if (not result.aborted and self.calc_covar and HAS_NUMDIFFTOOLS and
-                len(result.residual) > len(result.var_names)):
-            _covar_ndt = self._calculate_covariance_matrix(result.x)
-            if _covar_ndt is not None:
-                result.covar = self._int2ext_cov_x(_covar_ndt, result.x)
-                self._calculate_uncertainties_correlations()
-
-        return result
-
-    def _lnprob(self, theta, userfcn, params, var_names, bounds, userargs=(),
-                userkws=None, float_behavior='posterior', is_weighted=True,
-                nan_policy='raise'):
-        """Calculate the log-posterior probability.
-
-        See the `Minimizer.emcee` method for more details.
-
-        Parameters
-        ----------
-        theta : sequence
-            Float parameter values (only those being varied).
-        userfcn : callable
-            User objective function.
-        params : :class:`~lmfit.parameters.Parameters`
-            The entire set of Parameters.
-        var_names : list
-            The names of the parameters that are varying.
-        bounds : numpy.ndarray
-            Lower and upper bounds of parameters. Has shape (nvarys, 2).
-        userargs : tuple, optional
-            Extra positional arguments required for user objective function.
-        userkws : dict, optional
-            Extra keyword arguments required for user objective function.
-        float_behavior : str, optional
-            Specifies meaning of objective when it returns a float. Use
-            'posterior' if objective function returnins a log-posterior
-            probability (default) or 'chi2' if it returns a chi2 value.
-        is_weighted : bool, optional
-            If `userfcn` returns a vector of residuals then `is_weighted`
-            (default is True) specifies if the residuals have been weighted by
-            data uncertainties.
-        nan_policy : str, optional
-            Specifies action if `userfcn` returns NaN values. Use 'raise'
-            (default) to raise a `ValueError`, 'propagate' to use values as-is,
-            or 'omit' to filter out the non-finite values.
-
-        Returns
-        -------
-        lnprob : float
-            Log posterior probability.
-
-        """
-        # the comparison has to be done on theta and bounds. DO NOT inject theta
-        # values into Parameters, then compare Parameters values to the bounds.
-        # Parameters values are clipped to stay within bounds.
-        if np.any(theta > bounds[:, 1]) or np.any(theta < bounds[:, 0]):
-            return -np.inf
-        for name, val in zip(var_names, theta):
-            params[name].value = val
-        userkwargs = {}
-        if userkws is not None:
-            userkwargs = userkws
-        # update the constraints
-        params.update_constraints()
-        # now calculate the log-likelihood
-        out = userfcn(params, *userargs, **userkwargs)
-        self.result.nfev += 1
-        if callable(self.iter_cb):
-            abort = self.iter_cb(params, self.result.nfev, out,
-                                 *userargs, **userkwargs)
-            self._abort = self._abort or abort
-        if self._abort:
-            self.result.residual = out
-            self._lastpos = theta
-            raise AbortFitException("fit aborted by user.")
-        else:
-            out = _nan_policy(np.asarray(out).ravel(),
-                              nan_policy=self.nan_policy)
-        lnprob = np.asarray(out).ravel()
-        if len(lnprob) == 0:
-            lnprob = np.array([-1.e100])
-        if lnprob.size > 1:
-            # objective function returns a vector of residuals
-            if '__lnsigma' in params and not is_weighted:
-                # marginalise over a constant data uncertainty
-                __lnsigma = params['__lnsigma'].value
-                c = np.log(2 * np.pi) + 2 * __lnsigma
-                lnprob = -0.5 * np.sum((lnprob / np.exp(__lnsigma)) ** 2 + c)
-            else:
-                lnprob = -0.5 * (lnprob * lnprob).sum()
-        else:
-            # objective function returns a single value.
-            # use float_behaviour to figure out if the value is posterior or chi2
-            if float_behavior == 'posterior':
-                pass
-            elif float_behavior == 'chi2':
-                lnprob *= -0.5
-            else:
-                raise ValueError("float_behaviour must be either 'posterior' "
-                                 "or 'chi2' " + float_behavior)
-        return lnprob
-
-    def emcee(self, params=None, steps=1000, nwalkers=100, burn=0, thin=1,
-              ntemps=1, pos=None, reuse_sampler=False, workers=1,
-              float_behavior='posterior', is_weighted=True, seed=None,
-              progress=True,
-              check_converge=True, nevery=1000):
-        """Bayesian sampling of the posterior distribution using the `emcee`
-        Markov Chain Monte Carlo package.
-
-        The method assumes that the prior is Uniform. You need to have `emcee`
-        version 3 installed to use this method.
-
-        Parameters
-        ----------
-        params : :class:`~lmfit.parameter.Parameters`, optional
-            Parameters to use as starting point. If this is not specified then
-            the Parameters used to initialize the Minimizer object are used.
-        steps : int, optional
-            How many samples you would like to draw from the posterior
-            distribution for each of the walkers?
-        nwalkers : int, optional
-            Should be set so :math:`nwalkers >> nvarys`, where `nvarys` are
-            the number of parameters being varied during the fit.
-            'Walkers are the members of the ensemble. They are almost like
-            separate Metropolis-Hastings chains but, of course, the proposal
-            distribution for a given walker depends on the positions of all
-            the other walkers in the ensemble.' - from the `emcee` webpage.
-        burn : int, optional
-            Discard this many samples from the start of the sampling regime.
-        thin : int, optional
-            Only accept 1 in every `thin` samples.
-        ntemps : int, deprecated
-            ntemps has no effect.
-        pos : numpy.ndarray, optional
-            Specify the initial positions for the sampler, an ndarray of shape
-            `(nwalkers, nvarys)`.  You can also initialise using a previous
-            chain of the same `nwalkers` and `nvarys`. Note that `nvarys` may
-            be one larger than you expect it to be if your `userfcn` returns
-            an array and `is_weighted` is `False`.
-        reuse_sampler : bool, optional
-            Set to `True` if you have already run `emcee` with the `Minimizer`
-            instance and want to continue to draw from its ``sampler`` (and so
-            retain the chain history). If `False`,  a new sampler is created.
-            The keywords `nwalkers`, `pos`, and `params` will be ignored when
-            this is set, as they will be set by the existing sampler.
-            **Important**: the Parameters used to create the sampler must not
-            change in-between calls to `emcee`. Alteration of Parameters
-            would include changed ``min``, ``max``, ``vary`` and ``expr``
-            attributes. This may happen, for example, if you use an altered
-            Parameters object and call the `minimize` method in-between calls
-            to `emcee`.
-        workers : Pool-like or int, optional
-            For parallelization of sampling.  It can be any Pool-like object
-            with a map method that follows the same calling sequence as the
-            built-in `map` function. If int is given as the argument, then a
-            multiprocessing-based pool is spawned internally with the
-            corresponding number of parallel processes. 'mpi4py'-based
-            parallelization and 'joblib'-based parallelization pools can also
-            be used here. **Note**: because of multiprocessing overhead it may
-            only be worth parallelising if the objective function is expensive
-            to calculate, or if there are a large number of objective
-            evaluations per step (`nwalkers * nvarys`).
-        float_behavior : str, optional
-            Meaning of float (scalar) output of objective function. Use
-            'posterior' if it returns a log-posterior probability or 'chi2'
-            if it returns :math:`\\chi^2`. See Notes for further details.
-        is_weighted : bool, optional
-            Has your objective function been weighted by measurement
-            uncertainties? If `is_weighted is True` then your objective
-            function is assumed to return residuals that have been divided by
-            the true measurement uncertainty `(data - model) / sigma`. If
-            `is_weighted is False` then the objective function is assumed to
-            return unweighted residuals, `data - model`. In this case `emcee`
-            will employ a positive measurement uncertainty during the sampling.
-            This measurement uncertainty will be present in the output params
-            and output chain with the name `__lnsigma`. A side effect of this
-            is that you cannot use this parameter name yourself.
-            **Important** this parameter only has any effect if your objective
-            function returns an array. If your objective function returns a
-            float, then this parameter is ignored. See Notes for more details.
-        seed : int or `numpy.random.RandomState`, optional
-            If `seed` is an int, a new `numpy.random.RandomState` instance is
-            used, seeded with `seed`.
-            If `seed` is already a `numpy.random.RandomState` instance, then
-            that `numpy.random.RandomState` instance is used.
-            Specify `seed` for repeatable minimizations.
-        progress : bool, optional
-            Print a progress bar to the console while running.
-
-        Returns
-        -------
-        :class:`MinimizerResult`
-            MinimizerResult object containing updated params, statistics,
-            etc. The updated params represent the median of the samples,
-            while the uncertainties are half the difference of the 15.87
-            and 84.13 percentiles.  The `MinimizerResult` contains a few
-            additional attributes: ``chain`` contain the samples and has
-            shape `((steps - burn) // thin, nwalkers, nvarys)`.
-            ``flatchain`` is a `pandas.DataFrame` of the flattened chain,
-            that can be accessed with `result.flatchain[parname]`.
-            ``lnprob`` contains the log probability for each sample in
-            ``chain``.  The sample with the highest probability corresponds
-            to the maximum likelihood estimate.  ``acor`` is an array
-            containing the autocorrelation time for each parameter if the
-            autocorrelation time can be computed from the chain. Finally,
-            ``acceptance_fraction`` (an array of the fraction of steps
-            accepted for each walker).
-
-        Notes
-        -----
-        This method samples the posterior distribution of the parameters
-        using Markov Chain Monte Carlo.  It calculates the log-posterior
-        probability of the model parameters, `F`, given the data, `D`,
-        :math:`\\ln p(F_{true} | D)`. This 'posterior probability' is given by:
-
-        .. math::
-
-            \\ln p(F_{true} | D) \\propto \\ln p(D | F_{true}) + \\ln p(F_{true})
-
-        where :math:`\\ln p(D | F_{true})` is the 'log-likelihood' and
-        :math:`\\ln p(F_{true})` is the 'log-prior'. The default log-prior
-        encodes prior information known about the model that the log-prior
-        probability is `-numpy.inf` (impossible) if any of the parameters
-        is outside its limits, and is zero if all the parameters are inside
-        their bounds (uniform prior). The log-likelihood function is [1]_:
-
-        .. math::
-
-            \\ln p(D|F_{true}) = -\\frac{1}{2}\\sum_n \\left[\\frac{(g_n(F_{true}) - D_n)^2}{s_n^2}+\\ln (2\\pi s_n^2)\\right]
-
-        The first term represents the residual (:math:`g` being the generative
-        model, :math:`D_n` the data and :math:`s_n` the measurement
-        uncertainty). This gives :math:`\\chi^2` when summed over all data
-        points.  The objective function may also return the log-posterior
-        probability, :math:`\\ln p(F_{true} | D)`.  Since the default
-        log-prior term is zero, the objective function can also just return
-        the log-likelihood, unless you wish to create a non-uniform prior.
-
-        If the objective function returns a float value, this is assumed by
-        default to be the log-posterior probability, (`float_behavior` default
-        is 'posterior'. If your objective function returns :math:`\\chi^2`,
-        then you should use a value of `float_behavior='chi2'`.
-
-        By default objective functions may return an ndarray of (possibly
-        weighted) residuals. In this case, use `is_weighted` to select whether
-        these are correctly weighted by measurement uncertainty.  Note that
-        this ignores the second term above, so that to calculate a correct
-        log-posterior probability value your objective function should return
-        a float value.  With `is_weighted=False` the data uncertainty, `s_n`,
-        will be treated as a nuisance parameter to be marginalized out. This
-        uses strictly positive uncertainty (homoscedasticity) for each data
-        point, :math:`s_n = \\exp(\\rm{\\_\\_lnsigma})`.  `__lnsigma` will be
-        present in `MinimizerResult.params`, as well as `Minimizer.chain` and
-        `nvarys` will be increased by one.
-
-        References
-        ----------
-        .. [1] https://emcee.readthedocs.io
-
-        """
-        if not HAS_EMCEE:
-            raise NotImplementedError('emcee version 3 is required.')
-
-        if ntemps > 1:
-            msg = ("'ntemps' has no effect anymore, since the PTSampler was "
-                   "removed from emcee version 3.")
-            raise DeprecationWarning(msg)
-
-        tparams = params
-        # if you're reusing the sampler then nwalkers have to be
-        # determined from the previous sampling
-        if reuse_sampler:
-            if not hasattr(self, 'sampler') or not hasattr(self, '_lastpos'):
-                raise ValueError("You wanted to use an existing sampler, but "
-                                 "it hasn't been created yet")
-            if len(self._lastpos.shape) == 2:
-                nwalkers = self._lastpos.shape[0]
-            elif len(self._lastpos.shape) == 3:
-                nwalkers = self._lastpos.shape[1]
-            tparams = None
-
-        result = self.prepare_fit(params=tparams)
-        params = result.params
-
-        # check if the userfcn returns a vector of residuals
-        out = self.userfcn(params, *self.userargs, **self.userkws)
-        out = np.asarray(out).ravel()
-        if out.size > 1 and is_weighted is False:
-            # we need to marginalise over a constant data uncertainty
-            if '__lnsigma' not in params:
-                # __lnsigma should already be in params if is_weighted was
-                # previously set to True.
-                params.add('__lnsigma', value=0.01, min=-np.inf, max=np.inf,
-                           vary=True)
-                # have to re-prepare the fit
-                result = self.prepare_fit(params)
-                params = result.params
-
-        result.method = 'emcee'
-
-        # Removing internal parameter scaling. We could possibly keep it,
-        # but I don't know how this affects the emcee sampling.
-        bounds = []
-        var_arr = np.zeros(len(result.var_names))
-        i = 0
-        for par in params:
-            param = params[par]
-            if param.expr is not None:
-                param.vary = False
-            if param.vary:
-                var_arr[i] = param.value
-                i += 1
-            else:
-                # don't want to append bounds if they're not being varied.
-                continue
-
-            param.from_internal = lambda val: val
-            lb, ub = param.min, param.max
-            if lb is None or lb is np.nan:
-                lb = -np.inf
-            if ub is None or ub is np.nan:
-                ub = np.inf
-            bounds.append((lb, ub))
-        bounds = np.array(bounds)
-
-        self.nvarys = len(result.var_names)
-
-        # set up multiprocessing options for the samplers
-        auto_pool = None
-        sampler_kwargs = {}
-        if isinstance(workers, int) and workers > 1 and HAS_DILL:
-            auto_pool = multiprocessing.Pool(workers)
-            sampler_kwargs['pool'] = auto_pool
-        elif hasattr(workers, 'map'):
-            sampler_kwargs['pool'] = workers
-
-        # function arguments for the log-probability functions
-        # these values are sent to the log-probability functions by the sampler.
-        lnprob_args = (self.userfcn, params, result.var_names, bounds)
-        lnprob_kwargs = {'is_weighted': is_weighted,
-                         'float_behavior': float_behavior,
-                         'userargs': self.userargs,
-                         'userkws': self.userkws,
-                         'nan_policy': self.nan_policy}
-
-        sampler_kwargs['args'] = lnprob_args
-        sampler_kwargs['kwargs'] = lnprob_kwargs
-
-        # TM added:
-        #sampler_kwargs['skip_initial_state_check'] = True 
-
-        # set up the random number generator
-        rng = _make_random_gen(seed)
-
-        # now initialise the samplers
-        if reuse_sampler:
-            if auto_pool is not None:
-                self.sampler.pool = auto_pool
-
-            p0 = self._lastpos
-            if p0.shape[-1] != self.nvarys:
-                raise ValueError("You cannot reuse the sampler if the number "
-                                 "of varying parameters has changed")
-
-        else:
-            p0 = 1 + rng.randn(nwalkers, self.nvarys) * 1.e-4
-            p0 *= var_arr
-            sampler_kwargs['pool'] = auto_pool
-            self.sampler = emcee.EnsembleSampler(nwalkers, self.nvarys,
-                                                 self._lnprob, **sampler_kwargs)
-
-        # user supplies an initialisation position for the chain
-        # If you try to run the sampler with p0 of a wrong size then you'll get
-        # a ValueError. Note, you can't initialise with a position if you are
-        # reusing the sampler.
-        if pos is not None and not reuse_sampler:
-            tpos = np.asfarray(pos)
-            if p0.shape == tpos.shape:
-                pass
-            # trying to initialise with a previous chain
-            elif tpos.shape[-1] == self.nvarys:
-                tpos = tpos[-1]
-            else:
-                raise ValueError('pos should have shape (nwalkers, nvarys)')
-            p0 = tpos
-
-        # if you specified a seed then you also need to seed the sampler
-        if seed is not None:
-            self.sampler.random_state = rng.get_state()
-
-        # now do a production run, sampling all the time
-        try:
-            if check_converge: # TM; 2021.Jan.13
-                # Now we'll sample for up to max_n steps
-                index = 0
-                old_tau = np.inf
-                autocorr = np.empty(steps)
-
-                for sample in self.sampler.sample(p0, iterations=steps, progress=True):
-                    # Only check convergence every 100 steps
-                    if self.sampler.iteration % nevery:
-                        continue
-
-                    # Compute the autocorrelation time so far
-                    # Using tol=0 means that we'll always get an estimate even
-                    # if it isn't trustworthy
-                    tau = self.sampler.get_autocorr_time(tol=0)
-                    autocorr[index] = np.mean(tau)
-                    index += 1
-
-                    # Check convergence
-                    converged = np.all(tau * 100 < self.sampler.iteration)
-                    converged &= np.all(np.abs(old_tau - tau) / tau < 0.01)
-                    if converged:
-                        #print('Converged at %d'%index*nevery)
-                        burn = int(index*nevery / 50) # Burn 50%
-                        result.burn = burn
-                        result.steps = index*nevery
-                        break
-                    old_tau = tau
-
-            else:
-                output = self.sampler.run_mcmc(p0, steps, progress=progress)
-                self._lastpos = output.coords
-
-        except AbortFitException:
-            result.aborted = True
-            result.message = "Fit aborted by user callback. Could not estimate error-bars."
-            result.success = False
-            result.nfev = self.result.nfev
-            output = None
-
-        # discard the burn samples and thin
-        chain = self.sampler.get_chain(thin=thin, discard=burn)[..., :, :]
-        lnprobability = self.sampler.get_log_prob(thin=thin, discard=burn)[..., :]
-        flatchain = chain.reshape((-1, self.nvarys))
-        if not result.aborted:
-            quantiles = np.percentile(flatchain, [15.87, 50, 84.13], axis=0)
-
-            for i, var_name in enumerate(result.var_names):
-                std_l, median, std_u = quantiles[:, i]
-                params[var_name].value = median
-                params[var_name].stderr = 0.5 * (std_u - std_l)
-                params[var_name].correl = {}
-
-            params.update_constraints()
-
-            # work out correlation coefficients
-            corrcoefs = np.corrcoef(flatchain.T)
-
-            for i, var_name in enumerate(result.var_names):
-                for j, var_name2 in enumerate(result.var_names):
-                    if i != j:
-                        result.params[var_name].correl[var_name2] = corrcoefs[i, j]
-
-        result.chain = np.copy(chain)
-        result.lnprob = np.copy(lnprobability)
-        result.errorbars = True
-        result.nvarys = len(result.var_names)
-        result.nfev = nwalkers*steps
-        try:
-            result.acor = self.sampler.get_autocorr_time()
-        except AutocorrError as e:
-            print(str(e))
-            pass
-        result.acceptance_fraction = self.sampler.acceptance_fraction
-
-        # Calculate the residual with the "best fit" parameters
-        out = self.userfcn(params, *self.userargs, **self.userkws)
-        result.residual = _nan_policy(out, nan_policy=self.nan_policy,
-                                      handle_inf=False)
-
-        # If uncertainty was automatically estimated, weight the residual properly
-        if (not is_weighted) and (result.residual.size > 1):
-            if '__lnsigma' in params:
-                result.residual = result.residual/np.exp(params['__lnsigma'].value)
-
-        # Calculate statistics for the two standard cases:
-        if isinstance(result.residual, ndarray) or (float_behavior == 'chi2'):
-            result._calculate_statistics()
-
-        # Handle special case unique to emcee:
-        # This should eventually be moved into result._calculate_statistics.
-        elif float_behavior == 'posterior':
-            result.ndata = 1
-            result.nfree = 1
-
-            # assuming prior prob = 1, this is true
-            _neg2_log_likel = -2*result.residual
-
-            # assumes that residual is properly weighted, avoid overflowing np.exp()
-            result.chisqr = np.exp(min(650, _neg2_log_likel))
-
-            result.redchi = result.chisqr / result.nfree
-            result.aic = _neg2_log_likel + 2 * result.nvarys
-            result.bic = _neg2_log_likel + np.log(result.ndata) * result.nvarys
-
-        if auto_pool is not None:
-            auto_pool.terminate()
-
-        return result
-
-    def least_squares(self, params=None, **kws):
-        """Least-squares minimization using :scipydoc:`optimize.least_squares`.
-
-        This method wraps :scipydoc:`optimize.least_squares`, which has inbuilt
-        support for bounds and robust loss functions. By default it uses the
-        Trust Region Reflective algorithm with a linear loss function (i.e.,
-        the standard least-squares problem).
-
-        Parameters
-        ----------
-        params : :class:`~lmfit.parameter.Parameters`, optional
-           Parameters to use as starting point.
-        **kws : dict, optional
-            Minimizer options to pass to :scipydoc:`optimize.least_squares`.
-
-        Returns
-        -------
-        :class:`MinimizerResult`
-            Object containing the optimized parameter and several
-            goodness-of-fit statistics.
-
-
-        .. versionchanged:: 0.9.0
-           Return value changed to :class:`MinimizerResult`.
-
-        """
-        result = self.prepare_fit(params)
-        result.method = 'least_squares'
-
-        replace_none = lambda x, sign: sign*np.inf if x is None else x
-
-        start_vals, lower_bounds, upper_bounds = [], [], []
-        for vname in result.var_names:
-            par = self.params[vname]
-            start_vals.append(par.value)
-            lower_bounds.append(replace_none(par.min, -1))
-            upper_bounds.append(replace_none(par.max, 1))
-
-        try:
-            ret = least_squares(self.__residual, start_vals,
-                                bounds=(lower_bounds, upper_bounds),
-                                kwargs=dict(apply_bounds_transformation=False),
-                                **kws)
-            result.residual = ret.fun
-        except AbortFitException:
-            pass
-
-        # note: upstream least_squares is actually returning
-        # "last evaluation", not "best result", but we do this
-        # here for consistency, and assuming it will be fixed.
-        if not result.aborted:
-            result.residual = self.__residual(ret.x, False)
-            result.nfev -= 1
-        result._calculate_statistics()
-
-        if not result.aborted:
-            for attr in ret:
-                setattr(result, attr, ret[attr])
-
-            result.x = np.atleast_1d(result.x)
-
-            # calculate the cov_x and estimate uncertainties/correlations
-            try:
-                if issparse(ret.jac):
-                    hess = (ret.jac.T * ret.jac).toarray()
-                elif isinstance(ret.jac, LinearOperator):
-                    identity = np.eye(ret.jac.shape[1], dtype=ret.jac.dtype)
-                    # TODO: Remove try-except when scipy < 1.4.0 support dropped
-                    try:
-                        # For scipy >= 1.4.0 (with Linear Operator transpose)
-                        # https://github.com/scipy/scipy/pull/9064
-                        hess = (ret.jac.T * ret.jac) * identity
-                    except AttributeError:
-                        # For scipy < 1.4.0 (without Linear Operator transpose)
-                        jac = ret.jac * identity
-                        hess = np.matmul(jac.T, jac)
-                else:
-                    hess = np.matmul(ret.jac.T, ret.jac)
-                result.covar = np.linalg.inv(hess)
-                self._calculate_uncertainties_correlations()
-            except LinAlgError:
-                pass
-
-        return result
-
-    def leastsq(self, params=None, **kws):
-        """Use Levenberg-Marquardt minimization to perform a fit.
-
-        It assumes that the input Parameters have been initialized, and
-        a function to minimize has been properly set up.
-        When possible, this calculates the estimated uncertainties and
-        variable correlations from the covariance matrix.
-
-        This method calls :scipydoc:`optimize.leastsq`.
-        By default, numerical derivatives are used, and the following
-        arguments are set:
-
-        +------------------+----------------+------------------------------------------------------------+
-        | :meth:`leastsq`  |  Default Value | Description                                                |
-        | arg              |                |                                                            |
-        +==================+================+============================================================+
-        |   xtol           |  1.e-7         | Relative error in the approximate solution                 |
-        +------------------+----------------+------------------------------------------------------------+
-        |   ftol           |  1.e-7         | Relative error in the desired sum of squares               |
-        +------------------+----------------+------------------------------------------------------------+
-        |   maxfev         | 2000*(nvar+1)  | Maximum number of function calls (nvar= # of variables)    |
-        +------------------+----------------+------------------------------------------------------------+
-        |   Dfun           | None           | Function to call for Jacobian calculation                  |
-        +------------------+----------------+------------------------------------------------------------+
-
-        Parameters
-        ----------
-        params : :class:`~lmfit.parameter.Parameters`, optional
-           Parameters to use as starting point.
-        **kws : dict, optional
-            Minimizer options to pass to :scipydoc:`optimize.leastsq`.
-
-        Returns
-        -------
-        :class:`MinimizerResult`
-            Object containing the optimized parameter
-            and several goodness-of-fit statistics.
-
-
-        .. versionchanged:: 0.9.0
-           Return value changed to :class:`MinimizerResult`.
-
-        """
-        result = self.prepare_fit(params=params)
-        result.method = 'leastsq'
-        result.nfev -= 2  # correct for "pre-fit" initialization/checks
-        variables = result.init_vals
-        nvars = len(variables)
-        lskws = dict(full_output=1, xtol=1.e-7, ftol=1.e-7, col_deriv=False,
-                     gtol=1.e-7, maxfev=2000*(nvars+1), Dfun=None)
-
-        lskws.update(self.kws)
-        lskws.update(kws)
-
-        self.col_deriv = False
-        if lskws['Dfun'] is not None:
-            self.jacfcn = lskws['Dfun']
-            self.col_deriv = lskws['col_deriv']
-            lskws['Dfun'] = self.__jacobian
-
-        # suppress runtime warnings during fit and error analysis
-        orig_warn_settings = np.geterr()
-        np.seterr(all='ignore')
-
-        try:
-            lsout = scipy_leastsq(self.__residual, variables, **lskws)
-        except AbortFitException:
-            pass
-
-        if not result.aborted:
-            _best, _cov, infodict, errmsg, ier = lsout
-            result.residual = self.__residual(_best)
-            result.nfev -= 1
-        result._calculate_statistics()
-
-        if result.aborted:
-            return result
-
-        result.ier = ier
-        result.lmdif_message = errmsg
-        result.success = ier in [1, 2, 3, 4]
-        if ier in {1, 2, 3}:
-            result.message = 'Fit succeeded.'
-        elif ier == 0:
-            result.message = ('Invalid Input Parameters. I.e. more variables '
-                              'than data points given, tolerance < 0.0, or '
-                              'no data provided.')
-        elif ier == 4:
-            result.message = 'One or more variable did not affect the fit.'
-        elif ier == 5:
-            result.message = self._err_maxfev % lskws['maxfev']
-        else:
-            result.message = 'Tolerance seems to be too small.'
-
-        # self.errorbars = error bars were successfully estimated
-        result.errorbars = (_cov is not None)
-        if result.errorbars:
-            # transform the covariance matrix to "external" parameter space
-            result.covar = self._int2ext_cov_x(_cov, _best)
-            # calculate parameter uncertainties and correlations
-            self._calculate_uncertainties_correlations()
-        else:
-            result.message = '%s Could not estimate error-bars.' % result.message
-
-        np.seterr(**orig_warn_settings)
-
-        return result
-
-    def basinhopping(self, params=None, **kws):
-        """Use the `basinhopping` algorithm to find the global minimum of a function.
-
-        This method calls :scipydoc:`optimize.basinhopping` using the default
-        arguments. The default minimizer is `BFGS`, but since lmfit supports
-        parameter bounds for all minimizers, the user can choose any of the
-        solvers present in :scipydoc:`optimize.minimize`.
-
-        Parameters
-        ----------
-        params : :class:`~lmfit.parameter.Parameters` object, optional
-            Contains the Parameters for the model. If None, then the
-            Parameters used to initialize the Minimizer object are used.
-
-        Returns
-        -------
-        :class:`MinimizerResult`
-            Object containing the optimization results from the basinhopping
-            algorithm.
-
-
-        .. versionadded:: 0.9.10
-
-        """
-        result = self.prepare_fit(params=params)
-        result.method = 'basinhopping'
-
-        basinhopping_kws = dict(niter=100, T=1.0, stepsize=0.5,
-                                minimizer_kwargs={}, take_step=None,
-                                accept_test=None, callback=None, interval=50,
-                                disp=False, niter_success=None, seed=None)
-
-        basinhopping_kws.update(self.kws)
-        basinhopping_kws.update(kws)
-
-        x0 = result.init_vals
-
-        try:
-            ret = scipy_basinhopping(self.penalty, x0, **basinhopping_kws)
-        except AbortFitException:
-            pass
-
-        if not result.aborted:
-            result.message = ret.message
-            result.residual = self.__residual(ret.x)
-            result.nfev -= 1
-
-        result._calculate_statistics()
-
-        # calculate the cov_x and estimate uncertanties/correlations
-        if (not result.aborted and self.calc_covar and HAS_NUMDIFFTOOLS and
-                len(result.residual) > len(result.var_names)):
-            _covar_ndt = self._calculate_covariance_matrix(ret.x)
-            if _covar_ndt is not None:
-                result.covar = self._int2ext_cov_x(_covar_ndt, ret.x)
-                self._calculate_uncertainties_correlations()
-
-        return result
-
-    def brute(self, params=None, Ns=20, keep=50, workers=1):
-        """Use the `brute` method to find the global minimum of a function.
-
-        The following parameters are passed to :scipydoc:`optimize.brute`
-        and cannot be changed:
-
-        +-------------------+-------+----------------------------------------+
-        | :meth:`brute` arg | Value | Description                            |
-        +===================+=======+========================================+
-        |   full_output     | 1     | Return the evaluation grid and         |
-        |                   |       | the objective function's values on it. |
-        +-------------------+-------+----------------------------------------+
-        |   finish          | None  | No "polishing" function is to be used  |
-        |                   |       | after the grid search.                 |
-        +-------------------+-------+----------------------------------------+
-        |   disp            | False | Do not print convergence messages      |
-        |                   |       | (when finish is not None).             |
-        +-------------------+-------+----------------------------------------+
-
-        It assumes that the input Parameters have been initialized, and a
-        function to minimize has been properly set up.
-
-        Parameters
-        ----------
-        params : :class:`~lmfit.parameter.Parameters`, optional
-            Contains the Parameters for the model. If None, then the
-            Parameters used to initialize the Minimizer object are used.
-        Ns : int, optional
-            Number of grid points along the axes, if not otherwise specified
-            (see Notes).
-        keep : int, optional
-            Number of best candidates from the brute force method that are
-            stored in the :attr:`candidates` attribute. If 'all', then all grid
-            points from :scipydoc:`optimize.brute` are stored as candidates.
-        workers : int or map-like callable, optional
-            For parallel evaluation of the grid, added in SciPy v1.3 (see
-            :scipydoc:`optimize.brute` for more details).
-
-        Returns
-        -------
-        :class:`MinimizerResult`
-            Object containing the parameters from the brute force method.
-            The return values (`x0`, `fval`, `grid`, `Jout`) from
-            :scipydoc:`optimize.brute` are stored as `brute_<parname>` attributes.
-            The `MinimizerResult` also contains the `candidates` attribute and
-            `show_candidates()` method. The `candidates` attribute contains the
-            parameters and chisqr from the brute force method as a namedtuple,
-            ('Candidate', ['params', 'score']), sorted on the (lowest) chisqr
-            value. To access the values for a particular candidate one can use
-            `result.candidate[#].params` or `result.candidate[#].score`, where
-            a lower # represents a better candidate. The `show_candidates(#)`
-            uses the :meth:`pretty_print` method to show a specific candidate-#
-            or all candidates when no number is specified.
-
-
-        .. versionadded:: 0.9.6
-
-
-        Notes
-        -----
-        The :meth:`brute` method evalutes the function at each point of a
-        multidimensional grid of points. The grid points are generated from the
-        parameter ranges using `Ns` and (optional) `brute_step`.
-        The implementation in :scipydoc:`optimize.brute` requires finite bounds
-        and the `range` is specified as a two-tuple `(min, max)` or slice-object
-        `(min, max, brute_step)`. A slice-object is used directly, whereas a
-        two-tuple is converted to a slice object that interpolates `Ns` points
-        from `min` to `max`, inclusive.
-
-        In addition, the :meth:`brute` method in lmfit, handles three other
-        scenarios given below with their respective slice-object:
-
-            - lower bound (:attr:`min`) and :attr:`brute_step` are specified:
-                range = (`min`, `min` + `Ns` * `brute_step`, `brute_step`).
-            - upper bound (:attr:`max`) and :attr:`brute_step` are specified:
-                range = (`max` - `Ns` * `brute_step`, `max`, `brute_step`).
-            - numerical value (:attr:`value`) and :attr:`brute_step` are specified:
-                range = (`value` - (`Ns`//2) * `brute_step`, `value` +
-                (`Ns`//2) * `brute_step`, `brute_step`).
-
-        """
-        result = self.prepare_fit(params=params)
-        result.method = 'brute'
-
-        brute_kws = dict(full_output=1, finish=None, disp=False)
-        # keyword 'workers' is introduced in SciPy v1.3
-        # FIXME: remove this check after updating the requirement >= 1.3
-        major, minor, micro = scipy_version.split('.', 2)
-
-        if int(major) == 1 and int(minor) >= 3:
-            brute_kws.update({'workers': workers})
-
-        varying = np.asarray([par.vary for par in self.params.values()])
-        replace_none = lambda x, sign: sign*np.inf if x is None else x
-        lower_bounds = np.asarray([replace_none(i.min, -1) for i in
-                                   self.params.values()])[varying]
-        upper_bounds = np.asarray([replace_none(i.max, 1) for i in
-                                   self.params.values()])[varying]
-        value = np.asarray([i.value for i in self.params.values()])[varying]
-        stepsize = np.asarray([i.brute_step for i in self.params.values()])[varying]
-
-        ranges = []
-        for i, step in enumerate(stepsize):
-            if np.all(np.isfinite([lower_bounds[i], upper_bounds[i]])):
-                # lower AND upper bounds are specified (brute_step optional)
-                par_range = ((lower_bounds[i], upper_bounds[i], step)
-                             if step else (lower_bounds[i], upper_bounds[i]))
-            elif np.isfinite(lower_bounds[i]) and step:
-                # lower bound AND brute_step are specified
-                par_range = (lower_bounds[i], lower_bounds[i] + Ns*step, step)
-            elif np.isfinite(upper_bounds[i]) and step:
-                # upper bound AND brute_step are specified
-                par_range = (upper_bounds[i] - Ns*step, upper_bounds[i], step)
-            elif np.isfinite(value[i]) and step:
-                # no bounds, but an initial value is specified
-                par_range = (value[i] - (Ns//2)*step, value[i] + (Ns//2)*step,
-                             step)
-            else:
-                raise ValueError('Not enough information provided for the brute '
-                                 'force method. Please specify bounds or at '
-                                 'least an initial value and brute_step for '
-                                 'parameter "{}".'.format(result.var_names[i]))
-            ranges.append(par_range)
-
-        try:
-            ret = scipy_brute(self.penalty, tuple(ranges), Ns=Ns, **brute_kws)
-        except AbortFitException:
-            pass
-
-        if not result.aborted:
-            result.brute_x0 = ret[0]
-            result.brute_fval = ret[1]
-            result.brute_grid = ret[2]
-            result.brute_Jout = ret[3]
-
-            # sort the results of brute and populate .candidates attribute
-            grid_score = ret[3].ravel()  # chisqr
-            grid_points = [par.ravel() for par in ret[2]]
-
-            if len(result.var_names) == 1:
-                grid_result = np.array([res for res in zip(zip(grid_points), grid_score)],
-                                       dtype=[('par', 'O'), ('score', 'float64')])
-            else:
-                grid_result = np.array([res for res in zip(zip(*grid_points), grid_score)],
-                                       dtype=[('par', 'O'), ('score', 'float64')])
-            grid_result_sorted = grid_result[grid_result.argsort(order='score')]
-
-            result.candidates = []
-
-            if keep == 'all':
-                keep_candidates = len(grid_result_sorted)
-            else:
-                keep_candidates = min(len(grid_result_sorted), keep)
-
-            for data in grid_result_sorted[:keep_candidates]:
-                pars = deepcopy(self.params)
-                for i, par in enumerate(result.var_names):
-                    pars[par].value = data[0][i]
-                result.candidates.append(Candidate(params=pars, score=data[1]))
-
-            result.params = result.candidates[0].params
-            result.residual = self.__residual(result.brute_x0, apply_bounds_transformation=False)
-            result.nfev = len(result.brute_Jout.ravel())
-
-        result._calculate_statistics()
-
-        return result
-
-    def ampgo(self, params=None, **kws):
-        """Find the global minimum of a multivariate function using AMPGO.
-
-        AMPGO stands for 'Adaptive Memory Programming for Global Optimization'
-        and is an efficient algorithm to find the global minimum.
-
-        Parameters
-        ----------
-        params : :class:`~lmfit.parameter.Parameters`, optional
-            Contains the Parameters for the model. If None, then the
-            Parameters used to initialize the Minimizer object are used.
-        **kws : dict, optional
-            Minimizer options to pass to the ampgo algorithm, the options are
-            listed below::
-
-                local: str (default is 'L-BFGS-B')
-                    Name of the local minimization method. Valid options are:
-                    - 'L-BFGS-B'
-                    - 'Nelder-Mead'
-                    - 'Powell'
-                    - 'TNC'
-                    - 'SLSQP'
-                local_opts: dict (default is None)
-                    Options to pass to the local minimizer.
-                maxfunevals: int (default is None)
-                    Maximum number of function evaluations. If None, the optimization will stop
-                    after `totaliter` number of iterations.
-                totaliter: int (default is 20)
-                    Maximum number of global iterations.
-                maxiter: int (default is 5)
-                    Maximum number of `Tabu Tunneling` iterations during each global iteration.
-                glbtol: float (default is 1e-5)
-                    Tolerance whether or not to accept a solution after a tunneling phase.
-                eps1: float (default is 0.02)
-                    Constant used to define an aspiration value for the objective function during
-                    the Tunneling phase.
-                eps2: float (default is 0.1)
-                    Perturbation factor used to move away from the latest local minimum at the
-                    start of a Tunneling phase.
-                tabulistsize: int (default is 5)
-                    Size of the (circular) tabu search list.
-                tabustrategy: str (default is 'farthest')
-                    Strategy to use when the size of the tabu list exceeds `tabulistsize`. It
-                    can be 'oldest' to drop the oldest point from the tabu list or 'farthest'
-                    to drop the element farthest from the last local minimum found.
-                disp: bool (default is False)
-                    Set to True to print convergence messages.
-
-        Returns
-        -------
-        :class:`MinimizerResult`
-            Object containing the parameters from the ampgo method, with fit
-            parameters, statistics and such. The return values (`x0`, `fval`,
-            `eval`, `msg`, `tunnel`) are stored as `ampgo_<parname>` attributes.
-
-
-        .. versionadded:: 0.9.10
-
-
-        Notes
-        ----
-        The Python implementation was written by Andrea Gavana in 2014
-        (http://infinity77.net/global_optimization/index.html).
-
-        The details of the AMPGO algorithm are described in the paper
-        "Adaptive Memory Programming for Constrained Global Optimization"
-        located here:
-
-        http://leeds-faculty.colorado.edu/glover/fred%20pubs/416%20-%20AMP%20(TS)%20for%20Constrained%20Global%20Opt%20w%20Lasdon%20et%20al%20.pdf
-
-        """
-        result = self.prepare_fit(params=params)
-
-        ampgo_kws = dict(local='L-BFGS-B', local_opts=None, maxfunevals=None,
-                         totaliter=20, maxiter=5, glbtol=1e-5, eps1=0.02,
-                         eps2=0.1, tabulistsize=5, tabustrategy='farthest',
-                         disp=False)
-        ampgo_kws.update(self.kws)
-        ampgo_kws.update(kws)
-
-        values = result.init_vals
-        result.method = "ampgo, with {} as local solver".format(ampgo_kws['local'])
-
-        try:
-            ret = ampgo(self.penalty, values, **ampgo_kws)
-        except AbortFitException:
-            pass
-
-        if not result.aborted:
-            result.ampgo_x0 = ret[0]
-            result.ampgo_fval = ret[1]
-            result.ampgo_eval = ret[2]
-            result.ampgo_msg = ret[3]
-            result.ampgo_tunnel = ret[4]
-
-            for i, par in enumerate(result.var_names):
-                result.params[par].value = result.ampgo_x0[i]
-
-            result.residual = self.__residual(result.ampgo_x0)
-            result.nfev -= 1
-
-        result._calculate_statistics()
-
-        # calculate the cov_x and estimate uncertanties/correlations
-        if (not result.aborted and self.calc_covar and HAS_NUMDIFFTOOLS and
-                len(result.residual) > len(result.var_names)):
-            _covar_ndt = self._calculate_covariance_matrix(result.ampgo_x0)
-            if _covar_ndt is not None:
-                result.covar = self._int2ext_cov_x(_covar_ndt, result.ampgo_x0)
-                self._calculate_uncertainties_correlations()
-
-        return result
-
-    def shgo(self, params=None, **kws):
-        """Use the `SHGO` algorithm to find the global minimum.
-
-        SHGO stands for "simplicial homology global optimization" and calls
-        :scipydoc:`optimize.shgo` using its default arguments.
-
-        Parameters
-        ----------
-        params : :class:`~lmfit.parameter.Parameters`, optional
-            Contains the Parameters for the model. If None, then the
-            Parameters used to initialize the Minimizer object are used.
-        **kws : dict, optional
-            Minimizer options to pass to the SHGO algorithm.
-
-        Returns
-        -------
-        :class:`MinimizerResult`
-            Object containing the parameters from the SHGO method.
-            The return values specific to :scipydoc:`optimize.shgo` (`x`,
-            `xl`, `fun`, `funl`, `nfev`, `nit`, `nlfev`, `nlhev`, and
-            `nljev`) are stored as `shgo_<parname>` attributes.
-
-
-        .. versionadded:: 0.9.14
-
-        """
-        result = self.prepare_fit(params=params)
-        result.method = 'shgo'
-
-        shgo_kws = dict(constraints=None, n=100, iters=1, callback=None,
-                        minimizer_kwargs=None, options=None,
-                        sampling_method='simplicial')
-
-        shgo_kws.update(self.kws)
-        shgo_kws.update(kws)
-
-        varying = np.asarray([par.vary for par in self.params.values()])
-        bounds = np.asarray([(par.min, par.max) for par in
-                             self.params.values()])[varying]
-
-        try:
-            ret = scipy_shgo(self.penalty, bounds, **shgo_kws)
-        except AbortFitException:
-            pass
-
-        if not result.aborted:
-            for attr, value in ret.items():
-                if attr in ['success', 'message']:
-                    setattr(result, attr, value)
-                else:
-                    setattr(result, 'shgo_{}'.format(attr), value)
-
-            result.residual = self.__residual(result.shgo_x, False)
-            result.nfev -= 1
-
-        result._calculate_statistics()
-
-        # calculate the cov_x and estimate uncertanties/correlations
-        if (not result.aborted and self.calc_covar and HAS_NUMDIFFTOOLS and
-                len(result.residual) > len(result.var_names)):
-            result.covar = self._calculate_covariance_matrix(result.shgo_x)
-            if result.covar is not None:
-                self._calculate_uncertainties_correlations()
-
-        return result
-
-    def dual_annealing(self, params=None, **kws):
-        """Use the `dual_annealing` algorithm to find the global minimum.
-
-        This method calls :scipydoc:`optimize.dual_annealing` using its
-        default arguments.
-
-        Parameters
-        ----------
-        params : :class:`~lmfit.parameter.Parameters`, optional
-            Contains the Parameters for the model. If None, then the
-            Parameters used to initialize the Minimizer object are used.
-        **kws : dict, optional
-            Minimizer options to pass to the dual_annealing algorithm.
-
-        Returns
-        -------
-        :class:`MinimizerResult`
-            Object containing the parameters from the dual_annealing method.
-            The return values specific to :scipydoc:`optimize.dual_annealing`
-            (`x`, `fun`, `nfev`, `nhev`, `njev`, and `nit`) are stored as
-            `da_<parname>` attributes.
-
-
-        .. versionadded:: 0.9.14
-
-        """
-        result = self.prepare_fit(params=params)
-        result.method = 'dual_annealing'
-
-        da_kws = dict(maxiter=1000, local_search_options={},
-                      initial_temp=5230.0, restart_temp_ratio=2e-05,
-                      visit=2.62, accept=-5.0, maxfun=10000000.0, seed=None,
-                      no_local_search=False, callback=None, x0=None)
-
-        da_kws.update(self.kws)
-        da_kws.update(kws)
-
-        varying = np.asarray([par.vary for par in self.params.values()])
-        bounds = np.asarray([(par.min, par.max) for par in
-                             self.params.values()])[varying]
-
-        if not np.all(np.isfinite(bounds)):
-            raise ValueError('dual_annealing requires finite bounds for all'
-                             ' varying parameters')
-
-        try:
-            ret = scipy_dual_annealing(self.penalty, bounds, **da_kws)
-        except AbortFitException:
-            pass
-
-        if not result.aborted:
-            for attr, value in ret.items():
-                if attr in ['success', 'message']:
-                    setattr(result, attr, value)
-                else:
-                    setattr(result, 'da_{}'.format(attr), value)
-
-            result.residual = self.__residual(result.da_x, False)
-            result.nfev -= 1
-
-        result._calculate_statistics()
-
-        # calculate the cov_x and estimate uncertanties/correlations
-        if (not result.aborted and self.calc_covar and HAS_NUMDIFFTOOLS and
-                len(result.residual) > len(result.var_names)):
-            result.covar = self._calculate_covariance_matrix(result.da_x)
-            if result.covar is not None:
-                self._calculate_uncertainties_correlations()
-
-        return result
-
-    def minimize(self, method='leastsq', params=None, **kws):
-        """Perform the minimization.
-
-        Parameters
-        ----------
-        method : str, optional
-            Name of the fitting method to use. Valid values are:
-
-            - `'leastsq'`: Levenberg-Marquardt (default)
-            - `'least_squares'`: Least-Squares minimization, using Trust Region Reflective method
-            - `'differential_evolution'`: differential evolution
-            - `'brute'`: brute force method
-            - `'basinhopping'`: basinhopping
-            - `'ampgo'`: Adaptive Memory Programming for Global Optimization
-            - '`nelder`': Nelder-Mead
-            - `'lbfgsb'`: L-BFGS-B
-            - `'powell'`: Powell
-            - `'cg'`: Conjugate-Gradient
-            - `'newton'`: Newton-CG
-            - `'cobyla'`: Cobyla
-            - `'bfgs'`: BFGS
-            - `'tnc'`: Truncated Newton
-            - `'trust-ncg'`: Newton-CG trust-region
-            - `'trust-exact'`: nearly exact trust-region
-            - `'trust-krylov'`: Newton GLTR trust-region
-            - `'trust-constr'`: trust-region for constrained optimization
-            - `'dogleg'`: Dog-leg trust-region
-            - `'slsqp'`: Sequential Linear Squares Programming
-            - `'emcee'`: Maximum likelihood via Monte-Carlo Markov Chain
-            - `'shgo'`: Simplicial Homology Global Optimization
-            - `'dual_annealing'`: Dual Annealing optimization
-
-            In most cases, these methods wrap and use the method with the
-            same name from `scipy.optimize`, or use
-            `scipy.optimize.minimize` with the same `method` argument.
-            Thus '`leastsq`' will use `scipy.optimize.leastsq`, while
-            '`powell`' will use `scipy.optimize.minimizer(...,
-            method='powell')`
-
-            For more details on the fitting methods please refer to the
-            `SciPy docs <https://docs.scipy.org/doc/scipy/reference/optimize.html>`__.
-
-        params : :class:`~lmfit.parameter.Parameters`, optional
-            Parameters of the model to use as starting values.
-
-        **kws : optional
-            Additional arguments are passed to the underlying minimization
-            method.
-
-        Returns
-        -------
-        :class:`MinimizerResult`
-            Object containing the optimized parameter and several
-            goodness-of-fit statistics.
-
-
-        .. versionchanged:: 0.9.0
-           Return value changed to :class:`MinimizerResult`.
-
-        """
-        function = self.leastsq
-        kwargs = {'params': params}
-        kwargs.update(self.kws)
-        kwargs.update(kws)
-
-        user_method = method.lower()
-        if user_method.startswith('leasts'):
-            function = self.leastsq
-        elif user_method.startswith('least_s'):
-            function = self.least_squares
-        elif user_method == 'brute':
-            function = self.brute
-        elif user_method == 'basinhopping':
-            function = self.basinhopping
-        elif user_method == 'ampgo':
-            function = self.ampgo
-        elif user_method == 'emcee':
-            function = self.emcee
-        elif user_method == 'shgo':
-            function = self.shgo
-        elif user_method == 'dual_annealing':
-            function = self.dual_annealing
-        else:
-            function = self.scalar_minimize
-            for key, val in SCALAR_METHODS.items():
-                if (key.lower().startswith(user_method) or
-                        val.lower().startswith(user_method)):
-                    kwargs['method'] = val
-        return function(**kwargs)
-
-
-def _make_random_gen(seed):
-    """Turn seed into a numpy.random.RandomState instance.
-
-    If seed is None, return the RandomState singleton used by
-    numpy.random. If seed is an int, return a new RandomState instance
-    seeded with seed. If seed is already a RandomState instance, return
-    it. Otherwise raise ValueError.
-
-    """
-    if seed is None or seed is np.random:
-        return np.random.mtrand._rand
-    if isinstance(seed, (numbers.Integral, np.integer)):
-        return np.random.RandomState(seed)
-    if isinstance(seed, np.random.RandomState):
-        return seed
-    raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
-                     ' instance' % seed)
-
-
-def _nan_policy(arr, nan_policy='raise', handle_inf=True):
-    """Specify behaviour when an array contains numpy.nan or numpy.inf.
-
-    Parameters
-    ----------
-    arr : array_like
-        Input array to consider.
-    nan_policy : str, optional
-        One of:
-
-        'raise' - raise a `ValueError` if `arr` contains NaN (default)
-        'propagate' - propagate NaN
-        'omit' - filter NaN from input array
-    handle_inf : bool, optional
-        As well as NaN consider +/- inf.
-
-    Returns
-    -------
-    filtered_array : array_like
-
-    Note
-    ----
-    This function is copied, then modified, from
-    scipy/stats/stats.py/_contains_nan
-
-    """
-    if nan_policy not in ('propagate', 'omit', 'raise'):
-        raise ValueError("nan_policy must be 'propagate', 'omit', or 'raise'.")
-
-    if handle_inf:
-        handler_func = lambda x: ~np.isfinite(x)
-    else:
-        handler_func = isnull
-
-    if nan_policy == 'omit':
-        # mask locates any values to remove
-        mask = ~handler_func(arr)
-        if not np.all(mask):  # there are some NaNs/infs/missing values
-            return arr[mask]
-
-    if nan_policy == 'raise':
-        try:
-            # Calling np.sum to avoid creating a huge array into memory
-            # e.g. np.isnan(a).any()
-            with np.errstate(invalid='ignore'):
-                contains_nan = handler_func(np.sum(arr))
-        except TypeError:
-            # If the check cannot be properly performed we fallback to omiting
-            # nan values and raising a warning. This can happen when attempting to
-            # sum things that are not numbers (e.g. as in the function `mode`).
-            contains_nan = False
-            warnings.warn("The input array could not be checked for NaNs. "
-                          "NaNs will be ignored.", RuntimeWarning)
-
-        if contains_nan:
-            msg = ('NaN values detected in your input data or the output of '
-                   'your objective/model function - fitting algorithms cannot '
-                   'handle this! Please read https://lmfit.github.io/lmfit-py/faq.html#i-get-errors-from-nan-in-my-fit-what-can-i-do '
-                   'for more information.')
-            raise ValueError(msg)
-    return arr
-
-
-def minimize(fcn, params, method='leastsq', args=None, kws=None, iter_cb=None,
-             scale_covar=True, nan_policy='raise', reduce_fcn=None,
-             calc_covar=True, **fit_kws):
-    """Perform a fit of a set of parameters by minimizing an objective (or
-    cost) function using one of the several available methods.
-
-    The minimize function takes an objective function to be minimized,
-    a dictionary (:class:`~lmfit.parameter.Parameters`) containing the model
-    parameters, and several optional arguments.
-
-    Parameters
-    ----------
-    fcn : callable
-        Objective function to be minimized. When method is `leastsq` or
-        `least_squares`, the objective function should return an array
-        of residuals (difference between model and data) to be minimized
-        in a least-squares sense. With the scalar methods the objective
-        function can either return the residuals array or a single scalar
-        value. The function must have the signature:
-        `fcn(params, *args, **kws)`
-    params : :class:`~lmfit.parameter.Parameters`
-        Contains the Parameters for the model.
-    method : str, optional
-        Name of the fitting method to use. Valid values are:
-
-        - `'leastsq'`: Levenberg-Marquardt (default)
-        - `'least_squares'`: Least-Squares minimization, using Trust Region Reflective method
-        - `'differential_evolution'`: differential evolution
-        - `'brute'`: brute force method
-        - `'basinhopping'`: basinhopping
-        - `'ampgo'`: Adaptive Memory Programming for Global Optimization
-        - '`nelder`': Nelder-Mead
-        - `'lbfgsb'`: L-BFGS-B
-        - `'powell'`: Powell
-        - `'cg'`: Conjugate-Gradient
-        - `'newton'`: Newton-CG
-        - `'cobyla'`: Cobyla
-        - `'bfgs'`: BFGS
-        - `'tnc'`: Truncated Newton
-        - `'trust-ncg'`: Newton-CG trust-region
-        - `'trust-exact'`: nearly exact trust-region
-        - `'trust-krylov'`: Newton GLTR trust-region
-        - `'trust-constr'`: trust-region for constrained optimization
-        - `'dogleg'`: Dog-leg trust-region
-        - `'slsqp'`: Sequential Linear Squares Programming
-        - `'emcee'`: Maximum likelihood via Monte-Carlo Markov Chain
-        - `'shgo'`: Simplicial Homology Global Optimization
-        - `'dual_annealing'`: Dual Annealing optimization
-
-        In most cases, these methods wrap and use the method of the same
-        name from `scipy.optimize`, or use `scipy.optimize.minimize` with
-        the same `method` argument.  Thus '`leastsq`' will use
-        `scipy.optimize.leastsq`, while '`powell`' will use
-        `scipy.optimize.minimizer(..., method='powell')`
-
-        For more details on the fitting methods please refer to the
-        `SciPy docs <https://docs.scipy.org/doc/scipy/reference/optimize.html>`__.
-
-    args : tuple, optional
-        Positional arguments to pass to `fcn`.
-    kws : dict, optional
-        Keyword arguments to pass to `fcn`.
-    iter_cb : callable, optional
-        Function to be called at each fit iteration. This function should
-        have the signature `iter_cb(params, iter, resid, *args, **kws)`,
-        where `params` will have the current parameter values, `iter`
-        the iteration number, `resid` the current residual array, and `*args`
-        and `**kws` as passed to the objective function.
-    scale_covar : bool, optional
-        Whether to automatically scale the covariance matrix (default is True).
-    nan_policy : str, optional
-        Specifies action if `userfcn` (or a Jacobian) returns NaN
-        values. One of:
-
-        - 'raise' : a `ValueError` is raised
-        - 'propagate' : the values returned from `userfcn` are un-altered
-        - 'omit' : non-finite values are filtered
-
-    reduce_fcn : str or callable, optional
-        Function to convert a residual array to a scalar value for the scalar
-        minimizers. See notes in `Minimizer`.
-    calc_covar : bool, optional
-        Whether to calculate the covariance matrix (default is True) for
-        solvers other than `leastsq` and `least_squares`. Requires the
-        `numdifftools` package to be installed.
-    **fit_kws : dict, optional
-        Options to pass to the minimizer being used.
-
-    Returns
-    -------
-    :class:`MinimizerResult`
-        Object containing the optimized parameter and several
-        goodness-of-fit statistics.
-
-
-    .. versionchanged:: 0.9.0
-        Return value changed to :class:`MinimizerResult`.
-
-    Notes
-    -----
-    The objective function should return the value to be minimized. For the
-    Levenberg-Marquardt algorithm from leastsq(), this returned value must
-    be an array, with a length greater than or equal to the number of
-    fitting variables in the model. For the other methods, the return value
-    can either be a scalar or an array. If an array is returned, the sum of
-    squares of the array will be sent to the underlying fitting method,
-    effectively doing a least-squares optimization of the return values.
-
-    A common use for `args` and `kws` would be to pass in other
-    data needed to calculate the residual, including such things as the
-    data array, dependent variable, uncertainties in the data, and other
-    data structures for the model calculation.
-
-    On output, `params` will be unchanged.  The best-fit values and, where
-    appropriate, estimated uncertainties and correlations, will all be
-    contained in the returned :class:`MinimizerResult`.  See
-    :ref:`fit-results-label` for further details.
-
-    This function is simply a wrapper around :class:`Minimizer`
-    and is equivalent to::
-
-        fitter = Minimizer(fcn, params, fcn_args=args, fcn_kws=kws,
-                           iter_cb=iter_cb, scale_covar=scale_covar,
-                           nan_policy=nan_policy, reduce_fcn=reduce_fcn,
-                           calc_covar=calc_covar, **fit_kws)
-        fitter.minimize(method=method)
-
-    """
-    fitter = Minimizer(fcn, params, fcn_args=args, fcn_kws=kws,
-                       iter_cb=iter_cb, scale_covar=scale_covar,
-                       nan_policy=nan_policy, reduce_fcn=reduce_fcn,
-                       calc_covar=calc_covar, **fit_kws)
-    return fitter.minimize(method=method)
diff --git a/build/lib/gsf/plot_sed.py b/build/lib/gsf/plot_sed.py
deleted file mode 100644
index 6a5bd89..0000000
--- a/build/lib/gsf/plot_sed.py
+++ /dev/null
@@ -1,4122 +0,0 @@
-import numpy as np
-import sys
-import os
-import asdf
-
-import matplotlib.pyplot as plt
-from numpy import log10
-from scipy.integrate import simps
-from astropy.io import fits
-from matplotlib.ticker import FormatStrFormatter
-import matplotlib.ticker as ticker
-
-from .function import *
-from .function_class import Func
-from .basic_func import Basic
-import corner
-
-col = ['violet', 'indigo', 'b', 'lightblue', 'lightgreen', 'g', 'orange', 'coral', 'r', 'darkred']#, 'k']
-
-
-def plot_sed(MB, flim=0.01, fil_path='./', scale=1e-19, f_chind=True, figpdf=False, save_sed=True, 
-    mmax=300, dust_model=0, DIR_TMP='./templates/', f_label=False, f_bbbox=False, verbose=False, f_silence=True,
-    f_fill=False, f_fancyplot=False, f_Alog=True, dpi=300, f_plot_filter=True, f_plot_resid=False, NRbb_lim=10000,
-    x1min=4000):
-    '''
-    Parameters
-    ----------
-    MB.SNlim : float
-        SN limit to show flux or up lim in SED.
-    f_chind : bool
-        If include non-detection in chi2 calculation, using Sawicki12.
-    mmax : int
-        Number of mcmc realization for plot. Not for calculation.
-    f_fancy : bool
-        plot each SED component.
-    f_fill: bool
-        if True, and so is f_fancy, fill each SED component.
-
-    Returns
-    -------
-    plots
-
-    '''
-
-    from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
-    from mpl_toolkits.axes_grid1.inset_locator import mark_inset
-    from mpl_toolkits.axes_grid1.inset_locator import inset_axes
-    from scipy.optimize import curve_fit
-    from scipy import asarray as ar,exp
-    import matplotlib
-    import scipy.integrate as integrate
-    import scipy.special as special
-    import os.path
-    from astropy.io import ascii
-    import time
-
-    # if f_silence:
-    #     try:
-    #         import matplotlib
-    #         matplotlib.use("Agg")
-    #     except:
-    #         print('matplotlib Agg backend is not found.')
-    #         pass
-    # else:
-    #     try:
-    #         matplotlib.use("MacOSX")
-    #     except:
-    #         pass
-
-    def gaus(x,a,x0,sigma):
-        return a*exp(-(x-x0)**2/(2*sigma**2))
-
-    print('\n### Running plot_sed ###\n')
-
-    lcb = '#4682b4' # line color, blue
-
-    fnc  = MB.fnc 
-    bfnc = MB.bfnc
-    ID   = MB.ID
-    Z    = MB.Zall
-    age  = MB.age
-    nage = MB.nage 
-    tau0 = MB.tau0
-    
-    #col = ['violet', 'indigo', 'b', 'lightblue', 'lightgreen', 'g', 'orange', 'coral', 'r', 'darkred']#, 'k']
-    NUM_COLORS = len(age)
-    cm = plt.get_cmap('gist_rainbow')
-    col = [cm(1 - 1.*i/NUM_COLORS) for i in range(NUM_COLORS)]
-
-    nstep_plot = 1
-    if MB.f_bpass:
-        nstep_plot = 30
-
-    SNlim = MB.SNlim
-
-    ################
-    # RF colors.
-    home = os.path.expanduser('~')
-    c = MB.c
-    chimax = 1.
-    m0set = MB.m0set
-    Mpc_cm = MB.Mpc_cm
-
-    ##################
-    # Fitting Results
-    ##################
-    DIR_FILT = MB.DIR_FILT
-    SFILT = MB.filts
-
-    try:
-        f_err = MB.ferr
-    except:
-        f_err = 0
-
-    ###########################
-    # Open result file
-    ###########################
-    file = MB.DIR_OUT + 'summary_' + ID + '.fits'
-    hdul = fits.open(file)
-    
-    ndim_eff = hdul[0].header['NDIM']
-
-    # Redshift MC
-    zp16  = hdul[1].data['zmc'][0]
-    zp50  = hdul[1].data['zmc'][1]
-    zp84  = hdul[1].data['zmc'][2]
-
-    # Stellar mass MC
-    M16 = hdul[1].data['ms'][0]
-    M50 = hdul[1].data['ms'][1]
-    M84 = hdul[1].data['ms'][2]
-    if verbose:
-        print('Total stellar mass is %.2e'%(M50))
-
-    # Amplitude MC
-    A50 = np.zeros(len(age), dtype='float')
-    A16 = np.zeros(len(age), dtype='float')
-    A84 = np.zeros(len(age), dtype='float')
-    for aa in range(len(age)):
-        A16[aa] = 10**hdul[1].data['A'+str(aa)][0]
-        A50[aa] = 10**hdul[1].data['A'+str(aa)][1]
-        A84[aa] = 10**hdul[1].data['A'+str(aa)][2]
-
-    Asum  = np.sum(A50)
-
-    if MB.fneb:
-        logU50 = hdul[1].data['logU'][1]
-        Aneb50 = 10**hdul[1].data['Aneb'][1]
-
-    aa = 0
-    Av16 = hdul[1].data['Av'+str(aa)][0]
-    Av50 = hdul[1].data['Av'+str(aa)][1]
-    Av84 = hdul[1].data['Av'+str(aa)][2]
-    AAv = [Av50]
-
-    Z50 = np.zeros(len(age), dtype='float')
-    Z16 = np.zeros(len(age), dtype='float')
-    Z84 = np.zeros(len(age), dtype='float')
-    NZbest = np.zeros(len(age), dtype='int')
-    for aa in range(len(age)):
-        Z16[aa] = hdul[1].data['Z'+str(aa)][0]
-        Z50[aa] = hdul[1].data['Z'+str(aa)][1]
-        Z84[aa] = hdul[1].data['Z'+str(aa)][2]
-        NZbest[aa]= bfnc.Z2NZ(Z50[aa])
-
-    # Light weighted Z.
-    ZZ50 = np.sum(Z50*A50)/np.sum(A50)
-
-    # FIR Dust;
-    if MB.f_dust:
-        MD16 = hdul[1].data['MDUST'][0]
-        MD50 = hdul[1].data['MDUST'][1]
-        MD84 = hdul[1].data['MDUST'][2]
-        AD16 = hdul[1].data['ADUST'][0]
-        AD50 = hdul[1].data['ADUST'][1]
-        AD84 = hdul[1].data['ADUST'][2]
-        TD16 = hdul[1].data['TDUST'][0]
-        TD50 = hdul[1].data['TDUST'][1]
-        TD84 = hdul[1].data['TDUST'][2]
-        nTD16 = hdul[1].data['nTDUST'][0]
-        nTD50 = hdul[1].data['nTDUST'][1]
-        nTD84 = hdul[1].data['nTDUST'][2]
-        DFILT = MB.inputs['FIR_FILTER'] # filter band string.
-        DFILT = [x.strip() for x in DFILT.split(',')]
-        DFWFILT = fil_fwhm(DFILT, DIR_FILT)
-        if verbose:
-            print('Total dust mass is %.2e'%(MD50))
-
-    chi = hdul[1].data['chi'][0]
-    chin = hdul[1].data['chi'][1]
-    fitc = chin
-
-    Cz0 = hdul[0].header['Cz0']
-    Cz1 = hdul[0].header['Cz1']
-    Cz2 = hdul[0].header['Cz2']
-    zbes = zp50 
-    zscl = (1.+zbes)
-
-    ###############################
-    # Data taken from
-    ###############################
-    if MB.f_dust:
-        MB.dict = MB.read_data(Cz0, Cz1, Cz2, zbes, add_fir=True)
-    else:
-        MB.dict = MB.read_data(Cz0, Cz1, Cz2, zbes)
-
-    NR   = MB.dict['NR']
-    x    = MB.dict['x']
-    fy   = MB.dict['fy']
-    ey   = MB.dict['ey']
-    
-    con0 = (NR<1000)
-    xg0  = x[con0]
-    fg0  = fy[con0]
-    eg0  = ey[con0]
-    con1 = (NR>=1000) & (NR<2000)
-    xg1  = x[con1]
-    fg1  = fy[con1]
-    eg1  = ey[con1]
-    con2 = (NR>=1000) & (NR<NRbb_lim)
-    xg2  = x[con2]
-    fg2  = fy[con2]
-    eg2  = ey[con2]
-
-    if len(xg0)>0 or len(xg1)>0 or len(xg2)>0:
-        f_grsm = True
-    else:
-        f_grsm = False
-
-    wht = fy * 0
-    con_wht = (ey>0)
-    wht[con_wht] = 1./np.square(ey[con_wht])
-
-    # BB data points;
-    NRbb = MB.dict['NRbb']
-    xbb  = MB.dict['xbb']
-    fybb = MB.dict['fybb']
-    eybb = MB.dict['eybb']
-    exbb = MB.dict['exbb']
-    snbb = fybb/eybb
-
-    ######################
-    # Weight by line
-    ######################
-    wh0  = 1./np.square(eg0)
-    LW0  = []
-    model = fg0
-    wht3 = check_line_man(fy, x, wht, fy, zbes, LW0)
-
-    ######################
-    # Mass-to-Light ratio.
-    ######################
-    ms = np.zeros(len(age), dtype='float')
-    af = MB.af
-    sedpar = af['ML']
-
-    for aa in range(len(age)):
-        ms[aa] = sedpar['ML_' +  str(int(NZbest[aa]))][aa]
-    try:
-        isochrone = af['isochrone']
-        LIBRARY = af['library']
-        nimf = af['NIMF']
-    except:
-        isochrone = ''
-        LIBRARY = ''
-        nimf = ''
-
-    #############
-    # Plot.
-    #############
-    # Set the inset.
-    if f_grsm or MB.f_dust:
-        fig = plt.figure(figsize=(7.,3.2))
-        fig.subplots_adjust(top=0.98, bottom=0.16, left=0.1, right=0.99, hspace=0.15, wspace=0.25)
-        ax1 = fig.add_subplot(111)
-        xsize = 0.29
-        ysize = 0.25
-        if f_grsm:
-            ax2t = ax1.inset_axes((1-xsize-0.01,1-ysize-0.01,xsize,ysize))
-        if MB.f_dust:
-            ax3t = ax1.inset_axes((0.7,.35,.28,.25))
-
-        f_plot_resid = False
-        print('Grism data. f_plot_resid is turned off.')
-    else:
-        if f_plot_resid:
-            fig_mosaic = """
-            AAAA
-            AAAA
-            BBBB
-            """
-            fig,axes = plt.subplot_mosaic(mosaic=fig_mosaic, figsize=(5.5,4.))
-            fig.subplots_adjust(top=0.98, bottom=0.16, left=0.08, right=0.99, hspace=0.15, wspace=0.25)
-            ax1 = axes['A']
-        else:
-            if f_plot_filter:
-                fig = plt.figure(figsize=(5.5,2.))
-            else:
-                fig = plt.figure(figsize=(5.5,1.8))
-            fig.subplots_adjust(top=0.98, bottom=0.16, left=0.08, right=0.99, hspace=0.15, wspace=0.25)
-            ax1 = fig.add_subplot(111)
-
-    # Determine scale here;
-    if scale == None:
-        conbb_hs = (fybb/eybb > SNlim)
-        scale = 10**(int(np.log10(np.nanmax(fybb[conbb_hs] * c / np.square(xbb[conbb_hs])) / MB.d))) / 10
-    d = MB.d * scale
-
-    #######################################
-    # D.Kelson like Box for BB photometry
-    #######################################
-    col_dat = 'r'
-    if f_bbbox:
-        for ii in range(len(xbb)):
-            if eybb[ii]<100 and fybb[ii]/eybb[ii]>1:
-                xx = [xbb[ii]-exbb[ii],xbb[ii]-exbb[ii]]
-                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
-                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
-                xx = [xbb[ii]+exbb[ii],xbb[ii]+exbb[ii]]
-                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
-                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
-                xx = [xbb[ii]-exbb[ii],xbb[ii]+exbb[ii]]
-                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d]
-                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
-                xx = [xbb[ii]-exbb[ii],xbb[ii]+exbb[ii]]
-                yy = [(fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
-                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
-    else: # Normal BB plot;
-        # Detection;
-        conbb_hs = (fybb/eybb>SNlim)
-        ax1.errorbar(xbb[conbb_hs], fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d, \
-        yerr=eybb[conbb_hs]*c/np.square(xbb[conbb_hs])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
-        ax1.plot(xbb[conbb_hs], fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d, \
-        marker='.', color=col_dat, linestyle='', linewidth=0, zorder=4, ms=8)#, label='Obs.(BB)')
-        try:
-            # For any data removed fron fit (i.e. IRAC excess):
-            #data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
-            NR_ex = MB.data['bb_obs_removed']['NR']# data_ex['col1']
-        except:
-            NR_ex = []
-
-        # Upperlim;
-        sigma = 1.0
-        leng = np.max(fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d) * 0.05 #0.2
-        conebb_ls = (fybb/eybb<=SNlim) & (eybb>0)
-        
-        for ii in range(len(xbb)):
-            if NRbb[ii] in NR_ex[:]:
-                conebb_ls[ii] = False
-
-        ax1.errorbar(xbb[conebb_ls], eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, yerr=leng,\
-            uplims=eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, linestyle='', color=col_dat, marker='', ms=4, label='', zorder=4, capsize=3)
-
-
-    # For any data removed fron fit (i.e. IRAC excess):
-    f_exclude = False
-    try:
-        col_ex = 'lawngreen'
-        # Currently, this file is made after FILTER_SKIP;
-        # data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
-        # x_ex = data_ex['col2']
-        # fy_ex = data_ex['col3']
-        # ey_ex = data_ex['col4']
-        # ex_ex = data_ex['col5']
-        x_ex, fy_ex, ey_ex, ex_ex = MB.data['bb_obs_removed']['x'], MB.data['bb_obs_removed']['fy'], MB.data['bb_obs_removed']['ey'], MB.data['bb_obs_removed']['ex']
-
-        ax1.errorbar(
-            x_ex, fy_ex * c / np.square(x_ex) / d,
-            xerr=ex_ex, yerr=ey_ex*c/np.square(x_ex)/d, color='k', linestyle='', linewidth=0.5, zorder=5
-            )
-        ax1.scatter(
-            x_ex, fy_ex * c / np.square(x_ex) / d, marker='s', color=col_ex, edgecolor='k', zorder=5, s=30
-            )
-        f_exclude = True
-    except:
-        pass
-
-
-    #####################################
-    # Open ascii file and stock to array.
-    lib = fnc.open_spec_fits(fall=0)
-    lib_all = fnc.open_spec_fits(fall=1, orig=True)
-
-    if MB.f_dust:
-        MB.lib_dust = fnc.open_spec_dust_fits(fall=0)
-        MB.lib_dust_all = fnc.open_spec_dust_fits(fall=1)
-    if MB.fneb:
-        lib_neb = MB.fnc.open_spec_fits(fall=0, f_neb=True)
-        lib_neb_all = MB.fnc.open_spec_fits(fall=1, orig=True, f_neb=True)
-
-    iimax = len(nage)-1
-
-    # FIR dust plot;
-    if MB.f_dust:
-        from lmfit import Parameters
-        par = Parameters()
-        par.add('MDUST',value=AD50)
-        par.add('TDUST',value=nTD50)
-        par.add('zmc',value=zp50)
-
-        y0d, x0d = fnc.tmp04_dust(par.valuesdict())#, zbes, lib_dust_all)
-        y0d_cut, x0d_cut = fnc.tmp04_dust(par.valuesdict())#, zbes, lib_dust)
-
-        # data;
-        dat_d = ascii.read(MB.DIR_TMP + 'bb_dust_obs_' + MB.ID + '.cat')
-        NRbbd = dat_d['col1']
-        xbbd = dat_d['col2']
-        fybbd = dat_d['col3']
-        eybbd = dat_d['col4']
-        exbbd = dat_d['col5']
-        snbbd = fybbd/eybbd
-
-        try:
-            conbbd_hs = (fybbd/eybbd>SNlim)
-            ax1.errorbar(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
-            yerr=eybbd[conbbd_hs]*c/np.square(xbbd[conbbd_hs])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
-            ax1.plot(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
-            '.r', linestyle='', linewidth=0, zorder=4)#, label='Obs.(BB)')
-            ax3t.plot(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
-            '.r', linestyle='', linewidth=0, zorder=4)#, label='Obs.(BB)')
-        except:
-            pass
-
-        try:
-            conebbd_ls = (fybbd/eybbd<=SNlim)
-            ax1.errorbar(xbbd[conebbd_ls], eybbd[conebbd_ls] * c / np.square(xbbd[conebbd_ls]) / d, \
-            yerr=fybbd[conebbd_ls]*0+np.max(fybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d)*0.05, \
-            uplims=eybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d, color='r', linestyle='', linewidth=0.5, zorder=4)
-            ax3t.errorbar(xbbd[conebbd_ls], eybbd[conebbd_ls] * c / np.square(xbbd[conebbd_ls]) / d, \
-            yerr=fybbd[conebbd_ls]*0+np.max(fybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d)*0.05, \
-            uplims=eybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d, color='r', linestyle='', linewidth=0.5, zorder=4)
-        except:
-            pass
-
-
-    #
-    # This is for UVJ color time evolution.
-    #
-    Asum = np.sum(A50[:])
-    alp = .5
-
-    for jj in range(len(age)):
-        ii = int(len(nage) - jj - 1) # from old to young templates.
-        if jj == 0:
-            y0, x0 = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib_all)
-            y0p, x0p = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib)
-
-            ysum = y0
-            ysump = y0p
-            nopt = len(ysump)
-
-            f_50_comp = np.zeros((len(age),len(y0)),'float') 
-            # Keep each component;
-            f_50_comp[ii,:] = y0[:] * c / np.square(x0) / d
-
-            if MB.f_dust:
-                ysump[:] += y0d_cut[:nopt]
-                ysump = np.append(ysump,y0d_cut[nopt:])
-                # Keep each component;
-                f_50_comp_dust = y0d * c / np.square(x0d) / d
-
-            if MB.fneb: 
-                # Only at one age pixel;
-                y0_r, x0_tmp = fnc.get_template_single(Aneb50, AAv[0], ii, Z50[ii], zbes, lib_neb_all, logU=logU50)
-                y0p, x0p = fnc.get_template_single(Aneb50, AAv[0], ii, Z50[ii], zbes, lib_neb, logU=logU50)
-                ysum += y0_r
-                ysump[:nopt] += y0p
-
-        else:
-            y0_r, x0_tmp = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib_all)
-            y0p, x0p = fnc.get_template_single(A50[ii], AAv[0], ii, Z50[ii], zbes, lib)
-            ysum += y0_r
-            ysump[:nopt] += y0p
-
-            f_50_comp[ii,:] = y0_r[:] * c / np.square(x0_tmp) / d
-
-        # The following needs revised.
-        f_uvj = False
-        if f_uvj:
-            if jj == 0:
-                fwuvj = open(MB.DIR_OUT + ID + '_uvj.txt', 'w')
-                fwuvj.write('# age uv vj\n')
-            ysum_wid = ysum * 0
-            for kk in range(0,ii+1,1):
-                tt = int(len(nage) - kk - 1)
-                nn = int(len(nage) - ii - 1)
-
-                nZ = bfnc.Z2NZ(Z50[tt])
-                y0_wid, x0_wid = fnc.open_spec_fits_dir(tt, nZ, nn, AAv[0], zbes, A50[tt])
-                ysum_wid += y0_wid
-
-            lmrest_wid = x0_wid/(1.+zbes)
-            band0 = ['u','v','j']
-            lmconv,fconv = filconv(band0, lmrest_wid, ysum_wid, fil_path) # f0 in fnu
-            fu_t = fconv[0]
-            fv_t = fconv[1]
-            fj_t = fconv[2]
-            uvt = -2.5*log10(fu_t/fv_t)
-            vjt = -2.5*log10(fv_t/fj_t)
-            fwuvj.write('%.2f %.3f %.3f\n'%(age[ii], uvt, vjt))
-            fwuvj.close()
-
-
-    #############
-    # Main result
-    #############
-    conbb_ymax = (xbb>0) & (fybb>0) & (eybb>0) & (fybb/eybb>SNlim)
-    ymax = np.max(fybb[conbb_ymax]*c/np.square(xbb[conbb_ymax])/d) * 1.6
-
-    xboxl = 17000
-    xboxu = 28000
-
-    ax1.set_xlabel('Observed wavelength [$\mathrm{\mu m}$]', fontsize=11)
-    ax1.set_ylabel('$f_\lambda$ [$10^{%d}\mathrm{erg}/\mathrm{s}/\mathrm{cm}^{2}/\mathrm{\AA}$]'%(np.log10(scale)),fontsize=11,labelpad=2)
-
-    x1max = 100000
-    if x1max < np.max(xbb):
-        x1max = np.max(xbb) * 1.5
-    if x1min > np.min(xbb[conbb_ymax]):
-        x1min = np.min(xbb[conbb_ymax]) / 1.5
-
-    xticks = [2500, 5000, 10000, 20000, 40000, 80000, x1max]
-    xlabels= ['0.25', '0.5', '1', '2', '4', '8', '']
-    if MB.f_dust:
-        x1max = 400000
-        xticks = [2500, 5000, 10000, 20000, 40000, 80000, 400000]
-        xlabels= ['0.25', '0.5', '1', '2', '4', '8', '']
-
-    if x1min > 2500:
-        xticks = xticks[1:]
-        xlabels = xlabels[1:]
-
-    ax1.set_xlim(x1min, x1max)
-    ax1.set_xscale('log')
-    if f_plot_filter:
-        scl_yaxis = 0.2
-    else:
-        scl_yaxis = 0.1
-    ax1.set_ylim(-ymax*scl_yaxis,ymax)
-
-    if False:
-        ax1.text(x1min+100,-ymax*0.08,'SNlimit:%.1f'%(SNlim),fontsize=7)
-
-    ax1.set_xticks(xticks)
-    ax1.set_xticklabels(xlabels)
-
-    if False:
-        dely1 = 0.5
-        while (ymax-0)/dely1<1:
-            dely1 /= 2.
-        while (ymax-0)/dely1>4:
-            dely1 *= 2.
-
-        y1ticks = np.arange(0, ymax, dely1)
-        ax1.set_yticks(y1ticks)
-        ax1.set_yticklabels(np.arange(0, ymax, dely1), minor=False)
-        ax1.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
-        ax1.yaxis.labelpad = 1.5
-
-    xx = np.arange(100,400000)
-    yy = xx * 0
-    ax1.plot(xx, yy, ls='--', lw=0.5, color='k')
-
-    #############
-    # Plot
-    #############
-    eAAl = np.zeros(len(age),dtype='float')
-    eAAu = np.zeros(len(age),dtype='float')
-    eAMl = np.zeros(len(age),dtype='float')
-    eAMu = np.zeros(len(age),dtype='float')
-    MSsum = np.sum(ms)
-    Asum = np.sum(A50)
-    A50 /= Asum
-    A16 /= Asum
-    A84 /= Asum
-
-    AM50 = A50 * M50 * ms / MSsum
-    CM = M50/np.sum(AM50)
-
-    AM50 = A50 * M50 * ms / MSsum * CM
-    AM16 = A16 * M50 * ms / MSsum * CM
-    AM84 = A84 * M50 * ms / MSsum * CM
-
-    AC50 = A50 * 0 # Cumulative
-    for ii in range(len(A50)):
-        eAAl[ii] = A50[ii] - A16[ii]
-        eAAu[ii] = A84[ii] - A50[ii]
-        eAMl[ii] = AM50[ii] - AM16[ii]
-        eAMu[ii] = AM84[ii] - AM50[ii]
-        AC50[ii] = np.sum(AM50[ii:])
-
-    ################
-    # Lines
-    ################
-    LN = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3', 'O3', 'Mgb', 'Halpha', 'S2L', 'S2H']
-    FLW = np.zeros(len(LN),dtype='int')
-
-    ####################
-    # For cosmology
-    ####################
-    DL = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm #, **cosmo) # Luminositydistance in cm
-    Cons = (4.*np.pi*DL**2/(1.+zbes))
-    if f_grsm:
-        print('This function (write_lines) needs to be revised.')
-        write_lines(ID, zbes, DIR_OUT=MB.DIR_OUT)
-
-
-    ##########################
-    # Zoom in Line regions
-    ##########################
-    if f_grsm:
-        conspec = (NR<10000) #& (fy/ey>1)
-        ax2t.errorbar(xg2, fg2 * c/np.square(xg2)/d, yerr=eg2 * c/np.square(xg2)/d, lw=0.5, color='#DF4E00', zorder=10, alpha=1., label='', capsize=0)
-        ax2t.errorbar(xg1, fg1 * c/np.square(xg1)/d, yerr=eg1 * c/np.square(xg1)/d, lw=0.5, color='g', zorder=10, alpha=1., label='', capsize=0)
-        ax2t.errorbar(xg0, fg0 * c/np.square(xg0)/d, yerr=eg0 * c/np.square(xg0)/d, lw=0.5, linestyle='', color='royalblue', zorder=10, alpha=1., label='', capsize=0)
-
-        xgrism = np.concatenate([xg0,xg1,xg2])
-        fgrism = np.concatenate([fg0,fg1,fg2])
-        egrism = np.concatenate([eg0,eg1,eg2])
-        con4000b = (xgrism/zscl>3400) & (xgrism/zscl<3800) & (fgrism>0) & (egrism>0)
-        con4000r = (xgrism/zscl>4200) & (xgrism/zscl<5000) & (fgrism>0) & (egrism>0)
-        print('Median SN at 3400-3800 is;', np.median((fgrism/egrism)[con4000b]))
-        print('Median SN at 4200-5000 is;', np.median((fgrism/egrism)[con4000r]))
-
-
-    #
-    # From MCMC chain
-    #
-    file = MB.DIR_OUT + 'chain_' + ID + '_corner.cpkl'
-    niter = 0
-    data = loadcpkl(file)
-    try:
-        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
-        burnin = data['burnin']
-        nmc    = data['niter']
-        nwalk  = data['nwalkers']
-        Nburn  = burnin #*20
-        res    = data['chain'][:]
-    except:
-        if verbose: print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
-
-    samples = res
-
-    # Saved template;
-    ytmp = np.zeros((mmax,len(ysum)), dtype='float')
-    ytmp_each = np.zeros((mmax,len(ysum),len(age)), dtype='float')
-    ytmp_nl = np.zeros((mmax,len(ysum)), dtype='float') # no line
-
-    ytmpmax = np.zeros(len(ysum), dtype='float')
-    ytmpmin = np.zeros(len(ysum), dtype='float')
-
-    # MUV;
-    DL      = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm # Luminositydistance in cm
-    DL10    = Mpc_cm/1e6 * 10 # 10pc in cm
-    Fuv     = np.zeros(mmax, dtype='float') # For Muv
-    Fuv28   = np.zeros(mmax, dtype='float') # For Fuv(1500-2800)
-    Lir     = np.zeros(mmax, dtype='float') # For L(8-1000um)
-    UVJ     = np.zeros((mmax,4), dtype='float') # For UVJ color;
-    Cmznu   = 10**((48.6+m0set)/(-2.5)) # Conversion from m0_25 to fnu
-
-    # From random chain;
-    alp=0.02
-    for kk in range(0,mmax,1):
-        nr = np.random.randint(Nburn, len(samples['A%d'%MB.aamin[0]]))
-        try:
-            Av_tmp = samples['Av'][nr]
-        except:
-            Av_tmp = MB.AVFIX
-
-        try:
-            zmc = samples['zmc'][nr]
-        except:
-            zmc = zbes
-
-        for ss in MB.aamin:
-            try:
-                AA_tmp = 10**samples['A'+str(ss)][nr]
-            except:
-                AA_tmp = 0
-                pass
-            try:
-                Ztest = samples['Z'+str(len(age)-1)][nr]
-                ZZ_tmp = samples['Z'+str(ss)][nr]
-            except:
-                try:
-                    ZZ_tmp = samples['Z0'][nr]
-                except:
-                    ZZ_tmp = MB.ZFIX
-
-            if ss == MB.aamin[0]:
-                mod0_tmp, xm_tmp = fnc.get_template_single(AA_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_all)
-                fm_tmp = mod0_tmp.copy()
-                fm_tmp_nl = mod0_tmp.copy()
-                if MB.fneb:
-                    Aneb_tmp = 10**samples['Aneb'][nr]
-                    if not MB.logUFIX == None:
-                        logU_tmp = MB.logUFIX
-                    else:
-                        logU_tmp = samples['logU'][nr]
-                    mod0_tmp, xm_tmp = fnc.get_template_single(Aneb_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_neb_all, logU=logU_tmp)
-                    fm_tmp += mod0_tmp
-                    # Make no emission line template;
-                    mod0_tmp_nl, xm_tmp_nl = fnc.get_template_single(0, Av_tmp, ss, ZZ_tmp, zmc, lib_neb_all, logU=logU_tmp)
-                    fm_tmp_nl += mod0_tmp_nl
-            else:
-                mod0_tmp, xx_tmp = fnc.get_template_single(AA_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_all)
-                fm_tmp += mod0_tmp
-                fm_tmp_nl += mod0_tmp
-
-            # Each;
-            ytmp_each[kk,:,ss] = mod0_tmp[:] * c / np.square(xm_tmp[:]) / d
-
-        #
-        # Dust component;
-        #
-        if MB.f_dust:
-            if kk == 0:
-                par = Parameters()
-                par.add('MDUST',value=samples['MDUST'][nr])
-                try:
-                    par.add('TDUST',value=samples['TDUST'][nr])
-                except:
-                    par.add('TDUST',value=0)
-
-            par['MDUST'].value = samples['MDUST'][nr]
-            if not MB.TDUSTFIX == None:
-                par['TDUST'].value = MB.NTDUST
-            else:
-                par['TDUST'].value = samples['TDUST'][nr]
-                #except:
-                #    par['TDUST'].value = 0
-
-            model_dust, x1_dust = fnc.tmp04_dust(par.valuesdict())
-            if kk == 0:
-                deldt  = (x1_dust[1] - x1_dust[0])
-                x1_tot = np.append(xm_tmp,np.arange(np.max(xm_tmp),np.max(x1_dust)*2,deldt))
-                # Redefine??
-                ytmp = np.zeros((mmax,len(x1_tot)), dtype='float')
-                ytmp_dust = np.zeros((mmax,len(x1_dust)), dtype='float')
-                ytmp_comp = np.zeros((mmax,len(x1_tot)), dtype='float')
-
-            ytmp_dust[kk,:] = model_dust * c/np.square(x1_dust)/d
-            model_tot = np.interp(x1_tot,xx_tmp,fm_tmp) + np.interp(x1_tot,x1_dust,model_dust)
-            model_tot_nl = np.interp(x1_tot,xx_tmp,fm_tmp_nl) + np.interp(x1_tot,x1_dust,model_dust)
-
-            ytmp[kk,:] = model_tot[:] * c/np.square(x1_tot[:])/d
-            ytmp_nl[kk,:] = model_tot_nl[:] * c/np.square(x1_tot[:])/d
-
-        else:
-            x1_tot = xm_tmp
-            ytmp[kk,:] = fm_tmp[:] * c / np.square(xm_tmp[:]) / d
-            ytmp_nl[kk,:] = fm_tmp_nl[:] * c / np.square(xm_tmp[:]) / d
-
-        #
-        # Grism plot + Fuv flux + LIR.
-        #
-        #if f_grsm:
-            #ax2t.plot(x1_tot, ytmp[kk,:], '-', lw=0.5, color='gray', zorder=3., alpha=0.02)
-
-        # Get FUV flux density;
-        Fuv[kk] = get_Fuv(x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)) * (DL**2/(1.+zbes)) / (DL10**2), lmin=1250, lmax=1650)
-        Fuv28[kk] = get_Fuv(x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)) * (4*np.pi*DL**2/(1.+zbes))*Cmznu, lmin=1500, lmax=2800)
-        Lir[kk] = 0
-
-        # Get UVJ Color;
-        lmconv,fconv = filconv_fast(MB.filts_rf, MB.band_rf, x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)))
-        UVJ[kk,0] = -2.5*np.log10(fconv[0]/fconv[2])
-        UVJ[kk,1] = -2.5*np.log10(fconv[1]/fconv[2])
-        UVJ[kk,2] = -2.5*np.log10(fconv[2]/fconv[3])
-        UVJ[kk,3] = -2.5*np.log10(fconv[4]/fconv[3])
-
-        # Do stuff...
-        time.sleep(0.01)
-        # Update Progress Bar
-        printProgressBar(kk, mmax, prefix = 'Progress:', suffix = 'Complete', length = 40)
-
-    #
-    # Plot Median SED;
-    #
-    ytmp16 = np.percentile(ytmp[:,:],16,axis=0)
-    ytmp50 = np.percentile(ytmp[:,:],50,axis=0)
-    ytmp84 = np.percentile(ytmp[:,:],84,axis=0)
-    ytmp16_nl = np.percentile(ytmp_nl[:,:],16,axis=0)
-    ytmp50_nl = np.percentile(ytmp_nl[:,:],50,axis=0)
-    ytmp84_nl = np.percentile(ytmp_nl[:,:],84,axis=0)
-    
-    if MB.f_dust:
-        ytmp_dust50 = np.percentile(ytmp_dust[:,:],50, axis=0)
-
-    #if not f_fill:
-    ax1.fill_between(x1_tot[::nstep_plot], ytmp16[::nstep_plot], ytmp84[::nstep_plot], ls='-', lw=.5, color='gray', zorder=-2, alpha=0.5)
-    ax1.plot(x1_tot[::nstep_plot], ytmp50[::nstep_plot], '-', lw=.5, color='gray', zorder=-1, alpha=1.)
-
-    # For grism;
-    if f_grsm:
-        from astropy.convolution import convolve
-        from .maketmp_filt import get_LSF
-        LSF, _ = get_LSF(MB.inputs, MB.DIR_EXTR, ID, x1_tot[:]/(1.+zbes), c=3e18)
-        try:
-            spec_grsm16 = convolve(ytmp16[:], LSF, boundary='extend')
-            spec_grsm50 = convolve(ytmp50[:], LSF, boundary='extend')
-            spec_grsm84 = convolve(ytmp84[:], LSF, boundary='extend')
-        except:
-            spec_grsm16 = ytmp16[:]
-            spec_grsm50 = ytmp50[:]
-            spec_grsm84 = ytmp84[:]
-
-        if True:
-            ax2t.plot(x1_tot[:], ytmp50, '-', lw=0.5, color='gray', zorder=3., alpha=1.0)
-        else:
-            ax2t.plot(x1_tot[:], spec_grsm50, '-', lw=0.5, color='gray', zorder=3., alpha=1.0)
-        
-    # Attach the data point in MB;
-    MB.sed_wave_obs = xbb
-    MB.sed_flux_obs = fybb * c / np.square(xbb) / d
-    MB.sed_eflux_obs = eybb * c / np.square(xbb) / d
-    # Attach the best SED to MB;
-    MB.sed_wave = x1_tot
-    MB.sed_flux16 = ytmp16
-    MB.sed_flux50 = ytmp50
-    MB.sed_flux84 = ytmp84
-
-    if f_fancyplot:
-        alp_fancy = 0.5
-        #ax1.plot(x1_tot[::nstep_plot], np.percentile(ytmp[:, ::nstep_plot], 50, axis=0), '-', lw=.5, color='gray', zorder=-1, alpha=1.)
-        ysumtmp = ytmp[0, ::nstep_plot] * 0
-        ysumtmp2 = ytmp[:, ::nstep_plot] * 0
-        ysumtmp2_prior = ytmp[0, ::nstep_plot] * 0
-        for ss in range(len(age)):
-            ii = int(len(nage) - ss - 1)
-            # !! Take median after summation;
-            ysumtmp2[:,:len(xm_tmp)] += ytmp_each[:, ::nstep_plot, ii]
-            if f_fill:
-                ax1.fill_between(x1_tot[::nstep_plot], ysumtmp2_prior,  np.percentile(ysumtmp2[:,:], 50, axis=0), linestyle='None', lw=0., color=col[ii], alpha=alp_fancy, zorder=-3)
-            else:
-                ax1.plot(x1_tot[::nstep_plot], np.percentile(ysumtmp2[:, ::nstep_plot], 50, axis=0), linestyle='--', lw=.5, color=col[ii], alpha=alp_fancy, zorder=-3)
-            ysumtmp2_prior[:] = np.percentile(ysumtmp2[:, :], 50, axis=0)
-    elif f_fill:
-        print('f_fancyplot is False. f_fill is set to False.')
-
-    #########################
-    # Calculate non-det chi2
-    # based on Sawick12
-    #########################
-    def func_tmp(xint,eobs,fmodel):
-        int_tmp = np.exp(-0.5 * ((xint-fmodel)/eobs)**2)
-        return int_tmp
-
-    if f_chind:
-        conw = (wht3>0) & (ey>0) & (fy/ey>SNlim)
-    else:
-        conw = (wht3>0) & (ey>0) #& (fy/ey>SNlim)
-
-    #chi2 = sum((np.square(fy-ysump) * np.sqrt(wht3))[conw])
-    try:
-        logf = hdul[1].data['logf'][1]
-        ey_revised = np.sqrt(ey**2+ ysump**2 * np.exp(logf)**2)
-    except:
-        ey_revised = ey
-
-    chi2 = sum((np.square(fy-ysump) / ey_revised)[conw])
-
-    chi_nd = 0.0
-    if f_chind:
-        f_ex = np.zeros(len(fy), 'int')
-        if f_exclude:
-            for ii in range(len(fy)):
-                if x[ii] in x_ex:
-                    f_ex[ii] = 1
-
-        con_up = (ey>0) & (fy/ey<=SNlim) & (f_ex == 0)
-        from scipy import special
-        x_erf = (ey_revised[con_up] - ysump[con_up]) / (np.sqrt(2) * ey_revised[con_up])
-        f_erf = special.erf(x_erf)
-        chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey_revised[con_up] * (1 + f_erf)) )
-
-    # Number of degree;
-    con_nod = (wht3>0) & (ey>0) #& (fy/ey>SNlim)
-    nod = int(len(wht3[con_nod])-ndim_eff)
-
-    print('\n')
-    print('No-of-detection    : %d'%(len(wht3[conw])))
-    print('chi2               : %.2f'%(chi2))
-    if f_chind:
-        print('No-of-non-detection: %d'%(len(ey[con_up])))
-        print('chi2 for non-det   : %.2f'%(- 2 * chi_nd))
-    print('No-of-params       : %d'%(ndim_eff))
-    print('Degrees-of-freedom : %d'%(nod))
-    if nod>0:
-        fin_chi2 = (chi2 - 2 * chi_nd) / nod
-    else:
-        fin_chi2 = -99
-    print('Final chi2/nu      : %.2f'%(fin_chi2))
-
-    #
-    # plot BB model from best template (blue squares)
-    #
-    col_dia = 'blue'
-    if MB.f_dust:
-        ALLFILT = np.append(SFILT,DFILT)
-        #for ii in range(len(x1_tot)):
-        #    print(x1_tot[ii], model_tot[ii]*c/np.square(x1_tot[ii])/d)
-        lbb, fbb, lfwhm = filconv(ALLFILT, x1_tot, ytmp50, DIR_FILT, fw=True)
-        lbb, fbb16, lfwhm = filconv(ALLFILT, x1_tot, ytmp16, DIR_FILT, fw=True)
-        lbb, fbb84, lfwhm = filconv(ALLFILT, x1_tot, ytmp84, DIR_FILT, fw=True)
-
-        ax1.plot(x1_tot, ytmp50, '--', lw=0.5, color='purple', zorder=-1, label='')
-        ax3t.plot(x1_tot, ytmp50, '--', lw=0.5, color='purple', zorder=-1, label='')
-
-        iix = []
-        for ii in range(len(fbb)):
-            iix.append(ii)
-        con_sed = ()
-        ax1.scatter(lbb[iix][con_sed], fbb[iix][con_sed], lw=0.5, color='none', edgecolor=col_dia, zorder=3, alpha=1.0, marker='d', s=50)
-
-        # plot FIR range;
-        ax3t.scatter(lbb, fbb, lw=0.5, color='none', edgecolor=col_dia, \
-        zorder=2, alpha=1.0, marker='d', s=50)
-
-    else:
-        lbb, fbb, lfwhm = filconv(SFILT, x1_tot, ytmp50, DIR_FILT, fw=True, MB=MB, f_regist=False)
-        lbb, fbb16, lfwhm = filconv(SFILT, x1_tot, ytmp16, DIR_FILT, fw=True, MB=MB, f_regist=False)
-        lbb, fbb84, lfwhm = filconv(SFILT, x1_tot, ytmp84, DIR_FILT, fw=True, MB=MB, f_regist=False)
-
-        iix = []
-        for ii in range(len(fbb)):
-            iix.append(np.argmin(np.abs(lbb[ii]-xbb[:])))
-        con_sed = (eybb>0)
-        ax1.scatter(lbb[iix][con_sed], fbb[iix][con_sed], lw=0.5, color='none', edgecolor=col_dia, zorder=3, alpha=1.0, marker='d', s=50)
-
-        if f_plot_resid:
-            conbb_hs = (fybb/eybb>SNlim)
-            axes['B'].scatter(lbb[iix][conbb_hs], ((fybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs], lw=0.5, color='none', edgecolor='r', zorder=3, alpha=1.0, marker='.', s=50)
-            conbb_hs = (fybb/eybb<=SNlim) & (eybb>0)
-            axes['B'].errorbar(lbb[iix][conbb_hs], ((eybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs], yerr=leng,\
-                uplims=((fybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs] * sigma, linestyle='',\
-                color=col_dat, lw=0.5, marker='', ms=4, label='', zorder=4, capsize=1.5)
-            axes['B'].set_xscale(ax1.get_xscale())
-            axes['B'].set_xlim(ax1.get_xlim())
-            axes['B'].set_xticks(ax1.get_xticks())
-            axes['B'].set_xticklabels(ax1.get_xticklabels())
-            axes['B'].set_xlabel(ax1.get_xlabel())
-            xx = np.arange(axes['B'].get_xlim()[0],axes['B'].get_xlim()[1],100)
-            axes['B'].plot(xx,xx*0,linestyle='--',lw=0.5,color='k')
-            axes['B'].set_ylabel('Residual / $\sigma$')
-            axes['A'].set_xlabel('')
-            axes['A'].set_xticks(ax1.get_xticks())
-            axes['A'].set_xticklabels('')
-
-        # Calculate EW, if there is excess band;
-        try:
-            iix2 = []
-            for ii in range(len(fy_ex)):
-                iix2.append(np.argmin(np.abs(lbb[:]-x_ex[ii])))
-
-            # Rest-frame EW;
-            # Note about 16/84 in fbb
-            EW16 = (fy_ex * c / np.square(x_ex) / d - fbb84[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
-            EW50 = (fy_ex * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
-            EW84 = (fy_ex * c / np.square(x_ex) / d - fbb16[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
-
-            EW50_er1 = ((fy_ex-ey_ex) * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
-            EW50_er2 = ((fy_ex+ey_ex) * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
-
-            cnt50 = fbb[iix2]
-            cnt16 = fbb16[iix2]
-            cnt84 = fbb84[iix2]
- 
-            # Luminosity;
-            #Lsun = 3.839 * 1e33 #erg s-1
-            L16 = EW16 * cnt16 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
-            L50 = EW50 * cnt50 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
-            L84 = EW84 * cnt84 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
-
-            ew_label = []
-            for ii in range(len(fy_ex)):
-                lres = MB.band['%s_lam'%MB.filts[iix2[ii]]][:]
-                fres = MB.band['%s_res'%MB.filts[iix2[ii]]][:]
-                ew_label.append(MB.filts[iix2[ii]])
-
-                print('\n')
-                print('EW016 for', x_ex[ii], 'is %d'%EW16[ii])
-                print('EW050 for', x_ex[ii], 'is %d'%EW50[ii])
-                print('EW084 for', x_ex[ii], 'is %d'%EW84[ii])
-                print('%d_{-%d}^{+%d} , for sed error'%(EW50[ii],EW50[ii]-EW84[ii],EW16[ii]-EW50[ii]))
-                print('Or, %d\pm{%d} , for flux error'%(EW50[ii],EW50[ii]-EW50_er1[ii]))
-        except:
-            pass
-
-
-    if save_sed:
-        fbb16_nu = flamtonu(lbb, fbb16*scale, m0set=m0set)
-        fbb_nu = flamtonu(lbb, fbb*scale, m0set=m0set)
-        fbb84_nu = flamtonu(lbb, fbb84*scale, m0set=m0set)
-
-        # Then save full spectrum;
-        col00  = []
-        col1  = fits.Column(name='wave_model', format='E', unit='AA', array=x1_tot)
-        col00.append(col1)
-        col2  = fits.Column(name='f_model_16', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp16[:])
-        col00.append(col2)
-        col3  = fits.Column(name='f_model_50', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp50[:])
-        col00.append(col3)
-        col4  = fits.Column(name='f_model_84', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp84[:])
-        col00.append(col4)
-        col2  = fits.Column(name='f_model_noline_16', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp16_nl[:])
-        col00.append(col2)
-        col3  = fits.Column(name='f_model_noline_50', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp50_nl[:])
-        col00.append(col3)
-        col4  = fits.Column(name='f_model_noline_84', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp84_nl[:])
-        col00.append(col4)
-
-        # Each component
-        # Stellar
-        col1 = fits.Column(name='wave_model_stel', format='E', unit='AA', array=x0)
-        col00.append(col1)
-        for aa in range(len(age)):
-            col1 = fits.Column(name='f_model_stel_%d'%aa, format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=f_50_comp[aa,:])
-            col00.append(col1)
-        if MB.f_dust:
-            col1 = fits.Column(name='wave_model_dust', format='E', unit='AA', array=x1_dust)
-            col00.append(col1)
-            col1 = fits.Column(name='f_model_dust', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp_dust50)
-            col00.append(col1)
-            
-        # Grism;
-        if f_grsm:
-            col2 = fits.Column(name='f_model_conv_16', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=spec_grsm16)
-            col00.append(col2)
-            col3 = fits.Column(name='f_model_conv_50', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=spec_grsm50)
-            col00.append(col3)
-            col4 = fits.Column(name='f_model_conv_84', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=spec_grsm84)
-            col00.append(col4)
-
-        # BB for dust
-        if MB.f_dust:
-            xbb = np.append(xbb,xbbd)
-            fybb = np.append(fybb,fybbd)
-            eybb = np.append(eybb,eybbd)
-
-        col5 = fits.Column(name='wave_obs', format='E', unit='AA', array=xbb)
-        col00.append(col5)
-        col6 = fits.Column(name='f_obs', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=fybb[:] * c / np.square(xbb[:]) / d)
-        col00.append(col6)
-        col7 = fits.Column(name='e_obs', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=eybb[:] * c / np.square(xbb[:]) / d)
-        col00.append(col7)
-
-        hdr = fits.Header()
-        hdr['redshift'] = zbes
-        hdr['id'] = ID
-        hdr['hierarch isochrone'] = isochrone
-        hdr['library'] = LIBRARY
-        hdr['nimf'] = nimf
-        hdr['scale'] = scale
-
-        try:
-            # Chi square:
-            hdr['chi2'] = chi2
-            hdr['hierarch No-of-effective-data-points'] = len(wht3[conw])
-            hdr['hierarch No-of-nondetectioin'] = len(ey[con_up])
-            hdr['hierarch Chi2-of-nondetection'] = chi_nd
-            hdr['hierarch No-of-params'] = ndim_eff
-            hdr['hierarch Degree-of-freedom']  = nod
-            hdr['hierarch reduced-chi2'] = fin_chi2
-        except:
-            print('Chi seems to be wrong...')
-            pass
-
-        try:
-            # Muv
-            MUV = -2.5 * np.log10(Fuv[:]) + MB.m0set
-            hdr['MUV16'] = -2.5 * np.log10(np.percentile(Fuv[:],16)) + MB.m0set
-            hdr['MUV50'] = -2.5 * np.log10(np.percentile(Fuv[:],50)) + MB.m0set
-            hdr['MUV84'] = -2.5 * np.log10(np.percentile(Fuv[:],84)) + MB.m0set
-
-            # Flam to Fnu
-            hdr['LUV16'] = 10**(-0.4*hdr['MUV16'])
-            hdr['LUV50'] = 10**(-0.4*hdr['MUV50'])
-            hdr['LUV84'] = 10**(-0.4*hdr['MUV84'])
-
-            # Fuv (!= flux of Muv)
-            hdr['FUV16'] = np.percentile(Fuv28[:],16)
-            hdr['FUV50'] = np.percentile(Fuv28[:],50)
-            hdr['FUV84'] = np.percentile(Fuv28[:],84)
-
-            # # LIR
-            # hdr['LIR16'] = np.percentile(Lir[:],16)
-            # hdr['LIR50'] = np.percentile(Lir[:],50)
-            # hdr['LIR84'] = np.percentile(Lir[:],84)
-        except:
-            pass
-
-        # UV beta;
-        from .function import get_uvbeta
-        beta_16 = get_uvbeta(x1_tot, ytmp16, zbes)
-        beta_50 = get_uvbeta(x1_tot, ytmp50, zbes)
-        beta_84 = get_uvbeta(x1_tot, ytmp84, zbes)
-        hdr['UVBETA16'] = beta_16
-        hdr['UVBETA50'] = beta_50
-        hdr['UVBETA84'] = beta_84
-
-        # UVJ
-        try:
-            hdr['uv16'] = np.percentile(UVJ[:,0],16)
-            hdr['uv50'] = np.percentile(UVJ[:,0],50)
-            hdr['uv84'] = np.percentile(UVJ[:,0],84)
-            hdr['bv16'] = np.percentile(UVJ[:,1],16)
-            hdr['bv50'] = np.percentile(UVJ[:,1],50)
-            hdr['bv84'] = np.percentile(UVJ[:,1],84)
-            hdr['vj16'] = np.percentile(UVJ[:,2],16)
-            hdr['vj50'] = np.percentile(UVJ[:,2],50)
-            hdr['vj84'] = np.percentile(UVJ[:,2],84)
-            hdr['zj16'] = np.percentile(UVJ[:,3],16)
-            hdr['zj50'] = np.percentile(UVJ[:,3],50)
-            hdr['zj84'] = np.percentile(UVJ[:,3],84)
-        except:
-            print('\nError when writinf UVJ colors;\n')
-            pass
-
-        # EW;
-        try:
-            for ii in range(len(EW50)):
-                hdr['EW_%s_16'%(ew_label[ii])] = EW16[ii]
-                hdr['EW_%s_50'%(ew_label[ii])] = EW50[ii]
-                hdr['EW_%s_84'%(ew_label[ii])] = EW84[ii]
-                hdr['EW_%s_e1'%(ew_label[ii])] = EW50_er1[ii]
-                hdr['EW_%s_e2'%(ew_label[ii])] = EW50_er2[ii]
-                hdr['HIERARCH cnt_%s_16'%(ew_label[ii])]= cnt16[ii]
-                hdr['HIERARCH cnt_%s_50'%(ew_label[ii])]= cnt50[ii]
-                hdr['HIERARCH cnt_%s_84'%(ew_label[ii])]= cnt84[ii]
-                hdr['L_%s_16'%(ew_label[ii])] = L16[ii]
-                hdr['L_%s_50'%(ew_label[ii])] = L50[ii]
-                hdr['L_%s_84'%(ew_label[ii])] = L84[ii]
-        except:
-            pass
-
-        # Version;
-        import gsf
-        from astropy import units as u
-        hdr['version'] = gsf.__version__
-
-        # Write;
-        colspec = fits.ColDefs(col00)
-        hdu0 = fits.BinTableHDU.from_columns(colspec, header=hdr)
-        hdu0.writeto(MB.DIR_OUT + 'gsf_spec_%s.fits'%(ID), overwrite=True)
-
-        # ASDF;
-        tree_spec = {
-            'id': ID,
-            'redshift': '%.3f'%zbes,
-            'isochrone': '%s'%(isochrone),
-            'library': '%s'%(LIBRARY),
-            'nimf': '%s'%(nimf),
-            'scale': scale,
-            'm0set': MB.m0set,
-            'version_gsf': gsf.__version__
-        }
-        tree_spec['model'] = {}
-        tree_spec['obs'] = {}
-        Cnu_to_Jy = 10**((m0set-23.9)) # to microJy.
-
-        # BB;
-        tree_spec['model'].update({'wave_bb': lbb * u.AA})
-        tree_spec['model'].update({'fnu_bb_16': fbb16_nu * Cnu_to_Jy * u.uJy})
-        tree_spec['model'].update({'fnu_bb_50': fbb_nu * Cnu_to_Jy * u.uJy})
-        tree_spec['model'].update({'fnu_bb_84': fbb84_nu * Cnu_to_Jy * u.uJy})
-        # full spectrum;
-        tree_spec['model'].update({'wave': x1_tot * u.AA})
-        tree_spec['model'].update({'fnu_16': ytmp16 / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
-        tree_spec['model'].update({'fnu_50': ytmp50 / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
-        tree_spec['model'].update({'fnu_84': ytmp84 / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
-
-        # EW;
-        try:
-            for ii in range(len(EW50)):
-                tree_spec['model'].update({'EW_%s_16'%(ew_label[ii]): EW16[ii] * u.AA})
-                tree_spec['model'].update({'EW_%s_50'%(ew_label[ii]): EW50[ii] * u.AA})
-                tree_spec['model'].update({'EW_%s_84'%(ew_label[ii]): EW84[ii] * u.AA})
-                tree_spec['model'].update({'EW_%s_e1'%(ew_label[ii]): EW50_er1[ii] * u.AA})
-                tree_spec['model'].update({'EW_%s_e2'%(ew_label[ii]): EW50_er2[ii] * u.AA})
-                tree_spec['model'].update({'cnt_%s_16'%(ew_label[ii]): cnt16[ii] / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
-                tree_spec['model'].update({'cnt_%s_50'%(ew_label[ii]): cnt50[ii] / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
-                tree_spec['model'].update({'cnt_%s_84'%(ew_label[ii]): cnt84[ii] / (c / np.square(x1_tot[:]) / d) * Cnu_to_Jy * u.uJy})
-                tree_spec['model'].update({'L_%s_16'%(ew_label[ii]): L16[ii] * u.erg / u.s})
-                tree_spec['model'].update({'L_%s_50'%(ew_label[ii]): L50[ii] * u.erg / u.s})
-                tree_spec['model'].update({'L_%s_84'%(ew_label[ii]): L84[ii] * u.erg / u.s})
-        except:
-            pass
-
-        # Each component
-        # Stellar
-        tree_spec['model'].update({'wave_stel': x0 * u.AA})
-        for aa in range(len(age)):
-            tree_spec['model'].update({'fnu_stel_%d'%aa: f_50_comp[aa,:] / (c / np.square(x0[:]) / d) * Cnu_to_Jy * u.uJy})
-        if MB.f_dust:
-            # dust
-            tree_spec['model'].update({'wave_dust': x1_dust * u.AA})
-            tree_spec['model'].update({'fnu_dust': ytmp_dust50 / (c / np.square(x1_dust[:]) / d) * Cnu_to_Jy * u.uJy})
-
-        # Obs BB
-        tree_spec['obs'].update({'wave_bb': xbb * u.AA})
-        tree_spec['obs'].update({'fnu_bb': fybb[:] * Cnu_to_Jy * u.uJy})
-        tree_spec['obs'].update({'enu_bb': eybb[:] * Cnu_to_Jy * u.uJy})
-        # grism:
-        if f_grsm:
-            tree_spec['obs'].update({'fg0': fg0 * Cnu_to_Jy * u.uJy})
-            tree_spec['obs'].update({'eg0': eg0 * Cnu_to_Jy * u.uJy})
-            tree_spec['obs'].update({'wg0': xg0 * u.AA})
-            tree_spec['obs'].update({'fg1': fg1 * Cnu_to_Jy * u.uJy})
-            tree_spec['obs'].update({'eg1': eg1 * Cnu_to_Jy * u.uJy})
-            tree_spec['obs'].update({'wg1': xg1 * u.AA})
-            tree_spec['obs'].update({'fg2': fg2 * Cnu_to_Jy * u.uJy})
-            tree_spec['obs'].update({'eg2': eg2 * Cnu_to_Jy * u.uJy})
-            tree_spec['obs'].update({'wg2': xg2 * u.AA})
-
-        af = asdf.AsdfFile(tree_spec)
-        af.write_to(os.path.join(MB.DIR_OUT, 'gsf_spec_%s.asdf'%(ID)), all_array_compression='zlib')
-
-    #
-    # SED params in plot
-    #
-    if f_label:
-        fs_label = 8
-        fd = fits.open(MB.DIR_OUT + 'SFH_' + ID + '.fits')[0].header
-        if MB.f_dust:
-            label = 'ID: %s\n$z:%.2f$\n$\log M_\mathrm{*}/M_\odot:%.2f$\n$\log M_\mathrm{dust}/M_\odot:%.2f$\n$T_\mathrm{dust}/K:%.1f$\n$\log Z_\mathrm{*}/Z_\odot:%.2f$\n$\log T_\mathrm{*}$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$'\
-            %(ID, zbes, float(fd['Mstel_50']), MD50, TD50, float(fd['Z_MW_50']), float(fd['T_MW_50']), float(fd['AV_50']))#, fin_chi2)
-            ylabel = ymax*0.45
-        else:
-            label = 'ID: %s\n$z:%.2f$\n$\log M_\mathrm{*}/M_\odot:%.2f$\n$\log Z_\mathrm{*}/Z_\odot:%.2f$\n$\log T_\mathrm{*}$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$'\
-            %(ID, zbes, float(fd['Mstel_50']), float(fd['Z_MW_50']), float(fd['T_MW_50']), float(fd['AV_50']))
-            ylabel = ymax*0.25
-
-        if f_grsm:
-            ax1.text(0.02, 0.68, label,\
-            fontsize=fs_label, bbox=dict(facecolor='w', alpha=0.8, lw=1.), zorder=10,
-            ha='left', va='center', transform=ax1.transAxes)
-        else:
-            ax1.text(0.02, 0.68, label,\
-            fontsize=fs_label, bbox=dict(facecolor='w', alpha=0.8, lw=1.), zorder=10,
-            ha='left', va='center', transform=ax1.transAxes)
-
-    ax1.xaxis.labelpad = -3
-
-    if f_grsm:
-        if np.max(xg0)<23000: # E.g. WFC3, NIRISS grisms
-            conlim = (x0>10000) & (x0<25000)
-            xgmin, xgmax = np.min(x0[conlim]),np.max(x0[conlim]), #7500, 17000
-            ax2t.set_xlabel('')
-            ax2t.set_xlim(xgmin, xgmax)
-
-            conaa = (x0>xgmin-50) & (x0<xgmax+50)
-            ymaxzoom = np.max(ysum[conaa]*c/np.square(x0[conaa])/d) * 1.15
-            yminzoom = np.min(ysum[conaa]*c/np.square(x0[conaa])/d) / 1.15
-
-            ax2t.set_ylim(yminzoom, ymaxzoom)
-            ax2t.xaxis.labelpad = -2
-            if xgmax>20000:
-                ax2t.set_xticks([8000, 12000, 16000, 20000, 24000])
-                ax2t.set_xticklabels(['0.8', '1.2', '1.6', '2.0', '2.4'])
-            else:
-                ax2t.set_xticks([8000, 10000, 12000, 14000, 16000])
-                ax2t.set_xticklabels(['0.8', '1.0', '1.2', '1.4', '1.6'])
-        else:
-            conlim = (x0>10000) & (x0<54000) # NIRSPEC spectrum;
-            xgmin, xgmax = np.min(x0[conlim]),np.max(x0[conlim]), #7500, 17000
-            ax2t.set_xlabel('')
-            ax2t.set_xlim(xgmin, xgmax)
-
-            conaa = (x0>xgmin-50) & (x0<xgmax+50)
-            ymaxzoom = np.max(ysum[conaa]*c/np.square(x0[conaa])/d) * 1.15
-            yminzoom = np.min(ysum[conaa]*c/np.square(x0[conaa])/d) / 1.15
-
-            ax2t.set_ylim(yminzoom, ymaxzoom)
-            ax2t.xaxis.labelpad = -2
-            if xgmax>40000:
-                ax2t.set_xticks([8000, 20000, 32000, 44000, 56000])
-                ax2t.set_xticklabels(['0.8', '2.0', '3.2', '4.4', '5.6'])
-            else:
-                ax2t.set_xticks([8000, 20000, 32000, 44000])
-                ax2t.set_xticklabels(['0.8', '2.0', '3.2', '4.4'])
-
-    if MB.f_dust:
-        try:
-            contmp = (x1_tot>10*1e4) #& (fybbd/eybbd>SNlim)
-            y3min, y3max = -.2*np.max((model_tot * c/ np.square(x1_tot) / d)[contmp]), np.max((model_tot * c/ np.square(x1_tot) / d)[contmp])*2.0
-            ax3t.set_ylim(y3min, y3max)
-        except:
-            if verbose:
-                print('y3 limit is not specified.')
-            pass
-        ax3t.set_xlim(1e5, 3e7)
-        ax3t.set_xscale('log')
-        ax3t.set_xticks([100000, 1000000, 10000000])
-        ax3t.set_xticklabels(['10', '100', '1000'])
-
-    ###############
-    # Line name
-    ###############
-    LN0 = ['Mg2', '$NeIV$', '[OII]', 'H$\theta$', 'H$\eta$', 'Ne3?', 'H$\delta$', 'H$\gamma$', 'H$\\beta$', 'O3', 'O3', 'Mgb', 'Halpha', 'S2L', 'S2H']
-    LW0 = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4959, 5007, 5175, 6563, 6717, 6731]
-    fsl = 9 # Fontsize for line
-    if f_grsm:
-        try:
-            for ii in range(len(LW)):
-                ll = np.argmin(np.abs(LW[ii]-LW0[:]))
-
-                if ll == 2 and FLW[ii] == 1: # FLW is the flag for line fitting.
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
-                    ax2t.text(xxl[0]-130, yyl[0]*1.28, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
-
-                elif (ll == 9 and FLW[ii] == 1):
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
-
-                elif (ll == 10 and FLW[ii] == 1):
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
-                    ax2t.text(xxl[0]+40, yyl[0]*0.75, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
-
-                elif FLW[ii] == 1 and (ll == 6 or ll == 7 or ll == 8):
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.2,yminzoom+(ymaxzoom-yminzoom)*0.35, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
-                    ax2t.text(xxl[0]+40, yyl[0]*0.95, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
-
-                elif ll == 6 or ll == 7 or ll == 8:
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.2,yminzoom+(ymaxzoom-yminzoom)*0.35, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color='gray', zorder=1, alpha=1., label='', capsize=0)
-                    ax2t.text(xxl[0]+40, yyl[0]*0.95, '%s'%(LN0[ll]),  color='gray', fontsize=9, rotation=90)
-
-                elif FLW[ii] == 1:
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.7,yminzoom+(ymaxzoom-yminzoom)*.95, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
-                    ax2t.text(xxl[0]+40, yyl[0]*1.25, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
-        except:
-            pass
-
-    # Filters
-    ind_remove = np.where((wht3<=0) | (ey<=0))[0]
-    if f_plot_filter:
-        ax1 = plot_filter(MB, ax1, ymax, scl=scl_yaxis, ind_remove=ind_remove)
-
-    ####################
-    ## Save
-    ####################
-    ax1.legend(loc=1, fontsize=11)
-    if figpdf:
-        fig.savefig(MB.DIR_OUT + 'SPEC_' + ID + '_spec.pdf', dpi=dpi)
-    else:
-        fig.savefig(MB.DIR_OUT + 'SPEC_' + ID + '_spec.png', dpi=dpi)
-
-
-def plot_sed_tau(MB, flim=0.01, fil_path='./', scale=1e-19, f_chind=True, figpdf=False, save_sed=True, 
-    mmax=300, dust_model=0, DIR_TMP='./templates/', f_label=False, f_bbbox=False, verbose=False, f_silence=True, 
-    f_fill=False, f_fancyplot=False, f_Alog=True, dpi=300, f_plot_filter=True, f_plot_resid=False, NRbb_lim=10000):
-    '''
-    Parameters
-    ----------
-    MB.SNlim : float
-        SN limit to show flux or up lim in SED.
-    f_chind : bool
-        If include non-detection in chi2 calculation, using Sawicki12.
-    mmax : int
-        Number of mcmc realization for plot. Not for calculation.
-    f_fancy : bool
-        plot each SED component.
-    f_fill : bool
-        if True, and so is f_fancy, fill each SED component.
-
-    Returns
-    -------
-    plots
-    '''
-
-    from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
-    from mpl_toolkits.axes_grid1.inset_locator import mark_inset
-    from mpl_toolkits.axes_grid1.inset_locator import inset_axes
-    from scipy.optimize import curve_fit
-    from scipy import asarray as ar,exp
-    import matplotlib
-    import scipy.integrate as integrate
-    import scipy.special as special
-    import os.path
-    from astropy.io import ascii
-    import time
-
-    if f_silence:
-        import matplotlib
-        matplotlib.use("Agg")
-
-    def gaus(x,a,x0,sigma):
-        return a*exp(-(x-x0)**2/(2*sigma**2))
-    
-    print('\n### Running plot_sed_tau ###\n')
-
-    lcb = '#4682b4' # line color, blue
-
-    fnc  = MB.fnc
-    bfnc = MB.bfnc
-    ID   = MB.ID
-    Z    = MB.Zall
-    age  = MB.age
-    nage = MB.nage 
-    tau0 = MB.tau0
-    
-    NUM_COLORS = len(age)
-    cm = plt.get_cmap('gist_rainbow')
-    col = [cm(1 - 1.*i/NUM_COLORS) for i in range(NUM_COLORS)]
-
-    nstep_plot = 1
-    if MB.f_bpass:
-        nstep_plot = 30
-
-    SNlim = MB.SNlim
-
-    ################
-    # RF colors.
-    home = os.path.expanduser('~')
-    c = MB.c
-    chimax = 1.
-    m0set = MB.m0set
-    Mpc_cm = MB.Mpc_cm
-    
-    ##################
-    # Fitting Results
-    ##################
-    DIR_FILT = MB.DIR_FILT
-    SFILT = MB.filts
-
-    try:
-        f_err = MB.ferr
-    except:
-        f_err = 0
-
-    ###########################
-    # Open result file
-    ###########################
-    file = MB.DIR_OUT + 'summary_' + ID + '.fits'
-    hdul = fits.open(file) 
-    
-    ndim_eff = hdul[0].header['NDIM']
-    vals = {}
-
-    # Redshift MC
-    zp16  = hdul[1].data['zmc'][0]
-    zp50  = hdul[1].data['zmc'][1]
-    zp84  = hdul[1].data['zmc'][2]
-    vals['zmc'] = zp50
-
-    # Stellar mass MC
-    M16 = hdul[1].data['ms'][0]
-    M50 = hdul[1].data['ms'][1]
-    M84 = hdul[1].data['ms'][2]
-    if verbose:
-        print('Total stellar mass is %.2e'%(M50))
-
-    # Amplitude MC
-    A50 = np.zeros(len(age), dtype='float')
-    A16 = np.zeros(len(age), dtype='float')
-    A84 = np.zeros(len(age), dtype='float')
-    for aa in range(len(age)):
-        A16[aa] = 10**hdul[1].data['A'+str(aa)][0]
-        A50[aa] = 10**hdul[1].data['A'+str(aa)][1]
-        A84[aa] = 10**hdul[1].data['A'+str(aa)][2]
-        vals['A'+str(aa)] = np.log10(A50[aa])
-
-    Asum  = np.sum(A50)
-
-    # TAU MC
-    # AGE MC
-    TAU50 = np.zeros(len(age), dtype='float')
-    TAU16 = np.zeros(len(age), dtype='float')
-    TAU84 = np.zeros(len(age), dtype='float')
-    AGE50 = np.zeros(len(age), dtype='float')
-    AGE16 = np.zeros(len(age), dtype='float')
-    AGE84 = np.zeros(len(age), dtype='float')
-    for aa in range(len(age)):
-        TAU16[aa] = 10**hdul[1].data['TAU'+str(aa)][0]
-        TAU50[aa] = 10**hdul[1].data['TAU'+str(aa)][1]
-        TAU84[aa] = 10**hdul[1].data['TAU'+str(aa)][2]
-        AGE16[aa] = 10**hdul[1].data['AGE'+str(aa)][0]
-        AGE50[aa] = 10**hdul[1].data['AGE'+str(aa)][1]
-        AGE84[aa] = 10**hdul[1].data['AGE'+str(aa)][2]
-        vals['TAU'+str(aa)] = np.log10(TAU50[aa])
-        vals['AGE'+str(aa)] = np.log10(AGE50[aa])
-
-    aa = 0
-    Av16 = hdul[1].data['Av'+str(aa)][0]
-    Av50 = hdul[1].data['Av'+str(aa)][1]
-    Av84 = hdul[1].data['Av'+str(aa)][2]
-    AAv = [Av50]
-    vals['Av'] = Av50
-
-    Z50 = np.zeros(len(age), dtype='float')
-    Z16 = np.zeros(len(age), dtype='float')
-    Z84 = np.zeros(len(age), dtype='float')
-    for aa in range(len(age)):
-        Z16[aa] = hdul[1].data['Z'+str(aa)][0]
-        Z50[aa] = hdul[1].data['Z'+str(aa)][1]
-        Z84[aa] = hdul[1].data['Z'+str(aa)][2]
-        vals['Z'+str(aa)] = Z50[aa]
-
-    # Light weighted Z.
-    ZZ50 = np.sum(Z50*A50)/np.sum(A50)
-
-    # FIR Dust;
-    try:
-        MD16 = hdul[1].data['MDUST'][0]
-        MD50 = hdul[1].data['MDUST'][1]
-        MD84 = hdul[1].data['MDUST'][2]
-        AD16 = hdul[1].data['ADUST'][0]
-        AD50 = hdul[1].data['ADUST'][1]
-        AD84 = hdul[1].data['ADUST'][2]
-        TD16 = hdul[1].data['TDUST'][0]
-        TD50 = hdul[1].data['TDUST'][1]
-        TD84 = hdul[1].data['TDUST'][2]
-        nTD16 = hdul[1].data['nTDUST'][0]
-        nTD50 = hdul[1].data['nTDUST'][1]
-        nTD84 = hdul[1].data['nTDUST'][2]
-        DFILT = MB.inputs['FIR_FILTER'] # filter band string.
-        DFILT = [x.strip() for x in DFILT.split(',')]
-        DFWFILT = fil_fwhm(DFILT, DIR_FILT)
-        if verbose:
-            print('Total dust mass is %.2e'%(MD50))
-        f_dust = True
-    except:
-        f_dust = False
-
-    chi = hdul[1].data['chi'][0]
-    chin = hdul[1].data['chi'][1]
-    fitc = chin
-
-    Cz0 = hdul[0].header['Cz0']
-    Cz1 = hdul[0].header['Cz1']
-    Cz2 = hdul[0].header['Cz2']
-    zbes = zp50 
-    zscl = (1.+zbes)
-
-    ###############################
-    # Data taken from
-    ###############################
-    if MB.f_dust:
-        MB.dict = MB.read_data(Cz0, Cz1, Cz2, zbes, add_fir=True)
-    else:
-        MB.dict = MB.read_data(Cz0, Cz1, Cz2, zbes)
-
-    NR   = MB.dict['NR']
-    x    = MB.dict['x']
-    fy   = MB.dict['fy']
-    ey   = MB.dict['ey']
-    
-    con0 = (NR<1000)
-    xg0  = x[con0]
-    fg0  = fy[con0]
-    eg0  = ey[con0]
-    con1 = (NR>=1000) & (NR<2000) #& (fy/ey>SNlim)
-    xg1  = x[con1]
-    fg1  = fy[con1]
-    eg1  = ey[con1]
-    con2 = (NR>=2000) & (NR<NRbb_lim) #& (fy/ey>SNlim)
-    xg2  = x[con2]
-    fg2  = fy[con2]
-    eg2  = ey[con2]
-    if len(xg0)>0 or len(xg1)>0 or len(xg2)>0:
-        f_grsm = True
-    else:
-        f_grsm = False
-
-    # Weight is set to zero for those no data (ey<0).
-    wht = fy * 0
-    con_wht = (ey>0)
-    wht[con_wht] = 1./np.square(ey[con_wht])
-
-    # BB data points;
-    NRbb = MB.dict['NRbb']
-    xbb  = MB.dict['xbb']
-    fybb = MB.dict['fybb']
-    eybb = MB.dict['eybb']
-    exbb = MB.dict['exbb']
-    snbb = fybb/eybb
-
-    ######################
-    # Weight by line
-    ######################
-    wh0  = 1./np.square(eg0)
-    LW0  = []
-    model = fg0
-    wht3 = check_line_man(fy, x, wht, fy, zbes, LW0)
-
-    ######################
-    # Mass-to-Light ratio.
-    ######################
-    af = MB.af
-    sedpar = af['ML']
-    try:
-        isochrone = af['isochrone']
-        LIBRARY = af['library']
-        nimf = af['nimf']
-    except:
-        isochrone = ''
-        LIBRARY = ''
-        nimf = ''
-
-    #############
-    # Plot.
-    #############
-    # Set the inset.
-    if f_grsm or f_dust:
-        fig = plt.figure(figsize=(7.,3.2))
-        fig.subplots_adjust(top=0.98, bottom=0.16, left=0.1, right=0.99, hspace=0.15, wspace=0.25)
-        ax1 = fig.add_subplot(111)
-        xsize = 0.29
-        ysize = 0.25
-        if f_grsm:
-            ax2t = ax1.inset_axes((1-xsize-0.01,1-ysize-0.01,xsize,ysize))
-        if f_dust:
-            ax3t = ax1.inset_axes((0.7,.35,.28,.25))
-        f_plot_resid = False
-    else:
-        if f_plot_resid:
-            fig_mosaic = """
-            AAAA
-            AAAA
-            BBBB
-            """
-            fig,axes = plt.subplot_mosaic(mosaic=fig_mosaic, figsize=(5.5,4.2))
-            fig.subplots_adjust(top=0.98, bottom=0.16, left=0.1, right=0.99, hspace=0.15, wspace=0.25)
-            ax1 = axes['A']
-        else:
-            fig = plt.figure(figsize=(5.5,2.2))
-            fig.subplots_adjust(top=0.98, bottom=0.16, left=0.1, right=0.99, hspace=0.15, wspace=0.25)
-            ax1 = fig.add_subplot(111)
-
-    # Determine scale here;
-    if scale == None:
-        conbb_hs = (fybb/eybb > SNlim)
-        scale = 10**(int(np.log10(np.nanmax(fybb[conbb_hs] * c / np.square(xbb[conbb_hs])) / MB.d)))
-    d = MB.d * scale
-
-    #######################################
-    # D.Kelson like Box for BB photometry
-    #######################################
-    #col_dat = 'darkgreen'
-    #col_dat = 'tomato'
-    col_dat = 'r'
-    if f_bbbox:
-        for ii in range(len(xbb)):
-            if eybb[ii]<100 and fybb[ii]/eybb[ii]>1:
-                xx = [xbb[ii]-exbb[ii],xbb[ii]-exbb[ii]]
-                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
-                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
-                xx = [xbb[ii]+exbb[ii],xbb[ii]+exbb[ii]]
-                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
-                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
-                xx = [xbb[ii]-exbb[ii],xbb[ii]+exbb[ii]]
-                yy = [(fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]-eybb[ii])*c/np.square(xbb[ii])/d]
-                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
-                xx = [xbb[ii]-exbb[ii],xbb[ii]+exbb[ii]]
-                yy = [(fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d, (fybb[ii]+eybb[ii])*c/np.square(xbb[ii])/d]
-                ax1.plot(xx, yy, color='k', linestyle='-', linewidth=0.5, zorder=3)
-    else: # Normal BB plot;
-        # Detection;
-        conbb_hs = (fybb/eybb>SNlim)
-        ax1.errorbar(xbb[conbb_hs], fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d, \
-        yerr=eybb[conbb_hs]*c/np.square(xbb[conbb_hs])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
-        ax1.plot(xbb[conbb_hs], fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d, \
-        marker='.', color=col_dat, linestyle='', linewidth=0, zorder=4, ms=8)#, label='Obs.(BB)')
-
-        try:
-            # For any data removed fron fit (i.e. IRAC excess):
-            data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
-            NR_ex = data_ex['col1']
-        except:
-            NR_ex = []
-
-        # Upperlim;
-        sigma = 1.0
-        leng = np.max(fybb[conbb_hs] * c / np.square(xbb[conbb_hs]) / d) * 0.05 #0.2
-        conebb_ls = (fybb/eybb<=SNlim) & (eybb>0)
-        
-        for ii in range(len(xbb)):
-            if NR[ii] in NR_ex[:]:
-                conebb_ls[ii] = False
-        
-        ax1.errorbar(xbb[conebb_ls], eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, yerr=leng,\
-            uplims=eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, linestyle='',color=col_dat, marker='', ms=4, label='', zorder=4, capsize=3)
-
-
-    # For any data removed fron fit (i.e. IRAC excess):
-    f_exclude = False
-    try:
-        col_ex = 'lawngreen'
-        #col_ex = 'limegreen'
-        #col_ex = 'r'
-        # Currently, this file is made after FILTER_SKIP;
-        data_ex = ascii.read(DIR_TMP + 'bb_obs_' + ID + '_removed.cat')
-        x_ex = data_ex['col2']
-        fy_ex = data_ex['col3']
-        ey_ex = data_ex['col4']
-        ex_ex = data_ex['col5']
-
-        ax1.errorbar(x_ex, fy_ex * c / np.square(x_ex) / d, \
-        xerr=ex_ex, yerr=ey_ex*c/np.square(x_ex)/d, color='k', linestyle='', linewidth=0.5, zorder=5)
-        ax1.scatter(x_ex, fy_ex * c / np.square(x_ex) / d, marker='s', color=col_ex, edgecolor='k', zorder=5, s=30)
-        f_exclude = True
-    except:
-        pass
-
-
-    #####################################
-    # Open ascii file and stock to array.
-    MB.lib = fnc.open_spec_fits(fall=0)
-    MB.lib_all = fnc.open_spec_fits(fall=1, orig=True)
-
-    if f_dust:
-        DT0 = float(MB.inputs['TDUST_LOW'])
-        DT1 = float(MB.inputs['TDUST_HIG'])
-        dDT = float(MB.inputs['TDUST_DEL'])
-        Temp = np.arange(DT0,DT1,dDT)
-        MB.lib_dust = fnc.open_spec_dust_fits(fall=0)
-        MB.lib_dust_all = fnc.open_spec_dust_fits(fall=1)
-
-    # FIR dust plot;
-    if f_dust:
-        from lmfit import Parameters
-        par = Parameters()
-        par.add('MDUST',value=AD50)
-        par.add('TDUST',value=nTD50)
-        par.add('zmc',value=zp50)
-
-        y0d, x0d = fnc.tmp04_dust(par.valuesdict())#, zbes, lib_dust_all)
-        y0d_cut, x0d_cut = fnc.tmp04_dust(par.valuesdict())#, zbes, lib_dust)
-        
-        # data;
-        dat_d = ascii.read(MB.DIR_TMP + 'bb_dust_obs_' + MB.ID + '.cat')
-        NRbbd = dat_d['col1']
-        xbbd = dat_d['col2']
-        fybbd = dat_d['col3']
-        eybbd = dat_d['col4']
-        exbbd = dat_d['col5']
-        snbbd = fybbd/eybbd
-
-        try:
-            conbbd_hs = (fybbd/eybbd>SNlim)
-            ax1.errorbar(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
-            yerr=eybbd[conbbd_hs]*c/np.square(xbbd[conbbd_hs])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
-            ax1.plot(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
-            '.r', linestyle='', linewidth=0, zorder=4)#, label='Obs.(BB)')
-            ax3t.plot(xbbd[conbbd_hs], fybbd[conbbd_hs] * c / np.square(xbbd[conbbd_hs]) / d, \
-            '.r', linestyle='', linewidth=0, zorder=4)#, label='Obs.(BB)')
-        except:
-            pass
-
-        try:
-            conebbd_ls = (fybbd/eybbd<=SNlim)
-            ax1.errorbar(xbbd[conebbd_ls], eybbd[conebbd_ls] * c / np.square(xbbd[conebbd_ls]) / d, \
-            yerr=fybbd[conebbd_ls]*0+np.max(fybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d)*0.05, \
-            uplims=eybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d, color='r', linestyle='', linewidth=0.5, zorder=4)
-            ax3t.errorbar(xbbd[conebbd_ls], eybbd[conebbd_ls] * c / np.square(xbbd[conebbd_ls]) / d, \
-            yerr=fybbd[conebbd_ls]*0+np.max(fybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d)*0.05, \
-            uplims=eybbd[conebbd_ls]*c/np.square(xbbd[conebbd_ls])/d, color='r', linestyle='', linewidth=0.5, zorder=4)
-        except:
-            pass
-
-
-    #
-    # This is for UVJ color time evolution.
-    #
-    Asum = np.sum(A50[:])
-    alp = .5
-
-    # Get total templates
-    y0p, x0p = MB.fnc.tmp04(vals, f_val=False, check_bound=False)
-    y0, x0 = MB.fnc.tmp04(vals, f_val=False, check_bound=False, lib_all=True)
-
-    ysum = y0
-    f_50_comp = y0[:] * c / np.square(x0) / d
-
-    ysump = y0p
-    nopt = len(ysump)
-
-    if f_dust:
-        ysump[:] += y0d_cut[:nopt]
-        ysump = np.append(ysump,y0d_cut[nopt:])
-        f_50_comp_dust = y0d * c / np.square(x0d) / d
-
-    # Plot each best fit:
-    vals_each = vals.copy()
-    for aa in range(len(age)):
-        vals_each['A%d'%aa] = -99
-    for aa in range(len(age)):
-        vals_each['A%d'%aa] = vals['A%d'%aa]
-        y0tmp, x0tmp = MB.fnc.tmp04(vals_each, f_val=False, check_bound=False, lib_all=True)
-        if aa == 0:
-            y0keep = y0tmp
-        else:
-            y0keep += y0tmp
-        ax1.plot(x0tmp, y0tmp * c / np.square(x0tmp) / d, linestyle='--', lw=0.5, color=col[aa])
-        vals_each['A%d'%aa] = 0
-
-    # Plot best fit;
-    ax1.plot(x0, f_50_comp, linestyle='-', lw=0.5, color='k')
-
-    #############
-    # Main result
-    #############
-    conbb_ymax = (xbb>0) & (fybb>0) & (eybb>0) & (fybb/eybb>1) # (conbb) &
-    ymax = np.max(fybb[conbb_ymax]*c/np.square(xbb[conbb_ymax])/d) * 1.6
-
-    xboxl = 17000
-    xboxu = 28000
-
-    x1max = 22000
-    if x1max < np.max(xbb[conbb_ymax]):
-        x1max = np.max(xbb[conbb_ymax]) * 1.5
-    ax1.set_xlim(2000, 11000)
-    ax1.set_xscale('log')
-    if f_plot_filter:
-        scl_yaxis = 0.2
-    else:
-        scl_yaxis = 0.1
-    ax1.set_ylim(-ymax*scl_yaxis,ymax)
-    ax1.text(2100,-ymax*0.08,'SNlimit:%.1f'%(SNlim),fontsize=8)
-
-    ax1.set_xlabel('Observed wavelength ($\mathrm{\mu m}$)', fontsize=12)
-    ax1.set_ylabel('Flux ($10^{%d}\mathrm{erg}/\mathrm{s}/\mathrm{cm}^{2}/\mathrm{\AA}$)'%(np.log10(scale)),fontsize=12,labelpad=-2)
-
-    xticks = [2500, 5000, 10000, 20000, 40000, 80000, 110000]
-    xlabels= ['0.25', '0.5', '1', '2', '4', '8', '']
-    if f_dust:
-        xticks = [2500, 5000, 10000, 20000, 40000, 80000, 400000]
-        xlabels= ['0.25', '0.5', '1', '2', '4', '8', '']
-
-    ax1.set_xticks(xticks)
-    ax1.set_xticklabels(xlabels)
-
-
-    dely1 = 0.5
-    while (ymax-0)/dely1<1:
-        dely1 /= 2.
-    while (ymax-0)/dely1>4:
-        dely1 *= 2.
-
-    y1ticks = np.arange(0, ymax, dely1)
-    ax1.set_yticks(y1ticks)
-    ax1.set_yticklabels(np.arange(0, ymax, dely1), minor=False)
-    ax1.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
-    ax1.yaxis.labelpad = 1.5
-
-    xx = np.arange(1200,400000)
-    yy = xx * 0
-    ax1.plot(xx, yy, ls='--', lw=0.5, color='k')
-
-    #############
-    # Plot
-    #############
-    ms = np.zeros(len(age), dtype='float')
-    af = MB.af
-    sedpar = af['ML']
-
-    eAAl = np.zeros(len(age),dtype='float')
-    eAAu = np.zeros(len(age),dtype='float')
-    eAMl = np.zeros(len(age),dtype='float')
-    eAMu = np.zeros(len(age),dtype='float')
-    MSsum = np.sum(ms)
-    Asum = np.sum(A50)
-    A50 /= Asum
-    A16 /= Asum
-    A84 /= Asum
-
-    AM50 = A50 * M50 * ms / MSsum
-    CM = M50/np.sum(AM50)
-
-    AM50 = A50 * M50 * ms / MSsum * CM
-    AM16 = A16 * M50 * ms / MSsum * CM
-    AM84 = A84 * M50 * ms / MSsum * CM
-
-    AC50 = A50 * 0 # Cumulative
-    for ii in range(len(A50)):
-        eAAl[ii] = A50[ii] - A16[ii]
-        eAAu[ii] = A84[ii] - A50[ii]
-        eAMl[ii] = AM50[ii] - AM16[ii]
-        eAMu[ii] = AM84[ii] - AM50[ii]
-        AC50[ii] = np.sum(AM50[ii:])
-
-
-    ################
-    # Lines
-    ################
-    LN = ['Mg2', 'Ne5', 'O2', 'Htheta', 'Heta', 'Ne3', 'Hdelta', 'Hgamma', 'Hbeta', 'O3', 'O3', 'Mgb', 'Halpha', 'S2L', 'S2H']
-    FLW = np.zeros(len(LN),dtype='int')
-
-    ####################
-    # For cosmology
-    ####################
-    DL = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm
-    Cons = (4.*np.pi*DL**2/(1.+zbes))
-
-    if f_grsm:
-        print('This function (write_lines) needs to be revised.')
-        write_lines(ID, zbes, DIR_OUT=MB.DIR_OUT)
-
-
-    ##########################
-    # Zoom in Line regions
-    ##########################
-    if f_grsm:
-        conspec = (NR<10000) #& (fy/ey>1)
-        ax2t.errorbar(xg2, fg2 * c/np.square(xg2)/d, yerr=eg2 * c/np.square(xg2)/d, lw=0.5, color='#DF4E00', zorder=10, alpha=1., label='', capsize=0)
-        ax2t.errorbar(xg1, fg1 * c/np.square(xg1)/d, yerr=eg1 * c/np.square(xg1)/d, lw=0.5, color='g', zorder=10, alpha=1., label='', capsize=0)
-        ax2t.errorbar(xg0, fg0 * c/np.square(xg0)/d, yerr=eg0 * c/np.square(xg0)/d, lw=0.5, linestyle='', color='royalblue', zorder=10, alpha=1., label='', capsize=0)
-
-        xgrism = np.concatenate([xg0,xg1,xg2])
-        fgrism = np.concatenate([fg0,fg1,fg2])
-        egrism = np.concatenate([eg0,eg1,eg2])
-        con4000b = (xgrism/zscl>3400) & (xgrism/zscl<3800) & (fgrism>0) & (egrism>0)
-        con4000r = (xgrism/zscl>4200) & (xgrism/zscl<5000) & (fgrism>0) & (egrism>0)
-        print('Median SN at 3400-3800 is;', np.median((fgrism/egrism)[con4000b]))
-        print('Median SN at 4200-5000 is;', np.median((fgrism/egrism)[con4000r]))
-
-
-    #
-    # From MCMC chain
-    #
-    file = MB.DIR_OUT + 'chain_' + ID + '_corner.cpkl'
-    niter = 0
-    data = loadcpkl(file)
-    ndim = data['ndim'] 
-    burnin = data['burnin']
-    nmc = data['niter']
-    nwalk = data['nwalkers']
-    Nburn = burnin
-    res = data['chain'][:]
-
-    samples = res
-
-    # Saved template;
-    ytmp = np.zeros((mmax,len(ysum)), dtype='float')
-    ytmp_each = np.zeros((mmax,len(ysum),len(age)), dtype='float')
-
-    ytmpmax = np.zeros(len(ysum), dtype='float')
-    ytmpmin = np.zeros(len(ysum), dtype='float')
-
-    # MUV;
-    DL      = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm # Luminositydistance in cm
-    DL10    = Mpc_cm/1e6 * 10 # 10pc in cm
-    Fuv     = np.zeros(mmax, dtype='float') # For Muv
-    Fuv28   = np.zeros(mmax, dtype='float') # For Fuv(1500-2800)
-    Lir     = np.zeros(mmax, dtype='float') # For L(8-1000um)
-    UVJ     = np.zeros((mmax,4), dtype='float') # For UVJ color;
-
-    Cmznu   = 10**((48.6+m0set)/(-2.5)) # Conversion from m0_25 to fnu
-
-    # From random chain;
-    alp=0.02
-    for kk in range(0,mmax,1):
-        nr = np.random.randint(Nburn, len(samples['A%d'%MB.aamin[0]]))
-        try:
-            Av_tmp = samples['Av'][nr]
-        except:
-            Av_tmp = MB.AVFIX
-        vals['Av'] = Av_tmp
-
-        try:
-            zmc = samples['zmc'][nr]
-        except:
-            zmc = zbes
-        vals['zmc'] = zmc
-
-        for ss in MB.aamin:
-            try:
-                AA_tmp = 10**samples['A'+str(ss)][nr]
-            except:
-                AA_tmp = 0
-            vals['A%d'%ss] = np.log10(AA_tmp)
-
-            if ss == 0 or MB.ZEVOL:
-                try:
-                    ZZtmp = samples['Z%d'%ss][nr]
-                except:
-                    ZZtmp = MB.ZFIX
-                vals['Z%d'%ss] = ZZtmp
-
-        mod0_tmp, xm_tmp = fnc.tmp04(vals, f_val=False, check_bound=False, lib_all=True)
-        fm_tmp = mod0_tmp
-
-        if False:
-            # Each;
-            ytmp_each[kk,:,ss] = mod0_tmp[:] * c / np.square(xm_tmp[:]) / d
-            #if kk == 100:
-            #    ax1.plot(xm_tmp[:], ytmp_each[kk,:,ss], color=col[ss], linestyle='--')
-
-        #
-        # Dust component;
-        #
-        if f_dust:
-            if kk == 0:
-                par = Parameters()
-                par.add('MDUST',value=samples['MDUST'][nr])
-                try:
-                    par.add('TDUST',value=samples['TDUST'][nr])
-                except:
-                    par.add('TDUST',value=0)
-
-            par['MDUST'].value = samples['MDUST'][nr]
-            if not MB.TDUSTFIX == None:
-                par['TDUST'].value = MB.NTDUST
-            else:
-                par['TDUST'].value = samples['TDUST'][nr]
-
-            model_dust, x1_dust = fnc.tmp04_dust(par.valuesdict())#, zbes, lib_dust_all)
-            if kk == 0:
-                deldt  = (x1_dust[1] - x1_dust[0])
-                x1_tot = np.append(xm_tmp,np.arange(np.max(xm_tmp),np.max(x1_dust),deldt))
-                # Redefine??
-                ytmp = np.zeros((mmax,len(x1_tot)), dtype='float')
-                ytmp_dust = np.zeros((mmax,len(x1_dust)), dtype='float')
-
-            ytmp_dust[kk,:] = model_dust * c/np.square(x1_dust)/d
-            model_tot = np.interp(x1_tot,xm_tmp,fm_tmp) + np.interp(x1_tot,x1_dust,model_dust)
-
-            ytmp[kk,:] = model_tot[:] * c/np.square(x1_tot[:])/d
-
-        else:
-            x1_tot = xm_tmp
-            ytmp[kk,:] = fm_tmp[:] * c / np.square(xm_tmp[:]) / d
-
-        # plot random sed;
-        plot_mc = True
-        if plot_mc:
-            ax1.plot(x1_tot, ytmp[kk,:], '-', lw=1, color='gray', zorder=-2, alpha=0.02)
-
-        # Grism plot + Fuv flux + LIR.
-        #if f_grsm:
-        #    ax2t.plot(x1_tot, ytmp[kk,:], '-', lw=0.5, color='gray', zorder=3., alpha=0.02)
-
-        if True:
-            # Get FUV flux;
-            Fuv[kk] = get_Fuv(x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)) * (DL**2/(1.+zbes)) / (DL10**2), lmin=1250, lmax=1650)
-            Fuv28[kk] = get_Fuv(x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)) * (4*np.pi*DL**2/(1.+zbes))*Cmznu, lmin=1500, lmax=2800)
-            Lir[kk] = 0
-
-            # Get UVJ Color;
-            lmconv,fconv = filconv_fast(MB.filts_rf, MB.band_rf, x1_tot[:]/(1.+zbes), (ytmp[kk,:]/(c/np.square(x1_tot)/d)))
-            UVJ[kk,0] = -2.5*np.log10(fconv[0]/fconv[2])
-            UVJ[kk,1] = -2.5*np.log10(fconv[1]/fconv[2])
-            UVJ[kk,2] = -2.5*np.log10(fconv[2]/fconv[3])
-            UVJ[kk,3] = -2.5*np.log10(fconv[4]/fconv[3])
-
-        # Do stuff...
-        time.sleep(0.01)
-        # Update Progress Bar
-        printProgressBar(kk, mmax, prefix = 'Progress:', suffix = 'Complete', length = 40)
-
-    print('')
-
-    #
-    # Plot Median SED;
-    #
-    ytmp16 = np.percentile(ytmp[:,:],16,axis=0)
-    ytmp50 = np.percentile(ytmp[:,:],50,axis=0)
-    ytmp84 = np.percentile(ytmp[:,:],84,axis=0)
-    
-    if f_dust:
-        ytmp_dust50 = np.percentile(ytmp_dust[:,:],50, axis=0)
-
-    # For grism;
-    if f_grsm:
-        from astropy.convolution import convolve
-        from .maketmp_filt import get_LSF
-        LSF, _ = get_LSF(MB.inputs, MB.DIR_EXTR, ID, x1_tot[:], c=3e18)
-        spec_grsm16 = convolve(ytmp16[:], LSF, boundary='extend')
-        spec_grsm50 = convolve(ytmp50[:], LSF, boundary='extend')
-        spec_grsm84 = convolve(ytmp84[:], LSF, boundary='extend')
-        if False:#True:
-            ax2t.plot(x1_tot[:], ytmp50, '-', lw=0.5, color='gray', zorder=3., alpha=1.0)
-        else:
-            ax2t.plot(x1_tot[:], spec_grsm50, '-', lw=0.5, color='gray', zorder=3., alpha=1.0)
-
-
-    #if not f_fill:
-    ax1.fill_between(x1_tot[::nstep_plot], ytmp16[::nstep_plot], ytmp84[::nstep_plot], ls='-', lw=.5, color='gray', zorder=-2, alpha=0.5)
-    ax1.plot(x1_tot[::nstep_plot], ytmp50[::nstep_plot], '-', lw=.5, color='gray', zorder=-1, alpha=1.)
-
-    # Attach the data point in MB;
-    MB.sed_wave_obs = xbb
-    MB.sed_flux_obs = fybb * c / np.square(xbb) / d
-    MB.sed_eflux_obs = eybb * c / np.square(xbb) / d
-    # Attach the best SED to MB;
-    MB.sed_wave = x1_tot
-    MB.sed_flux16 = ytmp16
-    MB.sed_flux50 = ytmp50
-    MB.sed_flux84 = ytmp84
-
-
-    #########################
-    # Calculate non-det chi2
-    # based on Sawick12
-    #########################
-    #chi2,fin_chi2 = get_chi2(fy, ey, wht3, ysump, ndim_eff, SNlim=1.0, f_chind=f_chind, f_exclude=f_exclude, xbb=xbb, x_ex=x_ex)
-    def func_tmp(xint,eobs,fmodel):
-        int_tmp = np.exp(-0.5 * ((xint-fmodel)/eobs)**2)
-        return int_tmp
-
-    if f_chind:
-        conw = (wht3>0) & (ey>0) & (fy/ey>SNlim)
-    else:
-        conw = (wht3>0) & (ey>0)
-
-    chi2 = sum((np.square(fy-ysump) * np.sqrt(wht3))[conw])
-
-    chi_nd = 0.0
-    if f_chind:
-        f_ex = np.zeros(len(fy), 'int')
-        if f_exclude:
-            for ii in range(len(fy)):
-                if x[ii] in x_ex:
-                    f_ex[ii] = 1
-
-        con_up = (ey>0) & (fy/ey<=SNlim) & (f_ex == 0)
-        from scipy import special
-        x_erf = (ey[con_up] - ysump[con_up]) / (np.sqrt(2) * ey[con_up])
-        f_erf = special.erf(x_erf)
-        chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey[con_up] * (1 + f_erf)) )
-
-    # Number of degree;
-    con_nod = (wht3>0) & (ey>0) #& (fy/ey>SNlim)
-    if MB.ferr:
-        ndim_eff -= 1
-        
-    nod = int(len(wht3[con_nod])-ndim_eff)
-
-    if nod>0:
-        fin_chi2 = (chi2 - 2 * chi_nd) / nod
-    else:
-        fin_chi2 = -99
-
-    if f_chind:
-        conw = (wht3>0) & (ey>0) & (fy/ey>SNlim)
-        con_up = (ey>0) & (fy/ey<=SNlim) & (f_ex == 0)
-    else:
-        conw = (wht3>0) & (ey>0)
-
-    # Print results;
-    print('\n')
-    print('No-of-detection    : %d'%(len(wht3[conw])))
-    print('chi2               : %.2f'%(chi2))
-    if f_chind:
-        print('No-of-non-detection: %d'%(len(ey[con_up])))
-        print('chi2 for non-det   : %.2f'%(- 2 * chi_nd))
-    print('No-of-params       : %d'%(ndim_eff))
-    print('Degrees-of-freedom : %d'%(nod))
-    print('Final chi2/nu      : %.2f'%(fin_chi2))
-
-
-    if False:
-        from lmfit import Model, Parameters, minimize, fit_report, Minimizer
-        from .posterior_flexible import Post
-        class_post = Post(MB)
-        residual = class_post.residual
-        MB.set_param()
-        fit_params = MB.fit_params #Parameters()
-        for key in vals.keys():
-            try:
-                fit_params[key].value=vals[key]
-            except:
-                pass
-        out_tmp = minimize(residual, fit_params, args=(fy, ey, wht3, False), method='differential_evolution') # nelder is the most efficient.
-        csq = out_tmp.chisqr
-        rcsq = out_tmp.redchi
-        print(csq, rcsq)
-
-    #
-    # plot BB model from best template (blue squares)
-    #
-    col_dia = 'blue'
-    if f_dust:
-        ALLFILT = np.append(SFILT,DFILT)
-        #for ii in range(len(x1_tot)):
-        #    print(x1_tot[ii], model_tot[ii]*c/np.square(x1_tot[ii])/d)
-        lbb, fbb, lfwhm = filconv(ALLFILT, x1_tot, ytmp50, DIR_FILT, fw=True)
-        lbb, fbb16, lfwhm = filconv(ALLFILT, x1_tot, ytmp16, DIR_FILT, fw=True)
-        lbb, fbb84, lfwhm = filconv(ALLFILT, x1_tot, ytmp84, DIR_FILT, fw=True)
-
-        ax1.plot(x1_tot, ytmp50, '--', lw=0.5, color='purple', zorder=-1, label='')
-        ax3t.plot(x1_tot, ytmp50, '--', lw=0.5, color='purple', zorder=-1, label='')
-
-        iix = []
-        for ii in range(len(fbb)):
-            iix.append(ii)
-        con_sed = ()
-        ax1.scatter(lbb[iix][con_sed], fbb[iix][con_sed], lw=0.5, color='none', edgecolor=col_dia, zorder=3, alpha=1.0, marker='d', s=50)
-
-        # plot FIR range;
-        ax3t.scatter(lbb, fbb, lw=0.5, color='none', edgecolor=col_dia, \
-        zorder=2, alpha=1.0, marker='d', s=50)
-
-    else:
-        lbb, fbb, lfwhm = filconv(SFILT, x1_tot, ytmp50, DIR_FILT, fw=True, MB=MB, f_regist=False)
-        lbb, fbb16, lfwhm = filconv(SFILT, x1_tot, ytmp16, DIR_FILT, fw=True, MB=MB, f_regist=False)
-        lbb, fbb84, lfwhm = filconv(SFILT, x1_tot, ytmp84, DIR_FILT, fw=True, MB=MB, f_regist=False)
-
-        iix = []
-        for ii in range(len(fbb)):
-            iix.append(np.argmin(np.abs(lbb[ii]-xbb[:])))
-        con_sed = (eybb>0)
-        ax1.scatter(lbb[iix][con_sed], fbb[iix][con_sed], lw=0.5, color='none', edgecolor=col_dia, zorder=3, alpha=1.0, marker='d', s=50)
-
-        if f_plot_resid:
-            conbb_hs = (fybb/eybb>SNlim)
-            axes['B'].scatter(lbb[iix][conbb_hs], ((fybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs], lw=0.5, color='none', edgecolor='r', zorder=3, alpha=1.0, marker='.', s=50)
-            conbb_hs = (fybb/eybb<=SNlim) & (eybb>0)
-            axes['B'].errorbar(lbb[iix][conbb_hs], ((eybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs], yerr=leng,\
-                uplims=((fybb*c/np.square(xbb)/d - fbb)/(eybb*c/np.square(xbb)/d))[iix][conbb_hs] * sigma, linestyle='',\
-                color=col_dat, lw=0.5, marker='', ms=4, label='', zorder=4, capsize=1.5)
-            axes['B'].set_xscale(ax1.get_xscale())
-            axes['B'].set_xlim(ax1.get_xlim())
-            axes['B'].set_xticks(ax1.get_xticks())
-            axes['B'].set_xticklabels(ax1.get_xticklabels())
-            axes['B'].set_xlabel(ax1.get_xlabel())
-            xx = np.arange(axes['B'].get_xlim()[0],axes['B'].get_xlim()[1],100)
-            axes['B'].plot(xx,xx*0,linestyle='--',lw=0.5,color='k')
-            axes['B'].set_ylabel('Residual / $\sigma$')
-            axes['A'].set_xlabel('')
-            axes['A'].set_xticks(ax1.get_xticks())
-            axes['A'].set_xticklabels('')
-
-        # Calculate EW, if there is excess band;
-        try:
-            iix2 = []
-            for ii in range(len(fy_ex)):
-                iix2.append(np.argmin(np.abs(lbb[:]-x_ex[ii])))
-
-            # Rest-frame EW;
-            # Note about 16/84 in fbb
-            EW16 = (fy_ex * c / np.square(x_ex) / d - fbb84[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
-            EW50 = (fy_ex * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
-            EW84 = (fy_ex * c / np.square(x_ex) / d - fbb16[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
-
-            EW50_er1 = ((fy_ex-ey_ex) * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
-            EW50_er2 = ((fy_ex+ey_ex) * c / np.square(x_ex) / d - fbb[iix2]) / (fbb[iix2]) * lfwhm[iix2] / (1.+zbes)
-
-            cnt50 = fbb[iix2]
-            cnt16 = fbb16[iix2]
-            cnt84 = fbb84[iix2]
- 
-            # Luminosity;
-            #Lsun = 3.839 * 1e33 #erg s-1
-            L16 = EW16 * cnt16 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
-            L50 = EW50 * cnt50 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
-            L84 = EW84 * cnt84 * (4.*np.pi*DL**2) * scale * (1+zbes) # A * erg/s/A/cm2 * cm2
-
-            ew_label = []
-            for ii in range(len(fy_ex)):
-                lres = MB.band['%s_lam'%MB.filts[iix2[ii]]][:]
-                fres = MB.band['%s_res'%MB.filts[iix2[ii]]][:]
-                ew_label.append(MB.filts[iix2[ii]])
-
-                print('\n')
-                print('EW016 for', x_ex[ii], 'is %d'%EW16[ii])
-                print('EW050 for', x_ex[ii], 'is %d'%EW50[ii])
-                print('EW084 for', x_ex[ii], 'is %d'%EW84[ii])
-                print('%d_{-%d}^{+%d} , for sed error'%(EW50[ii],EW50[ii]-EW84[ii],EW16[ii]-EW50[ii]))
-                print('Or, %d\pm{%d} , for flux error'%(EW50[ii],EW50[ii]-EW50_er1[ii]))
-        except:
-            print('\nEW calculation; Failed.\n')
-            pass
-
-
-    if save_sed:
-        fbb16_nu = flamtonu(lbb, fbb16*scale, m0set=m0set)
-        fbb_nu = flamtonu(lbb, fbb*scale, m0set=m0set)
-        fbb84_nu = flamtonu(lbb, fbb84*scale, m0set=m0set)
-
-        # Then save full spectrum;
-        col00  = []
-        col1  = fits.Column(name='wave_model', format='E', unit='AA', array=x1_tot)
-        col00.append(col1)
-        col2  = fits.Column(name='f_model_16', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp16[:])
-        col00.append(col2)
-        col3  = fits.Column(name='f_model_50', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp50[:])
-        col00.append(col3)
-        col4  = fits.Column(name='f_model_84', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp84[:])
-        col00.append(col4)
-
-        f_sed_each = False
-        if f_sed_each:
-            # Each component
-            # Stellar
-            col1 = fits.Column(name='wave_model_stel', format='E', unit='AA', array=x0)
-            col00.append(col1)
-            for aa in range(len(age)):
-                col1 = fits.Column(name='f_model_stel_%d'%aa, format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=f_50_comp[aa,:])
-                col00.append(col1)
-            if f_dust:
-                col1 = fits.Column(name='wave_model_dust', format='E', unit='AA', array=x1_dust)
-                col00.append(col1)
-                col1 = fits.Column(name='f_model_dust', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=ytmp_dust50)
-                col00.append(col1)
-            
-        # BB for dust
-        if f_dust:
-            xbb = np.append(xbb,xbbd)
-            fybb = np.append(fybb,fybbd)
-            eybb = np.append(eybb,eybbd)
-
-        col5  = fits.Column(name='wave_obs', format='E', unit='AA', array=xbb)
-        col00.append(col5)
-        col6  = fits.Column(name='f_obs', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=fybb[:] * c / np.square(xbb[:]) / d)
-        col00.append(col6)
-        col7  = fits.Column(name='e_obs', format='E', unit='1e%derg/s/cm2/AA'%(np.log10(scale)), array=eybb[:] * c / np.square(xbb[:]) / d)
-        col00.append(col7)
-
-        hdr = fits.Header()
-        hdr['redshift'] = zbes
-        hdr['id'] = ID
-        hdr['hierarch isochrone'] = isochrone
-        hdr['library'] = LIBRARY
-        hdr['nimf'] = nimf
-        hdr['scale'] = scale
-
-        try:
-            # Chi square:
-            hdr['chi2'] = chi2
-            hdr['hierarch No-of-effective-data-points'] = len(wht3[conw])
-            hdr['hierarch No-of-nondetectioin'] = len(ey[con_up])
-            hdr['hierarch Chi2-of-nondetection'] = chi_nd
-            hdr['hierarch No-of-params'] = ndim_eff
-            hdr['hierarch Degree-of-freedom']  = nod
-            hdr['hierarch reduced-chi2'] = fin_chi2
-        except:
-            print('Chi seems to be wrong...')
-            pass
-
-        try:
-            # Muv
-            MUV = -2.5 * np.log10(Fuv[:]) + MB.m0set
-            hdr['MUV16'] = -2.5 * np.log10(np.percentile(Fuv[:],16)) + MB.m0set
-            hdr['MUV50'] = -2.5 * np.log10(np.percentile(Fuv[:],50)) + MB.m0set
-            hdr['MUV84'] = -2.5 * np.log10(np.percentile(Fuv[:],84)) + MB.m0set
-
-            # Fuv (!= flux of Muv)
-            hdr['FUV16'] = np.percentile(Fuv28[:],16)
-            hdr['FUV50'] = np.percentile(Fuv28[:],50)
-            hdr['FUV84'] = np.percentile(Fuv28[:],84)
-
-            # LIR
-            hdr['LIR16'] = np.percentile(Lir[:],16)
-            hdr['LIR50'] = np.percentile(Lir[:],50)
-            hdr['LIR84'] = np.percentile(Lir[:],84)
-        except:
-            pass
-
-        # UV beta;
-        from .function import get_uvbeta
-        beta_16 = get_uvbeta(x1_tot, ytmp16, zbes)
-        beta_50 = get_uvbeta(x1_tot, ytmp50, zbes)
-        beta_84 = get_uvbeta(x1_tot, ytmp84, zbes)
-        hdr['UVBETA16'] = beta_16
-        hdr['UVBETA50'] = beta_50
-        hdr['UVBETA84'] = beta_84
-
-        # UVJ
-        try:
-            hdr['uv16'] = np.percentile(UVJ[:,0],16)
-            hdr['uv50'] = np.percentile(UVJ[:,0],50)
-            hdr['uv84'] = np.percentile(UVJ[:,0],84)
-            hdr['bv16'] = np.percentile(UVJ[:,1],16)
-            hdr['bv50'] = np.percentile(UVJ[:,1],50)
-            hdr['bv84'] = np.percentile(UVJ[:,1],84)
-            hdr['vj16'] = np.percentile(UVJ[:,2],16)
-            hdr['vj50'] = np.percentile(UVJ[:,2],50)
-            hdr['vj84'] = np.percentile(UVJ[:,2],84)
-            hdr['zj16'] = np.percentile(UVJ[:,3],16)
-            hdr['zj50'] = np.percentile(UVJ[:,3],50)
-            hdr['zj84'] = np.percentile(UVJ[:,3],84)
-        except:
-            print('\nError when writinf UVJ colors;\n')
-            pass
-
-        # EW;
-        try:
-            for ii in range(len(EW50)):
-                hdr['EW_%s_16'%(ew_label[ii])] = EW16[ii]
-                hdr['EW_%s_50'%(ew_label[ii])] = EW50[ii]
-                hdr['EW_%s_84'%(ew_label[ii])] = EW84[ii]
-                hdr['EW_%s_e1'%(ew_label[ii])] = EW50_er1[ii]
-                hdr['EW_%s_e2'%(ew_label[ii])] = EW50_er2[ii]
-                hdr['HIERARCH cnt_%s_16'%(ew_label[ii])]= cnt16[ii]
-                hdr['HIERARCH cnt_%s_50'%(ew_label[ii])]= cnt50[ii]
-                hdr['HIERARCH cnt_%s_84'%(ew_label[ii])]= cnt84[ii]
-                hdr['L_%s_16'%(ew_label[ii])] = L16[ii]
-                hdr['L_%s_50'%(ew_label[ii])] = L50[ii]
-                hdr['L_%s_84'%(ew_label[ii])] = L84[ii]
-        except:
-            pass
-
-        # Version;
-        import gsf
-        hdr['version'] = gsf.__version__
-
-        # Write;
-        colspec = fits.ColDefs(col00)
-        hdu0 = fits.BinTableHDU.from_columns(colspec, header=hdr)
-        hdu0.writeto(MB.DIR_OUT + 'gsf_spec_%s.fits'%(ID), overwrite=True)
-
-        # ASDF;
-        tree_spec = {
-            'id': ID,
-            'redshift': '%.3f'%zbes,
-            'isochrone': '%s'%(isochrone),
-            'library': '%s'%(LIBRARY),
-            'nimf': '%s'%(nimf),
-            'scale': scale,
-            'version_gsf': gsf.__version__
-        }
-
-        # BB;
-        tree_spec.update({'wave': lbb})
-        tree_spec.update({'fnu_16': fbb16_nu})
-        tree_spec.update({'fnu_50': fbb_nu})
-        tree_spec.update({'fnu_84': fbb84_nu})
-        # full spectrum;
-        tree_spec.update({'wave_model': x1_tot})
-        tree_spec.update({'f_model_16': ytmp16})
-        tree_spec.update({'f_model_50': ytmp50})
-        tree_spec.update({'f_model_84': ytmp84})
-
-        # EW;
-        try:
-            for ii in range(len(EW50)):
-                tree_spec.update({'EW_%s_16'%(ew_label[ii]): EW16[ii]})
-                tree_spec.update({'EW_%s_50'%(ew_label[ii]): EW50[ii]})
-                tree_spec.update({'EW_%s_84'%(ew_label[ii]): EW84[ii]})
-                tree_spec.update({'EW_%s_e1'%(ew_label[ii]): EW50_er1[ii]})
-                tree_spec.update({'EW_%s_e2'%(ew_label[ii]): EW50_er2[ii]})
-                tree_spec.update({'cnt_%s_16'%(ew_label[ii]): cnt16[ii]})
-                tree_spec.update({'cnt_%s_50'%(ew_label[ii]): cnt50[ii]})
-                tree_spec.update({'cnt_%s_84'%(ew_label[ii]): cnt84[ii]})
-                tree_spec.update({'L_%s_16'%(ew_label[ii]): L16[ii]})
-                tree_spec.update({'L_%s_50'%(ew_label[ii]): L50[ii]})
-                tree_spec.update({'L_%s_84'%(ew_label[ii]): L84[ii]})
-        except:
-            pass
-
-        # Each component
-        # Stellar
-        tree_spec.update({'wave_model_stel': x0})
-
-        if f_sed_each:
-            for aa in range(len(age)):
-                tree_spec.update({'f_model_stel_%d'%aa: f_50_comp[aa,:]})
-
-        if f_dust:
-            # dust
-            tree_spec.update({'wave_model_dust': x1_dust})
-            tree_spec.update({'f_model_dust': ytmp_dust50})            
-        # BB for dust
-        tree_spec.update({'wave_obs': xbb})
-        tree_spec.update({'f_obs': fybb[:] * c / np.square(xbb[:]) / d})
-        tree_spec.update({'e_obs': eybb[:] * c / np.square(xbb[:]) / d})
-        # grism:
-        if f_grsm:
-            tree_spec.update({'fg0_obs': fg0 * c/np.square(xg0)/d})
-            tree_spec.update({'eg0_obs': eg0 * c/np.square(xg0)/d})
-            tree_spec.update({'wg0_obs': xg0})
-            tree_spec.update({'fg1_obs': fg1 * c/np.square(xg1)/d})
-            tree_spec.update({'eg1_obs': eg1 * c/np.square(xg1)/d})
-            tree_spec.update({'wg1_obs': xg1})
-            tree_spec.update({'fg2_obs': fg2 * c/np.square(xg2)/d})
-            tree_spec.update({'eg2_obs': eg2 * c/np.square(xg2)/d})
-            tree_spec.update({'wg2_obs': xg2})
-
-        af = asdf.AsdfFile(tree_spec)
-        af.write_to(MB.DIR_OUT + 'gsf_spec_%s.asdf'%(ID), all_array_compression='zlib')
-
-    #
-    # SED params in plot
-    #
-    if f_label:
-        fd = fits.open(MB.DIR_OUT + 'SFH_' + ID + '.fits')[0].header
-        if f_dust:
-            label = 'ID: %s\n$z:%.2f$\n$\log M_*/M_\odot:%.2f$\n$\log M_\mathrm{dust}/M_\odot:%.2f$\n$T_\mathrm{dust}/K:%.1f$\n$\log Z_*/Z_\odot:%.2f$\n$\log T_0$/Gyr$:%.2f$\n$\log \\tau$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$\n$\\chi^2/\\nu:%.2f$'\
-            %(ID, zbes, float(fd['Mstel_50']), MD50, TD50, float(fd['Z_MW_50']), float(fd['T_MW_50']), float(fd['TAU_50']), float(fd['AV_50']), fin_chi2)
-            ylabel = ymax*0.45
-        else:
-            label = 'ID: %s\n$z:%.2f$\n$\log M_*/M_\odot:%.2f$\n$\log Z_*/Z_\odot:%.2f$\n$\log T_0$/Gyr$:%.2f$\n$\log \\tau$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$\n$\\chi^2/\\nu:%.2f$'\
-            %(ID, zbes, float(fd['Mstel_50']), float(fd['Z_MW_50']), float(fd['T_MW_50']), float(fd['TAU_50']), float(fd['AV_50']), fin_chi2)
-            ylabel = ymax*0.4
-
-        ax1.text(2200, ylabel, label,\
-        fontsize=7, bbox=dict(facecolor='w', alpha=0.7), zorder=10)
-        
-    #######################################
-    ax1.xaxis.labelpad = -3
-    if f_grsm:
-        conlim = (x0>10000) & (x0<25000)
-        xgmin, xgmax = np.min(x0[conlim]),np.max(x0[conlim]), #7500, 17000
-        ax2t.set_xlabel('')
-        ax2t.set_xlim(xgmin, xgmax)
-
-        conaa = (x0>xgmin-50) & (x0<xgmax+50)
-        ymaxzoom = np.max(ysum[conaa]*c/np.square(x0[conaa])/d) * 1.15
-        yminzoom = np.min(ysum[conaa]*c/np.square(x0[conaa])/d) / 1.15
-
-        ax2t.set_ylim(yminzoom, ymaxzoom)
-        ax2t.xaxis.labelpad = -2
-        if xgmax>20000:
-            ax2t.set_xticks([8000, 12000, 16000, 20000, 24000])
-            ax2t.set_xticklabels(['0.8', '1.2', '1.6', '2.0', '2.4'])
-        else:
-            ax2t.set_xticks([8000, 10000, 12000, 14000, 16000])
-            ax2t.set_xticklabels(['0.8', '1.0', '1.2', '1.4', '1.6'])
-
-    if f_dust:
-        try:
-            contmp = (x1_tot>10*1e4)
-            y3min, y3max = -.2*np.max((model_tot * c/ np.square(x1_tot) / d)[contmp]), np.max((model_tot * c/ np.square(x1_tot) / d)[contmp])*2.0
-            ax3t.set_ylim(y3min, y3max)
-        except:
-            if verbose:
-                print('y3 limit is not specified.')
-            pass
-        ax3t.set_xlim(1e5, 3e7)
-        ax3t.set_xscale('log')
-        ax3t.set_xticks([100000, 1000000, 10000000])
-        ax3t.set_xticklabels(['10', '100', '1000'])
-
-    ###############
-    # Line name
-    ###############
-    LN0 = ['Mg2', '$NeIV$', '[OII]', 'H$\theta$', 'H$\eta$', 'Ne3?', 'H$\delta$', 'H$\gamma$', 'H$\\beta$', 'O3', 'O3', 'Mgb', 'Halpha', 'S2L', 'S2H']
-    LW0 = [2800, 3347, 3727, 3799, 3836, 3869, 4102, 4341, 4861, 4959, 5007, 5175, 6563, 6717, 6731]
-    fsl = 9 # Fontsize for line
-    if f_grsm:
-        try:
-            for ii in range(len(LW)):
-                ll = np.argmin(np.abs(LW[ii]-LW0[:]))
-
-                if ll == 2 and FLW[ii] == 1: 
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
-                    ax2t.text(xxl[0]-130, yyl[0]*1.28, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
-
-                elif (ll == 9 and FLW[ii] == 1):
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
-
-                elif (ll == 10 and FLW[ii] == 1):
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.5,yminzoom+(ymaxzoom-yminzoom)*0.65, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
-                    ax2t.text(xxl[0]+40, yyl[0]*0.75, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
-
-                elif FLW[ii] == 1 and (ll == 6 or ll == 7 or ll == 8):
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.2,yminzoom+(ymaxzoom-yminzoom)*0.35, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
-                    ax2t.text(xxl[0]+40, yyl[0]*0.95, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
-
-                elif ll == 6 or ll == 7 or ll == 8:
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.2,yminzoom+(ymaxzoom-yminzoom)*0.35, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color='gray', zorder=1, alpha=1., label='', capsize=0)
-                    ax2t.text(xxl[0]+40, yyl[0]*0.95, '%s'%(LN0[ll]),  color='gray', fontsize=9, rotation=90)
-
-                elif FLW[ii] == 1:
-                    yyl = np.arange(yminzoom+(ymaxzoom-yminzoom)*0.7,yminzoom+(ymaxzoom-yminzoom)*.95, 0.01)
-                    xxl = yyl * 0 + LW0[ll]
-                    ax2t.errorbar(xxl, yyl, lw=0.5, color=lcb, zorder=20, alpha=1., label='', capsize=0)
-                    ax2t.text(xxl[0]+40, yyl[0]*1.25, '%s'%(LN0[ll]),  color=lcb, fontsize=9, rotation=90)
-        except:
-            pass
-
-    # Filters
-    ind_remove = np.where((wht3<=0) | (ey<=0))[0]
-    if f_plot_filter:
-        ax1 = plot_filter(MB, ax1, ymax, scl=scl_yaxis, ind_remove=ind_remove)
-
-    ####################
-    ## Save
-    ####################
-    ax1.legend(loc=1, fontsize=11)
-    if figpdf:
-        fig.savefig(MB.DIR_OUT + 'SPEC_' + ID + '_spec.pdf', dpi=dpi)
-    else:
-        fig.savefig(MB.DIR_OUT + 'SPEC_' + ID + '_spec.png', dpi=dpi)
-
-
-def plot_filter(MB, ax, ymax, scl=0.3, cmap='gist_rainbow', alp=0.4, ind_remove=[]):
-    '''
-    Add filter response curve to ax1.
-
-    '''
-    NUM_COLORS = len(MB.filts)
-    cm = plt.get_cmap(cmap)
-    cols = [cm(1 - 1.*i/NUM_COLORS) for i in range(NUM_COLORS)]
-
-    wavecen = []
-    for ii,filt in enumerate(MB.filts):
-        wave = MB.band['%s_lam'%filt]
-        flux = MB.band['%s_res'%filt]
-        #wavecen.append(np.median(wave * flux)/np.median(flux))
-        con = (flux/flux.max()>0.1)
-        wavecen.append(np.min(wave[con]))
-    wavecen = np.asarray(wavecen)
-    wavecen_sort = np.sort(wavecen)
-
-    for ii,filt in enumerate(MB.filts):
-        iix = np.argmin(np.abs(wavecen_sort[:]-wavecen[ii]))
-        col = cols[iix]
-        wave = MB.band['%s_lam'%filt]
-        flux = MB.band['%s_res'%filt]
-        if ii in ind_remove:
-            continue
-        ax.plot(wave, ((flux / np.max(flux))*0.8 - 1) * ymax * scl, linestyle='-', color='k', lw=0.2)
-        ax.fill_between(wave, (wave*0 - ymax)*scl, ((flux / np.max(flux))*0.8 - 1) * ymax * scl, linestyle='-', lw=0, color=col, alpha=alp)
-
-    return ax
-
-
-def plot_corner_physparam_summary(MB, fig=None, out_ind=0, DIR_OUT='./', mmax=1000, TMIN=0.0001, tau_lim=0.01, f_plot_filter=True, 
-    scale=1e-19, NRbb_lim=10000):
-    '''
-    Purpose
-    -------
-    For summary. In the same format as plot_corner_physparam_frame.
-
-    Notes
-    -----
-    Tau model not supported.
-    '''
-
-    col = ['violet', 'indigo', 'b', 'lightblue', 'lightgreen', 'g', 'orange', 'coral', 'r', 'darkred']#, 'k']
-    import matplotlib
-    import matplotlib.cm as cm
-    import scipy.stats as stats
-
-    nage = MB.nage 
-    fnc  = MB.fnc 
-    bfnc = MB.bfnc 
-    ID   = MB.ID
-    Z    = MB.Zall
-    age  = MB.age
-    c = MB.c
-
-    tau0 = MB.tau0 
-    dust_model = MB.dust_model
-    DIR_TMP = MB.DIR_TMP
-
-    Txmax = np.max(age) + 1.0 
-
-    ###########################
-    # Open result file
-    ###########################
-    lib = fnc.open_spec_fits(fall=0)
-    lib_all = fnc.open_spec_fits(fall=1, orig=True)
-
-    file = MB.DIR_OUT + 'summary_' + ID + '.fits'
-    hdul = fits.open(file) # open a FITS file
-
-    # Redshift MC
-    zp50  = hdul[1].data['zmc'][1]
-    zp16  = hdul[1].data['zmc'][0]
-    zp84  = hdul[1].data['zmc'][2]
-
-    M50 = hdul[1].data['ms'][1]
-    M16 = hdul[1].data['ms'][0]
-    M84 = hdul[1].data['ms'][2]
-    print('Total stellar mass is %.2e'%(M50))
-
-    A50 = np.zeros(len(age), dtype='float')
-    A16 = np.zeros(len(age), dtype='float')
-    A84 = np.zeros(len(age), dtype='float')
-    for aa in range(len(age)):
-        A50[aa] = hdul[1].data['A'+str(aa)][1]
-        A16[aa] = hdul[1].data['A'+str(aa)][0]
-        A84[aa] = hdul[1].data['A'+str(aa)][2]
-
-    Asum = np.sum(A50)
-    aa = 0
-
-    Av16 = hdul[1].data['Av'+str(aa)][0]
-    Av50 = hdul[1].data['Av'+str(aa)][1]
-    Av84 = hdul[1].data['Av'+str(aa)][2]
-
-    Z50  = np.zeros(len(age), dtype='float')
-    Z16  = np.zeros(len(age), dtype='float')
-    Z84  = np.zeros(len(age), dtype='float')
-
-    NZbest = np.zeros(len(age), dtype='int')
-    for aa in range(len(age)):
-        Z50[aa] = hdul[1].data['Z'+str(aa)][1]
-        Z16[aa] = hdul[1].data['Z'+str(aa)][0]
-        Z84[aa] = hdul[1].data['Z'+str(aa)][2]
-        #NZbest[aa]= bfnc.Z2NZ(Z50[aa])
-
-    ZZ50 = np.sum(Z50*A50)/np.sum(A50) # Light weighted Z.
-    chi = hdul[1].data['chi'][0]
-    chin = hdul[1].data['chi'][1]
-    fitc = chin
-    Cz0 = hdul[0].header['Cz0']
-    Cz1 = hdul[0].header['Cz1']
-    Cz2 = hdul[0].header['Cz2']
-    zbes = hdul[0].header['z']
-    zscl = (1.+zbes)
-
-    # plot Configuration
-    if MB.fzmc == 1:
-        Par = ['$\log M_*/M_\odot$', '$\log T_*$/Gyr', '$A_V$/mag', '$\log Z_* / Z_\odot$', '$z$']
-    else:
-        Par = ['$\log M_*/M_\odot$', '$\log T_*$/Gyr', '$A_V$/mag', '$\log Z_* / Z_\odot$']
-
-    K = len(Par) # No of params.
-    factor = 2.0           # size of one side of one panel
-    lbdim  = 0.5 * factor   # size of left/bottom margin
-    trdim  = 0.2 * factor   # size of top/right margin
-    whspace= 0.02         # w/hspace size
-    plotdim= factor * K + factor * (K - 1.) * whspace
-    dim    = lbdim + plotdim + trdim
-    sclfig = 0.7
-
-
-    # Format the figure.
-    fig, axes = plt.subplots(K, K, figsize=(dim*sclfig*2, dim*sclfig))
-    # Format the figure.
-    lb = lbdim / dim
-    tr = (lbdim + plotdim) / dim
-    #fig.subplots_adjust(left=lb*1.06, bottom=lb*.9, right=tr, top=tr*.99,
-    fig.subplots_adjust(left=0.5, bottom=lb*.9, right=tr, top=tr*.99,
-                        wspace=whspace, hspace=whspace)
-
-    # For spec plot
-    ax0 = fig.add_axes([0.05,0.73,0.37,0.23])
-    ax1 = fig.add_axes([0.05,0.40,0.37,0.23])
-    ax2 = fig.add_axes([0.05,0.07,0.37,0.23])
-
-    if MB.f_dust:
-        MB.dict = MB.read_data(MB.Cz0, MB.Cz1, MB.Cz2, MB.zgal, add_fir=True)
-    else:
-        MB.dict = MB.read_data(MB.Cz0, MB.Cz1, MB.Cz2, MB.zgal)
-
-    # Get data points;
-    NRbb = MB.dict['NRbb']
-    xbb = MB.dict['xbb'] 
-    fybb = MB.dict['fybb']
-    eybb = MB.dict['eybb']
-    exbb = MB.dict['exbb']
-    snbb = fybb/eybb
-
-    # Get spec data points;
-    dat = np.loadtxt(DIR_TMP + 'spec_obs_' + ID + '.cat', comments='#')
-    NR  = dat[:,0]
-    x   = dat[:,1]
-    fy00 = dat[:,2]
-    ey00 = dat[:,3]
-
-    con0 = (NR<1000) #& (fy/ey>SNlim)
-    xg0  = x[con0]
-    fg0  = fy00[con0] * Cz0
-    eg0  = ey00[con0] * Cz0
-    con1 = (NR>=1000) & (NR<2000) #& (fy/ey>SNlim)
-    xg1  = x[con1]
-    fg1  = fy00[con1] * Cz1
-    eg1  = ey00[con1] * Cz1
-    con2 = (NR>=2000) & (NR<NRbb_lim) #& (fy/ey>SNlim)
-    xg2  = x[con2]
-    fg2  = fy00[con2] * Cz2
-    eg2  = ey00[con2] * Cz2
-
-    con_bb = (NR>=NRbb_lim)
-    xg_bb = x[con_bb]
-    fg_bb = fy00[con_bb]
-    eg_bb = ey00[con_bb]
-
-    fy01 = np.append(fg0,fg1)
-    ey01 = np.append(eg0,eg1)
-    fy02 = np.append(fy01,fg2)
-    ey02 = np.append(ey01,eg2)
-
-    fy = np.append(fy01,fg_bb)
-    ey = np.append(ey01,eg_bb)
-    wht = 1./np.square(ey)
-
-    # Determine scale here;
-    if scale == None:
-        conbb_hs = (fybb/eybb > SNlim)
-        scale = 10**(int(np.log10(np.nanmax(fybb[conbb_hs] * c / np.square(xbb[conbb_hs])) / MB.d)))
-    d = MB.d * scale
-
-    # BB photometry
-    conspec = (NR<NRbb_lim)
-    sigma = 1.
-    conbb = (fybb/eybb > sigma)
-    ax0.errorbar(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, yerr=eybb[conbb]*c/np.square(xbb[conbb])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
-    ax0.plot(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, '.r', ms=10, linestyle='', linewidth=0, zorder=4)
-
-    conebb_ls = (fybb/eybb <= sigma)
-    if len(fybb[conebb_ls])>0:
-        leng = np.max(fybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d) * 0.05
-        ax0.errorbar(xbb[conebb_ls], eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, yerr=leng,\
-            uplims=eybb[conebb_ls] * c / np.square(xbb[conebb_ls]) / d * sigma, linestyle='',color='r', marker='', ms=4, label='', zorder=4, capsize=3)
-    
-    ####################
-    # MCMC corner plot.
-    ####################
-    file  = MB.DIR_OUT + 'chain_' + ID + '_corner.cpkl'
-    niter = 0
-    data  = loadcpkl(file)
-
-    try:
-        ndim = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
-        burnin = data['burnin']
-        nmc = data['niter']
-        nwalk = data['nwalkers']
-        Nburn = burnin
-        samples = data['chain'][:]
-    except:
-        if verbose: print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
-
-    af = MB.af
-    sedpar = af['ML']
-
-    getcmap = matplotlib.cm.get_cmap('jet')
-    nc = np.arange(0, nmc, 1)
-    col = getcmap((nc-0)/(nmc-0))
-
-    Ntmp = np.zeros(mmax, dtype='float')
-    lmtmp= np.zeros(mmax, dtype='float')
-    Avtmp= np.zeros(mmax, dtype='float')
-    Ztmp = np.zeros(mmax, dtype='float')
-    Ttmp = np.zeros(mmax, dtype='float')
-    ACtmp= np.zeros(mmax, dtype='float')
-    redshifttmp = np.zeros(mmax, dtype='float')
-
-    # Time bin
-    Tuni = MB.cosmo.age(zbes).value
-    Tuni0 = (Tuni - age[:])
-    delT  = np.zeros(len(age),dtype='float')
-    delTl = np.zeros(len(age),dtype='float')
-    delTu = np.zeros(len(age),dtype='float')
-
-    if len(age) == 1:
-        for aa in range(len(age)):
-            try:
-                tau_ssp = float(MB.inputs['TAU_SSP'])
-            except:
-                tau_ssp = tau_lim
-            delTl[aa] = tau_ssp/2
-            delTu[aa] = tau_ssp/2
-            delT[aa]  = delTu[aa] + delTl[aa]
-    else:
-        for aa in range(len(age)):
-            if aa == 0:
-                delTl[aa] = age[aa]
-                delTu[aa] = (age[aa+1]-age[aa])/2.
-                delT[aa]  = delTu[aa] + delTl[aa]
-            elif Tuni < age[aa]:
-                delTl[aa] = (age[aa]-age[aa-1])/2.
-                delTu[aa] = 10.
-                delT[aa]  = delTu[aa] + delTl[aa]
-            elif aa == len(age)-1:
-                delTl[aa] = (age[aa]-age[aa-1])/2.
-                delTu[aa] = Tuni - age[aa]
-                delT[aa]  = delTu[aa] + delTl[aa]
-            else:
-                delTl[aa] = (age[aa]-age[aa-1])/2.
-                delTu[aa] = (age[aa+1]-age[aa])/2.
-                delT[aa]  = delTu[aa] + delTl[aa]
-
-            if delT[aa] < tau_lim:
-                # This is because fsps has the minimum tau = tau_lim
-                delT[aa] = tau_lim
-
-
-    delT[:]  *= 1e9 # Gyr to yr
-    delTl[:] *= 1e9 # Gyr to yr
-    delTu[:] *= 1e9 # Gyr to yr
-
-    files = [] # For gif animation
-    SFmax = 0
-    Tsmin = 0
-    Tsmax = 0
-    Zsmin = 0
-    Zsmax = 0
-    AMtmp = 0
-    AMtmp16 = 0
-    AMtmp84 = 0
-
-    for ii in range(len(age)):
-
-        if aa == 0 or MB.ZEVOL:
-            ZZ_tmp = Z50[ii]
-            ZZ_tmp16 = Z16[ii]
-            ZZ_tmp84 = Z84[ii]
-        else:
-            ZZ_tmp = Z50[0]
-            ZZ_tmp16 = Z16[0]
-            ZZ_tmp84 = Z84[0]
-        
-        try:
-            AA_tmp = 10**np.max(samples['A'+str(ii)][:])
-            AA_tmp84 = 10**np.percentile(samples['A'+str(ii)][:],95)
-            AA_tmp16 = 10**np.percentile(samples['A'+str(ii)][:],5)
-        except:
-            AA_tmp = 0
-            AA_tmp84 = 0
-            AA_tmp16 = 0
-
-        nZtmp = bfnc.Z2NZ(ZZ_tmp)
-        mslist = sedpar['ML_'+str(nZtmp)][ii]
-
-        AMtmp16 += mslist*AA_tmp16
-        AMtmp84 += mslist*AA_tmp84
-
-        Tsmax += age[ii] * AA_tmp84 * mslist
-        Tsmin += age[ii] * AA_tmp16 * mslist
-
-        Zsmax += 10**ZZ_tmp84 * AA_tmp84 * mslist
-        Zsmin += 10**ZZ_tmp16 * AA_tmp16 * mslist
-
-        SFtmp = AA_tmp * mslist / delT[ii]
-        if SFtmp > SFmax:
-            SFmax = SFtmp
-    if SFmax > 0.5e4:
-        SFmax = 0.5e4
-
-    delM = np.log10(M84) - np.log10(M16)
-    if MB.fzmc == 1:
-        NPARmin = [np.log10(M16)-.1, np.log10(Tsmin/AMtmp16)-0.1, Av16-0.1, np.log10(Zsmin/AMtmp16)-0.2, np.percentile(samples['zmc'],1)-0.1]
-        NPARmax = [np.log10(M84)+.1, np.log10(Tsmax/AMtmp84)+0.2, Av84+0.1, np.log10(Zsmax/AMtmp84)+0.2, np.percentile(samples['zmc'],99)+0.1]
-    else:
-        NPARmin = [np.log10(M16)-.1, np.log10(Tsmin/AMtmp16)-0.1, Av16-0.1, np.log10(Zsmin/AMtmp16)-0.2]
-        NPARmax = [np.log10(M84)+.1, np.log10(Tsmax/AMtmp84)+0.2, Av84+0.1, np.log10(Zsmax/AMtmp84)+0.2]
-
-    # For redshift
-    if zbes<2:
-        zred  = [zbes, 2, 3, 6]
-        zredl = ['$z$', 2, 3, 6]
-    elif zbes<2.5:
-        zred  = [zbes, 2.5, 3, 6]
-        zredl = ['$z$', 2.5, 3, 6]
-    elif zbes<3.:
-        zred  = [zbes, 3, 6]
-        zredl = ['$z$', 3, 6]
-    elif zbes<6:
-        zred  = [zbes, 6]
-        zredl = ['$z$', 6]
-    else:
-        zred  = [zbes, 12]
-        zredl = ['$z$', 12]
-
-    Tzz = np.zeros(len(zred), dtype='float')
-    for zz in range(len(zred)):
-        Tzz[zz] = (Tuni - MB.cosmo.age(zred[zz]).value) #/ cc.Gyr_s
-        if Tzz[zz] < TMIN:
-            Tzz[zz] = TMIN
-
-
-    def density_estimation(m1, m2):
-        xmin, xmax = np.min(m1), np.max(m1)
-        ymin, ymax = np.min(m2), np.max(m2)
-        X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
-        positions = np.vstack([X.ravel(), Y.ravel()])
-        values = np.vstack([m1, m2])
-        kernel = stats.gaussian_kde(values)
-        Z = np.reshape(kernel(positions).T, X.shape)
-        return X, Y, Z
-
-
-    for kk in range(0,mmax,1):
-        nr = np.random.randint(len(samples))
-        try:
-            Avtmp[kk] = samples['Av'][nr]
-        except:
-            Avtmp[kk] = MB.AVFIX
-
-        ZMM = np.zeros((len(age)), dtype='float') # Mass weighted Z.
-        ZM = np.zeros((len(age)), dtype='float') # Light weighted T.
-        ZC = np.zeros((len(age)), dtype='float') # Light weighted T.
-        SF = np.zeros((len(age)), dtype='float') # SFR
-        AM = np.zeros((len(age)), dtype='float') # Light weighted T.
-        II0 = nage
-
-        for ss in range(len(age)):
-            ii = int(len(II0) - ss - 1) # from old to young templates.
-            
-            try:
-                AA_tmp = 10**samples['A'+str(ii)][nr]
-            except:
-                AA_tmp = 0
-                pass
-
-            try:
-                ZZ_tmp = samples['Z'+str(ii)][nr]
-            except:
-                try:
-                    ZZ_tmp = samples['Z0'][nr]
-                except:
-                    ZZ_tmp = MB.ZFIX
-
-            nZtmp = bfnc.Z2NZ(ZZ_tmp)
-            mslist = sedpar['ML_'+str(nZtmp)][ii]
-            lmtmp[kk] += AA_tmp * mslist
-            Ztmp[kk] += (10 ** ZZ_tmp) * AA_tmp * mslist
-            Ttmp[kk] += age[ii] * AA_tmp * mslist
-            ACtmp[kk] += AA_tmp * mslist
-            if MB.fzmc == 1:
-                redshifttmp[kk] = samples['zmc'][nr]
-
-            AM[ii] = AA_tmp * mslist
-            SF[ii] = AA_tmp * mslist / delT[ii]
-            ZM[ii] = ZZ_tmp
-            ZMM[ii]= (10 ** ZZ_tmp) * AA_tmp * mslist
-
-            # SED
-            flim = 0.05
-            if ss == 0:
-                y0, x0 = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all)
-                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib)
-                ysump = y0p #* 1e18
-                ysum = y0  #* 1e18
-                if AA_tmp/Asum > flim:
-                    ax0.plot(x0, y0 * c/ np.square(x0) / d, '--', lw=.1, color=col[ii], zorder=-1, label='', alpha=0.01)
-            else:
-                y0_r, x0_tmp = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all)
-                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib)
-                ysump += y0p  #* 1e18
-                ysum += y0_r #* 1e18
-                if AA_tmp/Asum > flim:
-                    ax0.plot(x0, y0_r * c/ np.square(x0) / d, '--', lw=.1, color=col[ii], zorder=-1, label='', alpha=0.01)
-
-        for ss in range(len(age)):
-            ii = ss # from old to young templates.
-            AC = np.sum(AM[ss:])
-            if AC > 0:
-                ZC[ss] = np.log10(np.sum(ZMM[ss:])/AC)
-            else:
-                ZC[ss] = -99
-
-        # Plot Total
-        ax0.plot(x0, ysum * c/ np.square(x0) / d, '-', lw=.1, color='gray', zorder=-1, label='', alpha=0.01)
-
-        if len(age)==1:
-            ax1.plot(age[:], SF[:], marker='.', linestyle='-', lw=.1, color='k', zorder=-1, label='', alpha=0.01)
-            ax2.plot(age[:], ZC[:], marker='.', linestyle='-', lw=.1, color='k', zorder=-1, label='', alpha=0.01)
-        else:
-            ax1.plot(age[:], SF[:], marker='', linestyle='-', lw=.1, color='k', zorder=-1, label='', alpha=0.01)
-            ax2.plot(age[:], ZC[:], marker='', linestyle='-', lw=.1, color='k', zorder=-1, label='', alpha=0.01)
-
-        # Get ymax
-        if f_plot_filter:
-            scl_yaxis = 0.2
-        else:
-            scl_yaxis = 0
-
-        ymax_bb = np.max(fybb[conbb] * c / np.square(xbb[conbb]) / d) * 1.10
-        ymax_temp = np.max(ysum * c/ np.square(x0) / d) * 1.10
-        ymax = np.max([ymax_bb, ymax_temp])
-
-        # Convert into log
-        Ztmp[kk] /= ACtmp[kk]
-        Ttmp[kk] /= ACtmp[kk]
-        Ntmp[kk] = kk
-
-        lmtmp[kk] = np.log10(lmtmp[kk])
-        Ztmp[kk] = np.log10(Ztmp[kk])
-        Ttmp[kk] = np.log10(Ttmp[kk])
-
-        if MB.fzmc == 1:
-            NPAR = [lmtmp[:kk+1], Ttmp[:kk+1], Avtmp[:kk+1], Ztmp[:kk+1], redshifttmp[:kk+1]]
-        else:
-            NPAR = [lmtmp[:kk+1], Ttmp[:kk+1], Avtmp[:kk+1], Ztmp[:kk+1]]
-
-        if kk == mmax-1:
-            # Histogram
-            for i, x in enumerate(Par):
-                ax = axes[i, i]
-                x1min, x1max = NPARmin[i], NPARmax[i]
-                nbin = 50
-                binwidth1 = (x1max-x1min)/nbin
-                bins1 = np.arange(x1min, x1max + binwidth1, binwidth1)
-                n, bins, patches = ax.hist(NPAR[i], bins=bins1, orientation='vertical', color='b', histtype='stepfilled', alpha=0.6)
-                yy = np.arange(0,np.max(n)*1.3,1)
-
-                try:
-                    ax.plot(yy*0+np.percentile(NPAR[i],16), yy, linestyle='--', color='gray', lw=1)
-                    ax.plot(yy*0+np.percentile(NPAR[i],84), yy, linestyle='--', color='gray', lw=1)
-                    ax.plot(yy*0+np.percentile(NPAR[i],50), yy, linestyle='-', color='gray', lw=1)
-
-                    ax.text(np.percentile(NPAR[i],16), np.max(yy)*1.02, '%.2f'%(np.percentile(NPAR[i],16)), fontsize=9)
-                    ax.text(np.percentile(NPAR[i],50), np.max(yy)*1.02, '%.2f'%(np.percentile(NPAR[i],50)), fontsize=9)
-                    ax.text(np.percentile(NPAR[i],84), np.max(yy)*1.02, '%.2f'%(np.percentile(NPAR[i],84)), fontsize=9)
-                except:
-                    print('Failed at i,x=',i,x)
-
-                ax.set_xlim(x1min, x1max)
-                ax.set_yticklabels([])
-                if i == K-1:
-                    ax.set_xlabel('%s'%(Par[i]), fontsize=12)
-                if i < K-1:
-                    ax.set_xticklabels([])
-
-        # Scatter and contour
-        for i, x in enumerate(Par):
-            for j, y in enumerate(Par):
-                if i > j:
-                    ax = axes[i, j]
-                    ax.scatter(NPAR[j], NPAR[i], c='b', s=1, marker='.', alpha=0.01)
-                    ax.set_xlabel('%s'%(Par[j]), fontsize=12)
-
-                    if kk == mmax-1:
-                        try:
-                            Xcont, Ycont, Zcont = density_estimation(NPAR[j], NPAR[i])
-                            mZ = np.max(Zcont)
-                            ax.contour(Xcont, Ycont, Zcont, levels=[0.68*mZ, 0.95*mZ, 0.99*mZ], linewidths=[0.8,0.5,0.3], colors='orange')
-                        except:
-                            print('Error occurs when density estimation. Maybe because some params are fixed.')
-                            pass
-
-                    x1min, x1max = NPARmin[j], NPARmax[j]
-                    y1min, y1max = NPARmin[i], NPARmax[i]
-                    ax.set_xlim(x1min, x1max)
-                    ax.set_ylim(y1min, y1max)
-
-                    if j==0:
-                        ax.set_ylabel('%s'%(Par[i]), fontsize=12)
-                    if j>0:
-                        ax.set_yticklabels([])
-                    if i<K-1:
-                        ax.set_xticklabels([])
-                    if i == 2:
-                        ax.yaxis.labelpad = 5.
-
-                if i < j:
-                    ax = axes[i, j]
-                    ax.set_xticklabels([])
-                    ax.set_yticklabels([])
-                    ax.set_frame_on(False)
-                    ax.set_xticks([])
-                    ax.set_yticks([])
-
-                if i == j:
-                    ax = axes[i, j]
-                    ax.set_yticklabels([])
-                    if i == K-1:
-                        ax.set_xlabel('%s'%(Par[i]), fontsize=12)
-                    if i < K-1:
-                        ax.set_xticklabels([])
-
-
-        if kk%10 == 0 and out_ind == 1:
-            fname = MB.DIR_OUT +  + '%d.png' % kk
-            print('Saving frame', fname)
-            plt.savefig(fname, dpi=200)
-            files.append(fname)
-
-    # For the last one
-    ax0.plot(xg0, fg0 * c / np.square(xg0) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='royalblue', label='')
-    ax0.plot(xg1, fg1 * c / np.square(xg1) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='g', label='')
-    ax0.plot(xg2, fg2 * c / np.square(xg2) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='#DF4E00', label='')
-
-    ax0.set_xlim(2200, 88000)
-    ax0.set_xscale('log')
-    ax0.set_ylim(-ymax * scl_yaxis, ymax)
-    ax0.set_xlabel('Observed wavelength ($\mathrm{\mu m}$)', fontsize=14)
-    ax0.set_ylabel('Flux ($10^{%d}\mathrm{erg}/\mathrm{s}/\mathrm{cm}^{2}/\mathrm{\AA}$)'%(np.log10(scale)),fontsize=12,labelpad=-2)
-    ax1.set_xlabel('$t_\mathrm{lookback}$ (Gyr)', fontsize=12)
-    ax1.set_ylabel('$\dot{M_*}/M_\odot$yr$^{-1}$', fontsize=12)
-    ax1.set_xlim(np.min(age)*0.8, Txmax)
-    ax1.set_ylim(0, SFmax)
-    ax1.set_xscale('log')
-    ax2.set_xlabel('$t_\mathrm{lookback}$ (Gyr)', fontsize=12)
-    ax2.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
-    ax2.set_xlim(np.min(age)*0.8, Txmax)
-    if round(np.min(Z),2) == round(np.max(Z),2):
-        ax2.set_ylim(-0.8, 0.5)
-    else:
-        ax2.set_ylim(np.min(Z)-0.05, np.max(Z)+0.05)
-    ax2.set_xscale('log')
-    #ax2.yaxis.labelpad = -5
-
-    ax1t = ax1.twiny()
-    ax2t = ax2.twiny()
-    ax1t.set_xlim(0.008, Txmax)
-    ax1t.set_xscale('log')
-
-    ax1t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
-    ax1t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
-    #ax1t.set_xticklabels(zredl[:])
-    #ax1t.set_xticks(Tzz[:])
-
-    ax1t.tick_params(axis='x', labelcolor='k')
-    ax1t.xaxis.set_ticks_position('none')
-    ax1.plot(Tzz, Tzz*0+SFmax, marker='|', color='k', ms=3, linestyle='None')
-
-    ax2t.set_xlim(0.008, Txmax)
-    ax2t.set_xscale('log')
-    #ax2t.set_xticklabels(zredl[:])
-    #ax2t.set_xticks(Tzz[:])
-    ax2t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
-    ax2t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
-
-    ax2t.tick_params(axis='x', labelcolor='k')
-    ax2t.xaxis.set_ticks_position('none')
-    ax2.plot(Tzz, Tzz*0+0.5, marker='|', color='k', ms=3, linestyle='None')
-
-    # Filters
-    ind_remove = np.where((wht<=0) | (ey<=0))[0]
-    if f_plot_filter:
-        ax1 = plot_filter(MB, ax1, ymax, scl=scl_yaxis, ind_remove=ind_remove)
-        xx = np.arange(2200,100000,100)
-        ax0.plot(xx, xx * 0, linestyle='--', lw=0.5, color='k')
-
-    plt.savefig(MB.DIR_OUT + 'param_' + ID + '_corner.png', dpi=150)
-
-
-def plot_corner_physparam_cumulative_frame(ID, Zall=np.arange(-1.2,0.4249,0.05), age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0], \
-    tau0=[0.1,0.2,0.3], fig=None, dust_model=0, out_ind=0, snlimbb=1.0, DIR_OUT='./', NRbb_lim=10000):
-    '''
-    Creat "cumulative" png for gif image.
-    
-    If you like to creat temporal png for gif image.
-
-    Parameters
-    ----------
-    snlimbb : float
-        SN limit to show flux or up lim in SED.
-    '''
-    col = ['violet', 'indigo', 'b', 'lightblue', 'lightgreen', 'g', 'orange', 'coral', 'r', 'darkred']#, 'k']
-    nage = np.arange(0,len(age),1)
-    fnc  = Func(ID, PA, Zall, age, dust_model=dust_model) # Set up the number of Age/ZZ
-    bfnc = Basic(Zall)
-
-    ###########################
-    # Open result file
-    ###########################
-    # Open ascii file and stock to array.
-    lib     = fnc.open_spec_fits(ID, PA, fall=0)
-    lib_all = fnc.open_spec_fits(ID, PA, fall=1)
-
-    file = 'summary_' + ID + '.fits'
-    hdul = fits.open(file) # open a FITS file
-
-    # Redshift MC
-    zp50  = hdul[1].data['zmc'][1]
-    zp16  = hdul[1].data['zmc'][0]
-    zp84  = hdul[1].data['zmc'][2]
-
-    M50 = hdul[1].data['ms'][1]
-    M16 = hdul[1].data['ms'][0]
-    M84 = hdul[1].data['ms'][2]
-    print('Total stellar mass is %.2e'%(M50))
-
-    A50 = np.zeros(len(age), dtype='float')
-    A16 = np.zeros(len(age), dtype='float')
-    A84 = np.zeros(len(age), dtype='float')
-    for aa in range(len(age)):
-        A50[aa] = hdul[1].data['A'+str(aa)][1]
-        A16[aa] = hdul[1].data['A'+str(aa)][0]
-        A84[aa] = hdul[1].data['A'+str(aa)][2]
-
-    Asum  = np.sum(A50)
-    aa   = 0
-    Av50 = hdul[1].data['Av'+str(aa)][1]
-    Av16 = hdul[1].data['Av'+str(aa)][0]
-    Av84 = hdul[1].data['Av'+str(aa)][2]
-    Z50 = np.zeros(len(age), dtype='float')
-    Z16 = np.zeros(len(age), dtype='float')
-    Z84 = np.zeros(len(age), dtype='float')
-    NZbest = np.zeros(len(age), dtype='int')
-    for aa in range(len(age)):
-        Z50[aa] = hdul[1].data['Z'+str(aa)][1]
-        Z16[aa] = hdul[1].data['Z'+str(aa)][0]
-        Z84[aa] = hdul[1].data['Z'+str(aa)][2]
-        NZbest[aa]= bfnc.Z2NZ(Z50[aa])
-
-
-    ZZ50 = np.sum(Z50*A50)/np.sum(A50)  # Light weighted Z.
-    chi   = hdul[1].data['chi'][0]
-    chin  = hdul[1].data['chi'][1]
-    fitc  = chin
-    Cz0   = hdul[0].header['Cz0']
-    Cz1   = hdul[0].header['Cz1']
-    Cz2   = hdul[0].header['Cz2']
-    zbes  = hdul[0].header['z']
-    zscl = (1.+zbes)
-
-    # Repeat no.
-    nplot = 1000
-    #DIR_OUT = '/astro/udfcen3/Takahiro/sedfitter/corner/' + ID + '_corner/'
-    try:
-        os.makedirs(DIR_OUT)
-    except:
-        pass
-
-    # plot Configuration
-    K = 4 # No of params.
-    Par = ['$\log M_*/M_\odot$', '$\log T$/Gyr', '$A_V$/mag', '$\log Z / Z_\odot$']
-    factor = 2.0           # size of one side of one panel
-    lbdim = 0.5 * factor   # size of left/bottom margin
-    trdim = 0.2 * factor   # size of top/right margin
-    whspace = 0.02         # w/hspace size
-    plotdim = factor * K + factor * (K - 1.) * whspace
-    dim = lbdim + plotdim + trdim
-    sclfig = 0.7
-
-    # Create a new figure if one wasn't provided.
-    if fig is None:
-        fig, axes = plt.subplots(K, K, figsize=(dim*sclfig, dim*sclfig))
-    else:
-        try:
-            axes = np.array(fig.axes).reshape((K, K))
-        except:
-            raise ValueError("Provided figure has {0} axes, but data has "
-                             "dimensions K={1}".format(len(fig.axes), K))
-    # Format the figure.
-    lb = lbdim / dim
-    tr = (lbdim + plotdim) / dim
-    fig.subplots_adjust(left=lb* 1.06, bottom=lb*.9, right=tr, top=tr*.99,
-                        wspace=whspace, hspace=whspace)
-
-    # For spec plot
-    ax0 = fig.add_axes([0.62,0.61,0.37,0.33])
-
-    ###############################
-    # Data taken from
-    ###############################
-    DIR_TMP = './templates/'
-    dat = np.loadtxt(DIR_TMP + 'spec_obs_' + ID + '.cat', comments='#')
-    NR = dat[:, 0]
-    x  = dat[:, 1]
-    fy00 = dat[:, 2]
-    ey00 = dat[:, 3]
-
-    con0 = (NR<1000) #& (fy/ey>SNlim)
-    xg0  = x[con0]
-    fg0  = fy00[con0] * Cz0
-    eg0  = ey00[con0] * Cz0
-    con1 = (NR>=1000) & (NR<2000) #& (fy/ey>SNlim)
-    xg1  = x[con1]
-    fg1  = fy00[con1] * Cz1
-    eg1  = ey00[con1] * Cz1
-    con2 = (NR>=2000) & (NR<NRbb_lim) #& (fy/ey>SNlim)
-    xg2  = x[con2]
-    fg2  = fy00[con2] * Cz2
-    eg2  = ey00[con2] * Cz2
-
-    con_bb = (NR>=NRbb_lim)#& (fy/ey>SNlim)
-    xg_bb  = x[con_bb]
-    fg_bb  = fy00[con_bb]
-    eg_bb  = ey00[con_bb]
-
-    fy01 = np.append(fg0,fg1)
-    ey01 = np.append(eg0,eg1)
-    fy02 = np.append(fy01,fg2)
-    ey02 = np.append(ey01,eg2)
-
-    fy = np.append(fy02,fg_bb)
-    ey = np.append(ey02,eg_bb)
-    wht=1./np.square(ey)
-
-    dat = np.loadtxt(DIR_TMP + 'bb_obs_' + ID + '.cat', comments='#')
-    NRbb = dat[:,0]
-    xbb  = dat[:,1]
-    fybb = dat[:,2]
-    eybb = dat[:,3]
-    exbb = dat[:,4]
-    snbb = fybb/eybb
-
-    conspec = (NR<NRbb_lim) #& (fy/ey>1)
-    #ax0.plot(xg0, fg0 * c / np.square(xg0) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='royalblue', label='')
-    #ax0.plot(xg1, fg1 * c / np.square(xg1) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='#DF4E00', label='')
-    conbb = (fybb/eybb>snlimbb)
-    ax0.errorbar(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, yerr=eybb[conbb]*c/np.square(xbb[conbb])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
-    ax0.plot(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, '.r', ms=10, linestyle='', linewidth=0, zorder=4)
-
-    conbbe = (fybb/eybb<snlimbb)
-    ax0.plot(xbb[conbbe], eybb[conbbe] * c / np.square(xbb[conbbe]) / d, 'vr', ms=10, linestyle='', linewidth=0, zorder=4)
-
-    ymax = np.max(fybb[conbb] * c / np.square(xbb[conbb]) / d) * 1.10
-    ax0.set_xlabel('Observed wavelength ($\mathrm{\mu m}$)', fontsize=14)
-    ax0.set_ylabel('Flux ($\mathrm{erg}/\mathrm{s}/\mathrm{cm}^{2}/\mathrm{\AA}$)', fontsize=13)
-    ax0.set_xlim(2200, 88000)
-    #ax1.set_xlim(12500, 16000)
-    ax0.set_xscale('log')
-    ax0.set_ylim(-0.05, ymax)
-
-    DIR_TMP = './templates/'
-    ####################
-    # MCMC corner plot.
-    ####################
-    file = 'chain_' + ID + '_corner.cpkl'
-    niter = 0
-    data = loadcpkl(os.path.join('./'+file))
-
-    try:
-        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
-        burnin = data['burnin']
-        nmc    = data['niter']
-        nwalk  = data['nwalkers']
-        Nburn  = burnin #*20
-        samples = data['chain'][:]
-    except:
-        if verbose: print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
-
-    f0     = fits.open(DIR_TMP + 'ms_' + ID + '.fits')
-    sedpar = f0[1]
-
-    import matplotlib
-    import matplotlib.cm as cm
-    getcmap   = matplotlib.cm.get_cmap('jet')
-    nc        = np.arange(0, nmc, 1)
-    col = getcmap((nc-0)/(nmc-0))
-
-    #for kk in range(0,nmc,1):
-    Ntmp = np.zeros(nplot, dtype='float')
-    lmtmp= np.zeros(nplot, dtype='float')
-    Avtmp= np.zeros(nplot, dtype='float')
-    Ztmp = np.zeros(nplot, dtype='float')
-    Ttmp = np.zeros(nplot, dtype='float')
-    ACtmp= np.zeros(nplot, dtype='float')
-
-    files = [] # For movie
-    for kk in range(0,nplot,1):
-
-        #nr = kk # np.random.randint(len(samples))
-        nr = np.random.randint(len(samples))
-        Avtmp[kk] = samples['Av'][nr]
-        #Asum = 0
-        #for ss in range(len(age)):
-        #Asum += np.sum(samples['A'+str(ss)][nr])
-        II0   = nage #[0,1,2,3] # Number for templates
-        for ss in range(len(age)):
-            ii = int(len(II0) - ss - 1) # from old to young templates.
-            AA_tmp = samples['A'+str(ii)][nr]
-            try:
-                ZZ_tmp = samples['Z'+str(ii)][nr]
-            except:
-                ZZ_tmp = samples['Z0'][nr]
-            nZtmp      = bfnc.Z2NZ(ZZ_tmp)
-            mslist     = sedpar.data['ML_'+str(nZtmp)][ii]
-            lmtmp[kk] += AA_tmp * mslist
-            Ztmp[kk]  += (10 ** ZZ_tmp) * AA_tmp * mslist
-            Ttmp[kk]  += age[ii] * AA_tmp * mslist
-            ACtmp[kk] += AA_tmp * mslist
-
-            # SED
-            flim = 0.05
-            if ss == 0:
-                y0, x0   = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
-                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
-                ysump = y0p #* 1e18
-                ysum  = y0  #* 1e18
-                if AA_tmp/Asum > flim:
-                    ax0.plot(x0, y0 * c/ np.square(x0) / d, '--', lw=0.1, color=col[ii], zorder=-1, label='', alpha=0.1)
-            else:
-                y0_r, x0_tmp = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
-                y0p, x0p  = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
-                ysump += y0p  #* 1e18
-                ysum  += y0_r #* 1e18
-                if AA_tmp/Asum > flim:
-                    ax0.plot(x0, y0_r * c/ np.square(x0) / d, '--', lw=0.1, color=col[ii], zorder=-1, label='', alpha=0.1)
-        # Total
-        ax0.plot(x0, ysum * c/ np.square(x0) / d, '-', lw=0.1, color='gray', zorder=-1, label='', alpha=0.1)
-        ax0.set_xlim(2200, 88000)
-        ax0.set_xscale('log')
-        ax0.set_ylim(0., ymax)
-
-        # Convert into log
-        Ztmp[kk] /= ACtmp[kk]
-        Ttmp[kk] /= ACtmp[kk]
-        Ntmp[kk]  = kk
-
-        lmtmp[kk] = np.log10(lmtmp[kk])
-        Ztmp[kk]  = np.log10(Ztmp[kk])
-        Ttmp[kk]  = np.log10(Ttmp[kk])
-
-
-        NPAR = [lmtmp[:kk+1], Ttmp[:kk+1], Avtmp[:kk+1], Ztmp[:kk+1]]
-        NPARmin = [np.log10(M16)-0.1, -0.4, Av16-0.1, -0.5]
-        NPARmax = [np.log10(M84)+0.1, 0.5, Av84+0.1, 0.5]
-
-        #for kk in range(0,nplot,1):
-        if kk == nplot-1:
-            # Histogram
-            for i, x in enumerate(Par):
-                ax = axes[i, i]
-                x1min, x1max = NPARmin[i], NPARmax[i]
-                nbin = 50
-                binwidth1 = (x1max-x1min)/nbin
-                bins1 = np.arange(x1min, x1max + binwidth1, binwidth1)
-                ax.hist(NPAR[i], bins=bins1, orientation='vertical', color='b', histtype='stepfilled', alpha=0.6)
-                ax.set_xlim(x1min, x1max)
-                ax.set_yticklabels([])
-                if i == K-1:
-                    ax.set_xlabel('%s'%(Par[i]), fontsize=12)
-                if i < K-1:
-                    ax.set_xticklabels([])
-
-        # Scatter and contour
-        for i, x in enumerate(Par):
-            for j, y in enumerate(Par):
-                if i > j:
-                    ax = axes[i, j]
-                    ax.scatter(NPAR[j], NPAR[i], c='b', s=1, marker='o', alpha=0.01)
-                    ax.set_xlabel('%s'%(Par[j]), fontsize=12)
-
-                    x1min, x1max = NPARmin[j], NPARmax[j]
-                    y1min, y1max = NPARmin[i], NPARmax[i]
-                    ax.set_xlim(x1min, x1max)
-                    ax.set_ylim(y1min, y1max)
-
-                    if j==0:
-                        ax.set_ylabel('%s'%(Par[i]), fontsize=12)
-                    if j>0:
-                        ax.set_yticklabels([])
-                    if i<K-1:
-                        ax.set_xticklabels([])
-
-                if i < j:
-                    ax = axes[i, j]
-                    ax.set_xticklabels([])
-                    ax.set_yticklabels([])
-                    ax.set_frame_on(False)
-                    ax.set_xticks([])
-                    ax.set_yticks([])
-
-                if i == j:
-                    ax = axes[i, j]
-                    ax.set_yticklabels([])
-                    if i == K-1:
-                        ax.set_xlabel('%s'%(Par[i]), fontsize=12)
-                    if i < K-1:
-                        ax.set_xticklabels([])
-
-
-        if kk%10 == 0 and out_ind == 1:
-            fname = DIR_OUT + '%d.png' % kk
-            print('Saving frame', fname)
-            plt.savefig(fname, dpi=200)
-            files.append(fname)
-
-        #plt.savefig(DIR_OUT + '%d.pdf'%(kk))
-
-    plt.savefig(DIR_OUT + 'param_' + ID + '_corner.png', dpi=200)
-    plt.close()
-
-
-def write_lines(ID, zbes, R_grs=45, dw=4, umag=1.0, DIR_OUT='./'):
-    '''
-    '''
-    dlw = R_grs * dw # Can affect the SFR.
-    ldw = 7
-
-    ###################################
-    # To add lines in the plot,
-    # ,manually edit the following file
-    # so as Fcont50 have >0.
-    ###################################
-    flw = open(DIR_OUT + ID + '_lines_fit.txt', 'w')
-    flw.write('# LW flux_line eflux_line flux_cont EW eEW L_line eL_line\n')
-    flw.write('# (AA) (Flam_1e-18) (Flam_1e-18) (Flam_1e-18) (AA) (AA) (erg/s) (erg/s)\n')
-    flw.write('# Error in EW is 1sigma, by pm eflux_line.\n')
-    flw.write('# If EW=-99, it means gaussian fit failed.\n')
-    flw.write('# and flux is the sum of excess at WL pm %.1f AA.\n'%(dlw))
-    flw.write('# Magnification is corrected; mu=%.3f\n'%(umag))
-    try:
-        fl = np.loadtxt(DIR_OUT + 'table_' + ID + '_lines.txt', comments='#')
-        LW      = fl[:,2]
-        Fcont50 = fl[:,3]
-        Fline50 = fl[:,6]
-        for ii in range(len(LW)):
-            if Fcont50[ii] > 0:
-                WL = LW[ii] * (1.+zbes)
-                if ii == 7:
-                    contmp = (x > WL - dlw) & (x < WL + dlw*1.5)
-                else:
-                    contmp = (x > WL - dlw) & (x < WL + dlw)
-                FLW[ii] = 1
-
-                xx   = x[contmp]
-                yy   = (fy - ysum_cut)[contmp]
-                eyy  = ey[contmp]
-                yy2  = (ysum_cut)[contmp]
-
-                xyzip = zip(xx,yy,eyy,yy2)
-                xyzip = sorted(xyzip)
-
-                xxs  = np.array([p1 for p1,p2,p3,p4 in xyzip])
-                yys  = np.array([p2 for p1,p2,p3,p4 in xyzip])
-                eyys = np.array([p3 for p1,p2,p3,p4 in xyzip])
-                yy2s = np.array([p4 for p1,p2,p3,p4 in xyzip])
-
-                flux = np.zeros(len(xxs), dtype='float')
-                efl  = np.zeros(len(xxs), dtype='float')
-                for ff in range(len(xxs)):
-                    flux[ff] = yy2s[ff]/np.square(xxs[ff]) * c/d
-                    efl[ff]  = np.square(eyys[ff]/np.square(xxs[ff]) * c/d)
-
-                fmed = np.median(flux) # Median of continuum, model flux
-                esum = np.sqrt(simps(efl, xxs))
-
-                try:
-                    popt,pcov = curve_fit(gaus,xxs,yys,p0=[Fline50[ii],WL,10],sigma=eyys)
-                    xxss = xxs/zscl
-
-                    if ii == 7:
-                        popt,pcov = curve_fit(gaus,xxs,yys,p0=[Fline50[ii],WL+20,10],sigma=eyys)
-                        xxss = xxs/zscl
-
-                    if f_grsm:
-                        ax2t.plot(xxs/zscl, (gaus(xxs,*popt)+yy2s) * c/np.square(xxs)/d, '#4682b4', linestyle='-', linewidth=1, alpha=0.8, zorder=20)
-
-                    I1 = simps((gaus(xxs,*popt)) * c/np.square(xxs)/d, xxs)
-                    I2 = I1 - simps((gaus(xxs,*popt)) * c/np.square(xxs)/d, xxs)
-                    fline = I1
-
-                    Flum = fline*Cons*1e-18 # luminosity in erg/s.
-                    elum = esum *Cons*1e-18 # luminosity in erg/s.
-                    SFR  = Flum * 6.58*1e-42
-                    print('SFR is', SFR/umag)
-                    EW_tmp   = simps( ((gaus(xxs,*popt)) * c/np.square(xxs)/d)/yy2s, xxs)
-                    EW_tmp_u = simps( ((gaus(xxs,*popt) + eyys/np.sqrt(len(xxs))) * c/np.square(xxs)/d)/yy2s, xxs)
-
-                    if ii == 7:
-                        contmp2 = (xxs/zscl>4320.) & (xxs/zscl<4380.)
-                        popt,pcov = curve_fit(gaus,xxs[contmp2], yys[contmp2], p0=[Fline50[ii],WL,10], sigma=eyys[contmp2])
-
-                        I1 = simps((gaus(xxs[contmp2],*popt)) * c/np.square(xxs[contmp2])/d, xxs[contmp2])
-                        I2 = I1 - simps((gaus(xxs[contmp2],*popt)) * c/np.square(xxs[contmp2])/d, xxs[contmp2])
-                        fline = I1
-
-                        Flum = fline*Cons*1e-18 # luminosity in erg/s.
-                        elum = esum *Cons*1e-18 # luminosity in erg/s.
-                        SFR  = Flum * 6.58*1e-42
-                        print('SFR, update, is', SFR/umag)
-                        EW_tmp   = simps( ((gaus(xxs[contmp2],*popt)) * c/np.square(xxs[contmp2])/d)/yy2s[contmp2], xxs[contmp2])
-                        EW_tmp_u = simps( ((gaus(xxs[contmp2],*popt) + eyys[contmp2]/np.sqrt(len(xxs[contmp2]))) * c/np.square(xxs[contmp2])/d)/yy2s[contmp2], xxs[contmp2])
-
-                    flw.write('%d %.2f %.2f %.2f %.2f %.2f %.2e %.2e %.2f\n'%(LW[ii],fline/umag, esum/umag, fmed/umag, EW_tmp,(EW_tmp_u-EW_tmp), Flum*1e-18/umag, elum*1e-18/umag, SFR/umag))
-
-                except Exception:
-                    fsum = np.zeros(len(xxs))
-                    for ff in range(len(fsum)):
-                        fsum[ff] = (yys[ff]+yy2s[ff])/np.square(xxs[ff])
-
-                    fline = np.sum(fsum) / d*c
-                    flw.write('%d %.2f %.2f %.2f %d %d %d %d %d\n'%(LW[ii],fline,esum,fmed, -99, 0, -99, 0, 0))
-                    pass
-
-    except:
-        pass
-    flw.close()
-
-
-def plot_corner_physparam_frame(ID, PA, Zall=np.arange(-1.2,0.4249,0.05), age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0], \
-    tau0=[0.1,0.2,0.3], fig=None, dust_model=0):
-    '''
-    If you like to
-    Creat temporal png for gif image.
-    '''
-    col = ['violet', 'indigo', 'b', 'lightblue', 'lightgreen', 'g', 'orange', 'coral', 'r', 'darkred']#, 'k']
-
-    nage = np.arange(0,len(age),1)
-    fnc  = Func(ID, PA, Zall, age, dust_model=dust_model) # Set up the number of Age/ZZ
-    bfnc = Basic(Zall)
-
-    ###########################
-    # Open result file
-    ###########################
-    # Open ascii file and stock to array.
-    lib     = fnc.open_spec_fits(fall=0, tau0=tau0)
-    lib_all = fnc.open_spec_fits(fall=1, tau0=tau0)
-
-    file = 'summary_' + ID + '.fits'
-    hdul = fits.open(file) # open a FITS file
-
-    # Redshift MC
-    zp50  = hdul[1].data['zmc'][1]
-    zp16  = hdul[1].data['zmc'][0]
-    zp84  = hdul[1].data['zmc'][2]
-
-    M50 = hdul[1].data['ms'][1]
-    M16 = hdul[1].data['ms'][0]
-    M84 = hdul[1].data['ms'][2]
-    print('Total stellar mass is %.2e'%(M50))
-
-    A50 = np.zeros(len(age), dtype='float')
-    A16 = np.zeros(len(age), dtype='float')
-    A84 = np.zeros(len(age), dtype='float')
-    for aa in range(len(age)):
-        A50[aa] = hdul[1].data['A'+str(aa)][1]
-        A16[aa] = hdul[1].data['A'+str(aa)][0]
-        A84[aa] = hdul[1].data['A'+str(aa)][2]
-
-    Asum  = np.sum(A50)
-    aa   = 0
-    Av50 = hdul[1].data['Av'+str(aa)][1]
-    Av16 = hdul[1].data['Av'+str(aa)][0]
-    Av84 = hdul[1].data['Av'+str(aa)][2]
-    Z50 = np.zeros(len(age), dtype='float')
-    Z16 = np.zeros(len(age), dtype='float')
-    Z84 = np.zeros(len(age), dtype='float')
-    NZbest = np.zeros(len(age), dtype='int')
-    for aa in range(len(age)):
-        Z50[aa] = hdul[1].data['Z'+str(aa)][1]
-        Z16[aa] = hdul[1].data['Z'+str(aa)][0]
-        Z84[aa] = hdul[1].data['Z'+str(aa)][2]
-        NZbest[aa]= bfnc.Z2NZ(Z50[aa])
-
-
-    ZZ50 = np.sum(Z50*A50)/np.sum(A50)  # Light weighted Z.
-    chi   = hdul[1].data['chi'][0]
-    chin  = hdul[1].data['chi'][1]
-    fitc  = chin
-    Cz0   = hdul[0].header['Cz0']
-    Cz1   = hdul[0].header['Cz1']
-    zbes  = hdul[0].header['z']
-    zscl = (1.+zbes)
-
-
-    # Repeat no.
-    nplot = 1000
-
-    DIR_OUT = '/astro/udfcen3/Takahiro/sedfitter/corner/' + ID + '_corner/'
-    try:
-        os.makedirs(DIR_OUT)
-    except:
-        pass
-
-    # plot Configuration
-    K = 4 # No of params.
-    Par = ['$\log M_*/M_\odot$', '$\log T_*$/Gyr', '$A_V$/mag', '$\log Z_* / Z_\odot$']
-    factor = 2.0           # size of one side of one panel
-    lbdim = 0.5 * factor   # size of left/bottom margin
-    trdim = 0.2 * factor   # size of top/right margin
-    whspace = 0.02         # w/hspace size
-    plotdim = factor * K + factor * (K - 1.) * whspace
-    dim = lbdim + plotdim + trdim
-    sclfig = 0.7
-
-    # Create a new figure if one wasn't provided.
-    ###############################
-    # Data taken from
-    ###############################
-    DIR_TMP = './templates/'
-    dat = np.loadtxt(DIR_TMP + 'spec_obs_' + ID + '.cat', comments='#')
-    NR = dat[:, 0]
-    x  = dat[:, 1]
-    fy00 = dat[:, 2]
-    ey00 = dat[:, 3]
-
-    con0 = (NR<1000) #& (fy/ey>SNlim)
-    xg0  = x[con0]
-    fg0  = fy00[con0] * Cz0
-    eg0  = ey00[con0] * Cz0
-    con1 = (NR>=1000) & (NR<10000) #& (fy/ey>SNlim)
-    xg1  = x[con1]
-    fg1  = fy00[con1] * Cz1
-    eg1  = ey00[con1] * Cz1
-    con2 = (NR>=10000)#& (fy/ey>SNlim)
-    xg2  = x[con2]
-    fg2  = fy00[con2]
-    eg2  = ey00[con2]
-    fy01 = np.append(fg0,fg1)
-    fy   = np.append(fy01,fg2)
-    ey01 = np.append(eg0,eg1)
-    ey   = np.append(ey01,eg2)
-    wht=1./np.square(ey)
-
-    dat = np.loadtxt(DIR_TMP + 'bb_obs_' + ID + '.cat', comments='#')
-    NRbb = dat[:, 0]
-    xbb  = dat[:, 1]
-    fybb = dat[:, 2]
-    eybb = dat[:, 3]
-    exbb = dat[:, 4]
-    snbb = fybb/eybb
-
-    conspec = (NR<10000) #& (fy/ey>1)
-    #ax0.plot(xg0, fg0 * c / np.square(xg0) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='royalblue', label='')
-    #ax0.plot(xg1, fg1 * c / np.square(xg1) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='#DF4E00', label='')
-    conbb = (fybb/eybb>1)
-
-    DIR_TMP = './templates/'
-    ####################
-    # MCMC corner plot.
-    ####################
-    file = 'chain_' + ID + '_corner.cpkl'
-    niter = 0
-    data = loadcpkl(os.path.join('./'+file))
-
-    try:
-        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
-        burnin = data['burnin']
-        nmc    = data['niter']
-        nwalk  = data['nwalkers']
-        Nburn  = burnin #*20
-        samples = data['chain'][:]
-    except:
-        if verbose: print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
-
-    f0     = fits.open(DIR_TMP + 'ms_' + ID + '.fits')
-    sedpar = f0[1]
-
-    import matplotlib
-    import matplotlib.cm as cm
-    getcmap   = matplotlib.cm.get_cmap('jet')
-    nc        = np.arange(0, nmc, 1)
-    col = getcmap((nc-0)/(nmc-0))
-
-    #for kk in range(0,nmc,1):
-    Ntmp = np.zeros(nplot, dtype='float')
-    lmtmp= np.zeros(nplot, dtype='float')
-    Avtmp= np.zeros(nplot, dtype='float')
-    Ztmp = np.zeros(nplot, dtype='float')
-    Ttmp = np.zeros(nplot, dtype='float')
-    ACtmp= np.zeros(nplot, dtype='float')
-
-
-    # Time bin
-    Txmax = 4 # Max x value
-    Tuni = MB.cosmo.age(zbes).value
-    Tuni0 = (Tuni - age[:])
-    delT  = np.zeros(len(age),dtype='float')
-    delTl = np.zeros(len(age),dtype='float')
-    delTu = np.zeros(len(age),dtype='float')
-    for aa in range(len(age)):
-        if aa == 0:
-            delTl[aa] = age[aa]
-            delTu[aa] = (age[aa+1]-age[aa])/2.
-            delT[aa]  = delTu[aa] + delTl[aa]
-        elif Tuni < age[aa]:
-            delTl[aa] = (age[aa]-age[aa-1])/2.
-            delTu[aa] = 10.
-            delT[aa]  = delTu[aa] + delTl[aa]
-        elif aa == len(age)-1:
-            delTl[aa] = (age[aa]-age[aa-1])/2.
-            delTu[aa] = Tuni - age[aa]
-            delT[aa]  = delTu[aa] + delTl[aa]
-        else:
-            delTl[aa] = (age[aa]-age[aa-1])/2.
-            delTu[aa] = (age[aa+1]-age[aa])/2.
-            delT[aa]  = delTu[aa] + delTl[aa]
-
-    delT[:]  *= 1e9 # Gyr to yr
-    delTl[:] *= 1e9 # Gyr to yr
-    delTu[:] *= 1e9 # Gyr to yr
-
-    ######
-    files = [] # For movie
-    SFmax = 0
-    Tsmin = 0
-    Tsmax = 0
-    Zsmin = 0
-    Zsmax = 0
-    AMtmp = 0
-    AMtmp16 = 0
-    AMtmp84 = 0
-    for ii in range(len(age)):
-        ZZ_tmp = Z50[ii] #samples['Z'+str(ii)][100]
-        ZZ_tmp16 = Z16[ii] #samples['Z'+str(ii)][100]
-        ZZ_tmp84 = Z84[ii] #samples['Z'+str(ii)][100]
-        AA_tmp = np.max(samples['A'+str(ii)][:])
-        AA_tmp84 = np.percentile(samples['A'+str(ii)][:],95)
-        AA_tmp16 = np.percentile(samples['A'+str(ii)][:],5)
-        #AA_tmp84 = A84[ii]
-        #AA_tmp16 = A16[ii]
-        nZtmp  = bfnc.Z2NZ(ZZ_tmp)
-        mslist = sedpar.data['ML_'+str(nZtmp)][ii]
-        AMtmp16 += mslist*AA_tmp16
-        AMtmp84 += mslist*AA_tmp84
-
-        Tsmax += age[ii] * AA_tmp84 * mslist
-        Tsmin += age[ii] * AA_tmp16 * mslist
-
-        Zsmax += 10**ZZ_tmp84 * AA_tmp84 * mslist
-        Zsmin += 10**ZZ_tmp16 * AA_tmp16 * mslist
-
-        SFtmp  = AA_tmp * mslist / delT[ii]
-        if SFtmp > SFmax:
-            SFmax = SFtmp
-
-    #NPARmin = [np.log10(M16)-0.1, -0.4, 0, -0.6]
-    #NPARmax = [np.log10(M84)+0.1, 0.5, 2., 0.5]
-    NPARmin = [np.log10(M16)-0.1, np.log10(Tsmin/AMtmp16)-0.1, Av16-0.1, np.log10(Zsmin/AMtmp16)-0.2]
-    NPARmax = [np.log10(M84)+0.1, np.log10(Tsmax/AMtmp84)+0.2, Av84+0.1, np.log10(Zsmax/AMtmp84)+0.2]
-
-    for kk in range(0,nplot,1):
-        if kk%10 == 0:
-            #print('New plot; %d'%kk)
-            fig, axes = plt.subplots(K, K, figsize=(dim*sclfig*2, dim*sclfig))
-            # Format the figure.
-            lb = lbdim / dim
-            tr = (lbdim + plotdim) / dim
-            #fig.subplots_adjust(left=lb*1.06, bottom=lb*.9, right=tr, top=tr*.99,
-            fig.subplots_adjust(left=0.5, bottom=lb*.9, right=tr, top=tr*.99,
-                                wspace=whspace, hspace=whspace)
-
-            # For spec plot
-            ax0 = fig.add_axes([0.05,0.73,0.37,0.23])
-            ax1 = fig.add_axes([0.05,0.40,0.37,0.23])
-            ax2 = fig.add_axes([0.05,0.07,0.37,0.23])
-
-            ax0.errorbar(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, yerr=eybb[conbb]*c/np.square(xbb[conbb])/d, color='k', linestyle='', linewidth=0.5, zorder=4)
-            ax0.plot(xbb[conbb], fybb[conbb] * c / np.square(xbb[conbb]) / d, '.r', ms=10, linestyle='', linewidth=0, zorder=4)#, label='Obs.(BB)')
-            ax0.plot(xg0, fg0 * c / np.square(xg0) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='royalblue', label='')
-            ax0.plot(xg1, fg1 * c / np.square(xg1) / d, marker='', linestyle='-', linewidth=0.5, ms=0.1, color='#DF4E00', label='')
-
-
-        #nr = kk # np.random.randint(len(samples))
-        nr = np.random.randint(len(samples))
-        Avtmp[kk] = samples['Av'][nr]
-        #Asum = 0
-        #for ss in range(len(age)):
-        #Asum += np.sum(samples['A'+str(ss)][nr])
-        ZMM = np.zeros((len(age)), dtype='float') # Mass weighted Z.
-        ZM  = np.zeros((len(age)), dtype='float') # Light weighted T.
-        ZC  = np.zeros((len(age)), dtype='float') # Light weighted T.
-        SF  = np.zeros((len(age)), dtype='float') # SFR
-        AM  = np.zeros((len(age)), dtype='float') # Light weighted T.
-
-
-        II0   = nage #[0,1,2,3] # Number for templates
-        for ss in range(len(age)):
-            ii = int(len(II0) - ss - 1) # from old to young templates.
-            AA_tmp = samples['A'+str(ii)][nr]
-            try:
-                ZZ_tmp = samples['Z'+str(ii)][nr]
-            except:
-                ZZ_tmp = samples['Z0'][nr]
-
-            nZtmp      = bfnc.Z2NZ(ZZ_tmp)
-            mslist     = sedpar.data['ML_'+str(nZtmp)][ii]
-            lmtmp[kk] += AA_tmp * mslist
-            Ztmp[kk]  += (10 ** ZZ_tmp) * AA_tmp * mslist
-            Ttmp[kk]  += age[ii] * AA_tmp * mslist
-            ACtmp[kk] += AA_tmp * mslist
-
-            AM[ii] = AA_tmp * mslist
-            SF[ii] = AA_tmp * mslist / delT[ii]
-            ZM[ii] = ZZ_tmp # AAtmp[aa] * mslist[aa]
-            ZMM[ii]= (10 ** ZZ_tmp) * AA_tmp * mslist
-
-            # SED
-            flim = 0.05
-            if ss == 0:
-                y0, x0   = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
-                y0p, x0p = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
-                ysump = y0p #* 1e18
-                ysum  = y0  #* 1e18
-                if AA_tmp/Asum > flim:
-                    ax0.plot(x0, y0 * c/ np.square(x0) / d, '--', lw=1, color=col[ii], zorder=-1, label='', alpha=0.5)
-                #ax1.plot(age[ii], SF[ii], marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
-                #ax1.errorbar(age[ii], SF[ii], xerr=[[delTl[ii]/1e9], [delTu[ii]/1e9]], ms=10, marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
-                xx1 = np.arange(age[ii]-delTl[ii]/1e9, age[ii]+delTu[ii]/1e9, 0.01)
-                ax1.fill_between(xx1, xx1*0, xx1*0+SF[ii], lw=1, facecolor=col[ii], zorder=1, label='', alpha=0.5)
-                #ax2.plot(age[ii], ZM[ii], marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
-            else:
-                y0_r, x0_tmp = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib_all, tau0=tau0)
-                y0p, x0p     = fnc.get_template_single(AA_tmp, Avtmp[kk], ii, ZZ_tmp, zbes, lib, tau0=tau0)
-                ysump += y0p  #* 1e18
-                ysum  += y0_r #* 1e18
-                if AA_tmp/Asum > flim:
-                    ax0.plot(x0, y0_r * c/ np.square(x0) / d, '--', lw=1, color=col[ii], zorder=-1, label='', alpha=0.5)
-                #ax1.plot(age[ii], SF[ii], marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
-                #ax1.errorbar(age[ii], SF[ii], xerr=[[delTl[ii]/1e9], [delTu[ii]/1e9]], ms=10, marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
-                xx1 = np.arange(age[ii]-delTl[ii]/1e9, age[ii]+delTu[ii]/1e9, 0.01)
-                ax1.fill_between(xx1, xx1*0, xx1*0+SF[ii], lw=1, facecolor=col[ii], zorder=1, label='', alpha=0.5)
-                #ax2.plot(age[ii], ZM[ii], marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
-
-        for ss in range(len(age)):
-            #ii = int(len(II0) - ss - 1) # from old to young templates.
-            ii = ss # from old to young templates.
-            AC = np.sum(AM[ss:])
-            ZC[ss] = np.log10(np.sum(ZMM[ss:])/AC)
-            #ax2.errorbar(age[ii], ZC[ii], xerr=[[delTl[ii]/1e9], [delTu[ii]/1e9]], ms=10*(SF[ii]/np.sum(SF[:]))+1, marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
-            ax2.errorbar(age[ii], ZC[ii], xerr=[[delTl[ii]/1e9], [delTu[ii]/1e9]], ms=10*(AC/np.sum(AM[:]))+1, marker='o', lw=1, color=col[ii], zorder=1, label='', alpha=0.5)
-
-        # Total
-        ymax = np.max(fybb[conbb] * c / np.square(xbb[conbb]) / d) * 1.10
-        ax0.plot(x0, ysum * c/ np.square(x0) / d, '-', lw=1., color='gray', zorder=-1, label='', alpha=0.8)
-        ax0.set_xlim(2200, 88000)
-        ax0.set_xscale('log')
-        ax0.set_ylim(0., ymax)
-        ax0.set_xlabel('Observed wavelength ($\mathrm{\mu m}$)', fontsize=14)
-        ax0.set_ylabel('Flux ($\mathrm{erg}/\mathrm{s}/\mathrm{cm}^{2}/\mathrm{\AA}$)', fontsize=13)
-
-
-        ax1.plot(age[:], SF[:], marker='', linestyle='-', lw=1, color='k', zorder=-1, label='', alpha=0.5)
-        ax1.set_xlabel('$t$ (Gyr)', fontsize=12)
-        ax1.set_ylabel('$\log \dot{M_*}/M_\odot$yr$^{-1}$', fontsize=12)
-        ax1.set_xlim(0.008, Txmax)
-        ax1.set_ylim(0, SFmax)
-        ax1.set_xscale('log')
-
-
-        ax2.plot(age[:], ZC[:], marker='', linestyle='-', lw=1, color='k', zorder=-1, label='', alpha=0.5)
-        ax2.set_xlabel('$t$ (Gyr)', fontsize=12)
-        ax2.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
-        ax2.set_xlim(0.008, Txmax)
-        #ax2.set_ylim(NPARmin[3], NPARmax[3])
-        ax2.set_ylim(-0.6, 0.5)
-        ax2.set_xscale('log')
-        #ax2.yaxis.labelpad = -5
-
-        # For redshift
-        if zbes<2:
-            zred  = [zbes, 2, 3, 6]
-            #zredl = ['$z$', 2, 3, 6]
-            zredl = ['$z$', 2, 3, 6]
-        elif zbes<2.5:
-            zred  = [zbes, 2.5, 3, 6]
-            zredl = ['$z$', 2.5, 3, 6]
-        elif zbes<3.:
-            zred  = [zbes, 3, 6]
-            zredl = ['$z$', 3, 6]
-        elif zbes<6:
-            zred  = [zbes, 6]
-            zredl = ['$z$', 6]
-
-        Tzz   = np.zeros(len(zred), dtype='float')
-        for zz in range(len(zred)):
-            Tzz[zz] = (Tuni - MB.cosmo.age(zbes).value)
-            if Tzz[zz] < 0.01:
-                Tzz[zz] = 0.01
-
-
-        ax1t = ax1.twiny()
-        ax2t = ax2.twiny()
-        ax1t.set_xlim(0.008, Txmax)
-        ax1t.set_xscale('log')
-        ax1t.set_xticklabels(zredl[:])
-        ax1t.set_xticks(Tzz[:])
-        ax1t.tick_params(axis='x', labelcolor='k')
-        ax1t.xaxis.set_ticks_position('none')
-        ax1.plot(Tzz, Tzz*0+SFmax, marker='|', color='k', ms=3, linestyle='None')
-
-        ax2t.set_xlim(0.008, Txmax)
-        ax2t.set_xscale('log')
-        ax2t.set_xticklabels(zredl[:])
-        ax2t.set_xticks(Tzz[:])
-        ax2t.tick_params(axis='x', labelcolor='k')
-        ax2t.xaxis.set_ticks_position('none')
-        ax2.plot(Tzz, Tzz*0+0.5, marker='|', color='k', ms=3, linestyle='None')
-
-        # Convert into log
-        Ztmp[kk] /= ACtmp[kk]
-        Ttmp[kk] /= ACtmp[kk]
-        Ntmp[kk]  = kk
-
-        lmtmp[kk] = np.log10(lmtmp[kk])
-        Ztmp[kk]  = np.log10(Ztmp[kk])
-        Ttmp[kk]  = np.log10(Ttmp[kk])
-
-        NPAR    = [lmtmp[kk], Ttmp[kk], Avtmp[kk], Ztmp[kk]]
-
-        # Scatter and contour
-        for i, x in enumerate(Par):
-            for j, y in enumerate(Par):
-                #print(i,j,Par[j], Par[i])
-                if i > j:
-                    ax = axes[i, j]
-                    ax.scatter(NPAR[j], NPAR[i], c='b', s=10, marker='o', alpha=0.5)
-                    ax.set_xlabel('%s'%(Par[j]), fontsize=12)
-
-                    #x1min, x1max = np.min(NPAR[j]), np.max(NPAR[j])
-                    #y1min, y1max = np.min(NPAR[i]), np.max(NPAR[i])
-                    x1min, x1max = NPARmin[j], NPARmax[j]
-                    y1min, y1max = NPARmin[i], NPARmax[i]
-                    ax.set_xlim(x1min, x1max)
-                    ax.set_ylim(y1min, y1max)
-
-                    if j==0:
-                        ax.set_ylabel('%s'%(Par[i]), fontsize=12)
-                    if j>0:
-                        ax.set_yticklabels([])
-                    if i<K-1:
-                        ax.set_xticklabels([])
-                    if i == 2:
-                        ax.yaxis.labelpad = 5.
-
-                if i < j:
-                    ax = axes[i, j]
-                    ax.set_xticklabels([])
-                    ax.set_yticklabels([])
-                    ax.set_frame_on(False)
-                    ax.set_xticks([])
-                    ax.set_yticks([])
-
-                if i == j:
-                    ax = axes[i, j]
-                    ax.set_xticklabels([])
-                    ax.set_yticklabels([])
-                    ax.set_frame_on(False)
-                    ax.set_xticks([])
-                    ax.set_yticks([])
-
-                    '''
-                    ax.set_yticklabels([])
-                    if i == K-1:
-                        ax.set_xlabel('%s'%(Par[i]), fontsize=12)
-                    if i < K-1:
-                        ax.set_xticklabels([])
-                    '''
-
-        if kk%10 == 0:
-            fname = DIR_OUT + '%d.png' % (kk)
-            #fname = DIR_OUT + '%d.png' % (kk+1)
-            print('Saving frame', fname)
-            plt.savefig(fname, dpi=150)
-            #files.append(fname)
-            plt.close()
diff --git a/build/lib/gsf/plot_sfh.py b/build/lib/gsf/plot_sfh.py
deleted file mode 100644
index 026c23e..0000000
--- a/build/lib/gsf/plot_sfh.py
+++ /dev/null
@@ -1,2004 +0,0 @@
-import numpy as np
-import sys
-import asdf
-import matplotlib.pyplot as plt
-from numpy import log10
-from scipy.integrate import simps
-import os
-import time
-from matplotlib.ticker import FormatStrFormatter
-import matplotlib.ticker as ticker
-
-from astropy.io import fits
-
-# Custom modules
-from .function import *
-from .function_class import Func
-from .basic_func import Basic
-from .function_igm import *
-
-
-def plot_sfh(MB, flim=0.01, lsfrl=-3, mmax=1000, Txmin=0.08, Txmax=4, lmmin=5, fil_path='./FILT/',
-    dust_model=0, f_SFMS=False, f_symbol=True, verbose=False, f_silence=True, DIR_TMP=None,
-    f_log_sfh=True, dpi=250, TMIN=0.0001, tau_lim=0.01, skip_zhist=False, tset_SFR_SED=0.1, f_axis_force=True):
-    '''
-    Purpose
-    -------
-    Star formation history plot.
-
-    Parametes
-    ---------
-    flim : float
-        Lower limit for plotting an age bin.
-    lsfrl : float
-        Lower limit for SFR, in logMsun/yr
-    f_SFMS : bool
-        If true, plot SFR of the main sequence of a ginen stellar mass at each lookback time.
-    tset_SFR_SED : float
-        in Gyr. Time scale over which SFR estimate is averaged.
-    '''
-    if f_silence:
-        import matplotlib
-        matplotlib.use("Agg")
-
-    print('\n### Running plot_sfh ###\n')
-    fnc = MB.fnc
-    bfnc = MB.bfnc
-    ID = MB.ID
-    Z = MB.Zall
-    age = MB.age
-    nage = MB.nage
-    tau0 = MB.tau0
-    age = np.asarray(age)
-
-    try:
-        if not MB.ZFIX == None:
-            skip_zhist = True
-    except:
-        pass
-
-    if Txmin > np.min(age):
-        Txmin = np.min(age) * 0.8
-
-    NUM_COLORS = len(age)
-    cm = plt.get_cmap('gist_rainbow_r')
-    col = np.atleast_2d([cm(1.*i/NUM_COLORS) for i in range(NUM_COLORS)])
-
-    ################
-    # RF colors.
-    home = os.path.expanduser('~')
-    c = MB.c
-    chimax = 1.
-    m0set = MB.m0set
-    Mpc_cm = MB.Mpc_cm
-    d = MB.d
-
-    #############
-    # Plot.
-    #############
-    if f_log_sfh:
-        fig = plt.figure(figsize=(8,2.8))
-        fig.subplots_adjust(top=0.88, bottom=0.18, left=0.07, right=0.99, hspace=0.15, wspace=0.3)
-    else:
-        fig = plt.figure(figsize=(8.2,2.8))
-        fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
-
-    if skip_zhist:
-        if f_log_sfh:
-            fig = plt.figure(figsize=(5.5,2.8))
-            fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
-        else:
-            fig = plt.figure(figsize=(6.2,2.8))
-            fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
-        ax1 = fig.add_subplot(121)
-        ax2 = fig.add_subplot(122)
-    else:
-        ax1 = fig.add_subplot(131)
-        ax2 = fig.add_subplot(132)
-        ax4 = fig.add_subplot(133)
-        ax4t = ax4.twiny()
-
-    ax1t = ax1.twiny()
-    ax2t = ax2.twiny()
-
-    ##################
-    # Fitting Results
-    ##################
-    SNlim = 3 # avobe which SN line is shown.
-
-    ###########################
-    # Open result file
-    ###########################
-    file = MB.DIR_OUT + 'summary_' + ID + '.fits'
-    hdul = fits.open(file) # open a FITS file
-    try:
-        zbes = hdul[0].header['zmc']
-    except:
-        zbes = hdul[0].header['z']
-    chinu= hdul[1].data['chi']
-
-    try:
-        RA   = hdul[0].header['RA']
-        DEC  = hdul[0].header['DEC']
-    except:
-        RA  = 0
-        DEC = 0
-    try:
-        SN = hdul[0].header['SN']
-    except:
-        ###########################
-        # Get SN of Spectra
-        ###########################
-        file = os.path.join(DIR_TMP, 'spec_obs_' + ID + '.cat')
-        fds  = np.loadtxt(file, comments='#')
-        nrs  = fds[:,0]
-        lams = fds[:,1]
-        fsp  = fds[:,2]
-        esp  = fds[:,3]
-
-        consp = (nrs<10000) & (lams/(1.+zbes)>3600) & (lams/(1.+zbes)<4200)
-        if len((fsp/esp)[consp]>10):
-            SN = np.median((fsp/esp)[consp])
-        else:
-            SN = 1
-
-    Asum = 0
-    A50 = np.arange(len(age), dtype='float')
-    for aa in range(len(A50)):
-        A50[aa] = 10**hdul[1].data['A'+str(aa)][1]
-        Asum += A50[aa]
-
-    ####################
-    # For cosmology
-    ####################
-    DL = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm # Luminositydistance in cm
-    Cons = (4.*np.pi*DL**2/(1.+zbes))
-
-    Tuni = MB.cosmo.age(zbes).value #, use_flat=True, **cosmo)
-    Tuni0 = (Tuni - age[:])
-
-    delT  = np.zeros(len(age),dtype='float')
-    delTl = np.zeros(len(age),dtype='float')
-    delTu = np.zeros(len(age),dtype='float')
-
-    if len(age) == 1:
-        for aa in range(len(age)):
-            try:
-                tau_ssp = float(MB.inputs['TAU_SSP'])
-            except:
-                tau_ssp = tau_lim
-            delTl[aa] = tau_ssp/2.
-            delTu[aa] = tau_ssp/2.
-            if age[aa] < tau_lim:
-                # This is because fsps has the minimum tau = tau_lim
-                delT[aa] = tau_lim
-            else:
-                delT[aa] = delTu[aa] + delTl[aa]
-    else: # This is only true when CSP...
-        for aa in range(len(age)):
-            if aa == 0:
-                delTl[aa] = age[aa]
-                delTu[aa] = (age[aa+1]-age[aa])/2.
-                delT[aa] = delTu[aa] + delTl[aa]
-            elif Tuni < age[aa]:
-                delTl[aa] = (age[aa]-age[aa-1])/2.
-                delTu[aa] = Tuni-age[aa] #delTl[aa] #10.
-                delT[aa]  = delTu[aa] + delTl[aa]
-            elif aa == len(age)-1:
-                delTl[aa] = (age[aa]-age[aa-1])/2.
-                delTu[aa] = Tuni - age[aa]
-                delT[aa]  = delTu[aa] + delTl[aa]
-            else:
-                delTl[aa] = (age[aa]-age[aa-1])/2.
-                delTu[aa] = (age[aa+1]-age[aa])/2.
-                if age[aa]+delTu[aa]>Tuni:
-                    delTu[aa] = Tuni-age[aa]
-                delT[aa] = delTu[aa] + delTl[aa]
-
-            if delTu[aa]<0:
-                delTu[aa] = 1e3
-
-    con_delt = (delT<=0)
-    delT[con_delt] = 1e10
-    delT[:] *= 1e9 # Gyr to yr
-    delTl[:] *= 1e9 # Gyr to yr
-    delTu[:] *= 1e9 # Gyr to yr
-
-    ##############################
-    # Load Pickle
-    ##############################
-    samplepath = MB.DIR_OUT 
-    pfile = 'chain_' + ID + '_corner.cpkl'
-
-    niter = 0
-    data = loadcpkl(os.path.join(samplepath+'/'+pfile))
-    try:
-        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
-        burnin = data['burnin']
-        nmc    = data['niter']
-        nwalk  = data['nwalkers']
-        Nburn  = burnin
-        samples = data['chain'][:]
-    except:
-        print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
-        return -1
-
-    ######################
-    # Mass-to-Light ratio.
-    ######################
-    AM = np.zeros((len(age), mmax), dtype='float') # Mass in each bin.
-    AC = np.zeros((len(age), mmax), dtype='float') -99 # Cumulative mass in each bin.
-    AL = np.zeros((len(age), mmax), dtype='float') # Cumulative light in each bin.
-    ZM = np.zeros((len(age), mmax), dtype='float') # Z.
-    ZC = np.zeros((len(age), mmax), dtype='float') -99 # Cumulative Z.
-    ZL = np.zeros((len(age), mmax), dtype='float') -99 # Light weighted cumulative Z.
-    TC = np.zeros((len(age), mmax), dtype='float') # Mass weighted T.
-    TL = np.zeros((len(age), mmax), dtype='float') # Light weighted T.
-    ZMM= np.zeros((len(age), mmax), dtype='float') # Mass weighted Z.
-    ZML= np.zeros((len(age), mmax), dtype='float') # Light weighted Z.
-    SF = np.zeros((len(age), mmax), dtype='float') # SFR
-    Av = np.zeros(mmax, dtype='float') # SFR
-
-
-    # ##############################
-    # Add simulated scatter in quad
-    # if files are available.
-    # ##############################
-    try:
-        f_zev = int(MB.inputs['ZEVOL'])
-    except:
-        f_zev = 1
-
-    eZ_mean = 0
-    try:
-        meanfile = './sim_SFH_mean.cat'
-        dfile = np.loadtxt(meanfile, comments='#')
-        eA = dfile[:,2]
-        eZ = dfile[:,4]
-        eAv= np.mean(dfile[:,6])
-        if f_zev == 0:
-            eZ_mean = np.mean(eZ[:])
-            eZ[:] = age * 0 #+ eZ_mean
-        else:
-            try:
-                f_zev = int(prihdr['ZEVOL'])
-                if f_zev == 0:
-                    eZ_mean = np.mean(eZ[:])
-                    eZ = age * 0
-            except:
-                pass
-    except:
-        if verbose:
-            print('No simulation file (%s).\nError may be underestimated.' % meanfile)
-        eA = age * 0
-        eZ = age * 0
-        eAv = 0
-
-    mm = 0
-
-    #####################
-    # Get SED based SFR
-    #####################
-    f_SFRSED_plot = False
-    SFR_SED = np.zeros(mmax,dtype='float')
-
-    # ASDF;
-    af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
-    af0 = asdf.open(MB.DIR_TMP + 'spec_all.asdf')
-    sedpar = af['ML'] # For M/L
-    sedpar0 = af0['ML'] # For mass loss frac.
-
-    AAtmp = np.zeros(len(age), dtype='float')
-    ZZtmp = np.zeros(len(age), dtype='float')
-    mslist= np.zeros(len(age), dtype='float')
-
-    for mm in range(mmax):
-        delt_tot = 0
-        mtmp  = np.random.randint(len(samples))# + Nburn
-
-        try:
-            Av_tmp = samples['Av'][mtmp]
-        except:
-            Av_tmp = MB.AVFIX
-
-        Avrand = np.random.uniform(-eAv, eAv)
-        if Av_tmp + Avrand<0:
-            Av[mm] = 0
-        else:
-            Av[mm] = Av_tmp + Avrand
-
-        for aa in range(len(age)):
-            try:
-                # This is in log.
-                AAtmp[aa] = samples['A'+str(aa)][mtmp]
-            except:
-                AAtmp[aa] = -10
-                pass
-
-            try:
-                ZZtmp[aa] = samples['Z'+str(aa)][mtmp]
-            except:
-                try:
-                    ZZtmp[aa] = samples['Z0'][mtmp]
-                except:
-                    ZZtmp[aa] = MB.ZFIX
-
-            nZtmp = bfnc.Z2NZ(ZZtmp[aa])
-            mslist[aa] = sedpar['ML_'+str(nZtmp)][aa]
-            Arand = np.random.uniform(-eA[aa],eA[aa])
-            Zrand = np.random.uniform(-eZ[aa],eZ[aa])
-            f_m_sur = sedpar0['frac_mass_survive_%d'%nZtmp][aa]
-
-            if True:# Age limit here?
-                # quantity in log scale;
-                AM[aa, mm] = AAtmp[aa] + np.log10(mslist[aa]) + Arand 
-                AL[aa, mm] = AM[aa,mm] - np.log10(mslist[aa])
-                SF[aa, mm] = AAtmp[aa] + np.log10(mslist[aa] / delT[aa] / f_m_sur) + Arand # / ml
-                ZM[aa, mm] = ZZtmp[aa] + Zrand
-                ZMM[aa, mm]= ZZtmp[aa] + AAtmp[aa] + np.log10(mslist[aa]) + Zrand
-                ZML[aa, mm]= ZMM[aa,mm] - np.log10(mslist[aa])
-            if SF[aa, mm] < -10:
-                SF[aa, mm] = np.nan
-
-            # SFR from SED. This will be converted in log later;
-            if age[aa]<=tset_SFR_SED:
-                SFR_SED[mm] += 10**SF[aa, mm] * delT[aa]
-                delt_tot += delT[aa]
-
-        SFR_SED[mm] /= delt_tot
-        if SFR_SED[mm] > 0:
-            SFR_SED[mm] = np.log10(SFR_SED[mm])
-        else:
-            SFR_SED[mm] = -99
-
-        for aa in range(len(age)):
-
-            if Tuni0[aa]<0:
-                continue
-
-            if np.sum(10**AM[aa:,mm])>0:
-                AC[aa, mm] = np.log10(np.sum(10**AM[aa:,mm]))
-                ZC[aa, mm] = np.log10(np.sum(10**ZMM[aa:,mm])/10**AC[aa, mm])
-            if np.sum(10**AL[aa:,mm])>0:
-                ZL[aa, mm] = np.log10(np.sum(10**ZML[aa:,mm])/np.sum(10**AL[aa:,mm]))
-            if f_zev == 0: # To avoid random fluctuation in A.
-                ZC[aa,mm] = ZM[aa,mm]
-
-            ACs = 0
-            ALs = 0
-            for bb in range(aa, len(age), 1):
-
-                if Tuni0[bb]<0:
-                    continue
-
-                tmpAA = 10**np.random.uniform(-eA[bb],eA[bb])
-                tmpTT = np.random.uniform(-delT[bb]/1e9/2.,delT[bb]/1e9/2.)
-
-                TC[aa, mm] += (age[bb]+tmpTT) * 10**AAtmp[bb] * mslist[bb] * tmpAA
-                TL[aa, mm] += (age[bb]+tmpTT) * 10**AAtmp[bb] * tmpAA
-                ACs += 10**AAtmp[bb] * mslist[bb] * tmpAA
-                ALs += 10**AAtmp[bb] * tmpAA
-
-            TC[aa, mm] /= ACs
-            TL[aa, mm] /= ALs
-
-            if TC[aa, mm]>0:
-                TC[aa, mm] = np.log10(TC[aa, mm])
-            else:
-                TC[aa, mm] = np.nan
-
-            if TL[aa, mm]>0:
-                TL[aa, mm] = np.log10(TL[aa, mm])
-            else:
-                TL[aa, mm] = np.nan
-
-        # Do stuff...
-        time.sleep(0.01)
-        # Update Progress Bar
-        printProgressBar(mm, mmax, prefix = 'Progress:', suffix = 'Complete', length = 40)
-
-    Avtmp = np.percentile(Av[:],[16,50,84])
-
-    #############
-    # Plot
-    #############
-    AMp = np.zeros((len(age),3), dtype='float')
-    ACp = np.zeros((len(age),3), dtype='float')
-    ZMp = np.zeros((len(age),3), dtype='float')
-    ZCp = np.zeros((len(age),3), dtype='float')
-    ZLp = np.zeros((len(age),3), dtype='float')
-    SFp = np.zeros((len(age),3), dtype='float')
-    for aa in range(len(age)):
-       AMp[aa,:] = np.nanpercentile(AM[aa,:], [16,50,84])
-       ACp[aa,:] = np.nanpercentile(AC[aa,:], [16,50,84])
-       ZMp[aa,:] = np.nanpercentile(ZM[aa,:], [16,50,84])
-       ZCp[aa,:] = np.nanpercentile(ZC[aa,:], [16,50,84])
-       ZLp[aa,:] = np.nanpercentile(ZL[aa,:], [16,50,84])
-       SFp[aa,:] = np.nanpercentile(SF[aa,:], [16,50,84])
-
-    SFR_SED_med = np.nanpercentile(SFR_SED[:],[16,50,84])
-    if f_SFRSED_plot:
-        ax1.errorbar(delt_tot/2./1e9, SFR_SED_med[1], xerr=[[delt_tot/2./1e9],[delt_tot/2./1e9]], \
-        yerr=[[SFR_SED_med[1]-SFR_SED_med[0]],[SFR_SED_med[2]-SFR_SED_med[1]]], \
-        linestyle='', color='orange', lw=1., marker='*',ms=8,zorder=-2)
-
-    ###################
-    msize = np.zeros(len(age), dtype='float')
-    for aa in range(len(age)):
-        if A50[aa]/Asum>flim: # if >1%
-            msize[aa] = 200 * A50[aa]/Asum
-
-    conA = (msize>=0)
-    if f_log_sfh:
-        ax1.fill_between(age[conA], SFp[:,0][conA], SFp[:,2][conA], linestyle='-', color='k', alpha=0.5, zorder=-1)
-        ax1.errorbar(age, SFp[:,1], linestyle='-', color='k', marker='', zorder=-1, lw=.5)
-    else:
-        ax1.fill_between(age[conA], 10**SFp[:,0][conA], 10**SFp[:,2][conA], linestyle='-', color='k', alpha=0.5, zorder=-1)
-        ax1.errorbar(age, 10**SFp[:,1], linestyle='-', color='k', marker='', zorder=-1, lw=.5)
-
-    if f_symbol:
-        tbnd = 0.0001
-        for aa in range(len(age)):
-            agebin = np.arange(age[aa]-delTl[aa]/1e9, age[aa]+delTu[aa]/1e9, delTu[aa]/1e10)
-            tbnd = age[aa]+delT[aa]/2./1e9
-            
-            if f_log_sfh:
-                ax1.errorbar(age[aa], SFp[aa,1], xerr=[[delTl[aa]/1e9], [delTu[aa]/1e9]], \
-                    yerr=[[SFp[aa,1]-SFp[aa,0]], [SFp[aa,2]-SFp[aa,1]]], linestyle='', color=col[aa], marker='', zorder=1, lw=1.)
-                if msize[aa]>0:
-                    ax1.scatter(age[aa], SFp[aa,1], marker='.', color=col[aa], edgecolor='k', s=msize[aa], zorder=1)
-            else:
-                ax1.errorbar(age[aa], 10**SFp[aa,1], xerr=[[delTl[aa]/1e9], [delTu[aa]/1e9]], \
-                    yerr=[[10**SFp[aa,1]-10**SFp[aa,0]], [10**SFp[aa,2]-10**SFp[aa,1]]], linestyle='', color=col[aa], marker='.', zorder=1, lw=1.)
-                if msize[aa]>0:
-                    ax1.scatter(age[aa], 10**SFp[aa,1], marker='.', color=col[aa], edgecolor='k', s=msize[aa], zorder=1)
-
-    #############
-    # Get SFMS in log10;
-    #############
-    IMF = int(MB.inputs['NIMF'])
-    SFMS_16 = get_SFMS(zbes,age,10**ACp[:,0],IMF=IMF)
-    SFMS_50 = get_SFMS(zbes,age,10**ACp[:,1],IMF=IMF)
-    SFMS_84 = get_SFMS(zbes,age,10**ACp[:,2],IMF=IMF)
-
-    #try:
-    if False:
-        f_rejuv,t_quench,t_rejuv = check_rejuv(age,SFp[:,:],ACp[:,:],SFMS_50)
-    else:
-        print('Failed to call rejuvenation module.')
-        f_rejuv,t_quench,t_rejuv = 0,0,0
-
-    # Plot MS?
-    if f_SFMS:
-        if f_log_sfh:
-            ax1.fill_between(age[conA], SFMS_50[conA]-0.2, SFMS_50[conA]+0.2, linestyle='-', color='b', alpha=0.3, zorder=-2)
-            ax1.plot(age[conA], SFMS_50[conA], linestyle='--', color='k', alpha=0.5, zorder=-2)
-        else:
-            ax1.fill_between(age[conA], 10**(SFMS_50[conA]-0.2), 10**(SFMS_50[conA]+0.2), linestyle='-', color='b', alpha=0.3, zorder=-2)
-            ax1.plot(age[conA], 10**SFMS_50[conA], linestyle='--', color='k', alpha=0.5, zorder=-2)
-
-    #
-    # Mass in each bin
-    #
-    ax2label = ''
-    ax2.fill_between(age[conA], ACp[:,0][conA], ACp[:,2][conA], linestyle='-', color='k', alpha=0.5)
-    ax2.errorbar(age[conA], ACp[:,1][conA], xerr=[delTl[:][conA]/1e9,delTu[:][conA]/1e9],
-        yerr=[ACp[:,1][conA]-ACp[:,0][conA],ACp[:,2][conA]-ACp[:,1][conA]], linestyle='-', color='k', lw=0.5, label=ax2label, zorder=1)
-
-    if f_symbol:
-        tbnd = 0.0001
-        mtmp = 0
-        for ii in range(len(age)):
-            aa = len(age) -1 - ii
-            agebin = np.arange(0, age[aa], delTu[aa]/1e10)
-            ax2.errorbar(age[aa], ACp[aa,1], xerr=[[delTl[aa]/1e9],[delTu[aa]/1e9]],
-                yerr=[[ACp[aa,1]-ACp[aa,0]],[ACp[aa,2]-ACp[aa,1]]], linestyle='-', color=col[aa], lw=1, zorder=2)
-
-            tbnd = age[aa]+delT[aa]/2./1e9
-            mtmp = ACp[aa,1]
-            if msize[aa]>0:
-                ax2.scatter(age[aa], ACp[aa,1], marker='.', c=[col[aa]], edgecolor='k', s=msize[aa], zorder=2)
-
-
-    y2min = np.max([lmmin,np.min(ACp[:,0][conA])])
-    y2max = np.max(ACp[:,2][conA])+0.05
-    if np.abs(y2max-y2min) < 0.2:
-        y2min -= 0.2
-
-    #
-    # Total Metal
-    #
-    if not skip_zhist:
-        ax4.fill_between(age[conA], ZCp[:,0][conA], ZCp[:,2][conA], linestyle='-', color='k', alpha=0.5)
-        ax4.errorbar(age[conA], ZCp[:,1][conA], linestyle='-', color='k', lw=0.5, zorder=1)
-        
-        for ii in range(len(age)):
-            aa = len(age) -1 - ii
-            if msize[aa]>0:
-                ax4.errorbar(age[aa], ZCp[aa,1], xerr=[[delTl[aa]/1e9],[delTu[aa]/1e9]], yerr=[[ZCp[aa,1]-ZCp[aa,0]],[ZCp[aa,2]-ZCp[aa,1]]], linestyle='-', color=col[aa], lw=1, zorder=1)
-                ax4.scatter(age[aa], ZCp[aa,1], marker='.', c=[col[aa]], edgecolor='k', s=msize[aa], zorder=2)
-
-
-    #############
-    # Axis
-    #############
-    # For redshift
-    if zbes<4:
-        if zbes<2:
-            zred  = [zbes, 2, 3, 6]
-            zredl = ['$z_\mathrm{obs.}$', 2, 3, 6]
-        elif zbes<2.5:
-            zred  = [zbes, 2.5, 3, 6]
-            zredl = ['$z_\mathrm{obs.}$', 2.5, 3, 6]
-        elif zbes<3.:
-            zred  = [zbes, 3, 6]
-            zredl = ['$z_\mathrm{obs.}$', 3, 6]
-        else:
-            zred  = [zbes, 6]
-            zredl = ['$z_\mathrm{obs.}$', 6]
-    elif zbes<6:
-        zred  = [zbes, 5, 6, 9]
-        zredl = ['$z_\mathrm{obs.}$', 5, 6, 9]
-    elif zbes<12:
-        zred  = [zbes, 12]
-        zredl = ['$z_\mathrm{obs.}$', 12]
-    else:
-        zred  = [zbes, 20]
-        zredl = ['$z_\mathrm{obs.}$', 20]
-
-    Tzz = np.zeros(len(zred), dtype='float')
-    for zz in range(len(zred)):
-        Tzz[zz] = (Tuni - MB.cosmo.age(zred[zz]).value)
-        if Tzz[zz] < Txmin:
-            Tzz[zz] = Txmin
-    
-    lsfru = 2.8
-    if np.max(SFp[:,2])>2.8:
-        lsfru = np.max(SFp[:,2])+0.1
-    if np.min(SFp[:,2])>lsfrl:
-        lsfrl = np.min(SFp[:,2])+0.1
-
-    if f_log_sfh:
-        if f_axis_force:
-            ax1.set_ylim(lsfrl, lsfru)
-        ax1.set_ylabel('$\log \dot{M}_*/M_\odot$yr$^{-1}$', fontsize=12)
-        #ax1.plot(Tzz, Tzz*0+lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
-    else:
-        ax1.set_ylim(0, 10**lsfru)
-        ax1.set_ylabel('$\dot{M}_*/M_\odot$yr$^{-1}$', fontsize=12)
-        #ax1.plot(Tzz, Tzz*0+10**lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
-
-    if Txmax < np.max(age):
-        Txmax = np.max(age)
-    ax1.set_xlim(Txmin, Txmax)
-    ax1.set_xscale('log')
-
-    ax2.set_ylabel('$\log M_*/M_\odot$', fontsize=12)
-    ax2.set_xlim(Txmin, Txmax)
-    if f_axis_force:
-        ax2.set_ylim(y2min, y2max)
-    ax2.set_xscale('log')
-    ax2.text(np.min(age*1.05), y2min + 0.07*(y2max-y2min), 'ID: %s\n$z_\mathrm{obs.}:%.2f$\n$\log M_\mathrm{*}/M_\odot:%.2f$\n$\log Z_\mathrm{*}/Z_\odot:%.2f$\n$\log T_\mathrm{*}$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$'\
-        %(ID, zbes, ACp[0,1], ZCp[0,1], np.nanmedian(TC[0,:]), Avtmp[1]), fontsize=9, bbox=dict(facecolor='w', alpha=0.7), zorder=10)
-
-    #
-    # Brief Summary
-    #
-    # Writing SED param in a fits file;
-    # Header
-    prihdr = fits.Header()
-    prihdr['ID'] = ID
-    prihdr['z'] = zbes
-    prihdr['RA'] = RA
-    prihdr['DEC'] = DEC
-    # Add rejuv properties;
-    prihdr['f_rejuv'] = f_rejuv
-    prihdr['t_quen'] = t_quench
-    prihdr['t_rejuv'] = t_rejuv
-    # SFR
-    prihdr['tset_SFR'] = tset_SFR_SED
-    # Version;
-    import gsf
-    prihdr['version'] = gsf.__version__
-
-    percs = [16,50,84]
-    zmc = hdul[1].data['zmc']
-    ACP = [ACp[0,0], ACp[0,1], ACp[0,2]]
-    ZCP = [ZCp[0,0], ZCp[0,1], ZCp[0,2]]
-    ZLP = [ZLp[0,0], ZLp[0,1], ZLp[0,2]]
-    con = (~np.isnan(TC[0,:]))
-    TMW = [np.percentile(TC[0,:][con],16), np.percentile(TC[0,:][con],50), np.percentile(TC[0,:][con],84)]
-    con = (~np.isnan(TL[0,:]))
-    TLW = [np.percentile(TL[0,:][con],16), np.percentile(TL[0,:][con],50), np.percentile(TL[0,:][con],84)]
-    for ii in range(len(percs)):
-        prihdr['zmc_%d'%percs[ii]] = ('%.3f'%zmc[ii],'redshift')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH Mstel_%d'%percs[ii]] = ('%.3f'%ACP[ii], 'Stellar mass, logMsun')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH SFR_%d'%percs[ii]] = ('%.3f'%SFR_SED_med[ii], 'SFR, logMsun/yr')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH Z_MW_%d'%percs[ii]] = ('%.3f'%ZCP[ii], 'Mass-weighted metallicity, logZsun')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH Z_LW_%d'%percs[ii]] = ('%.3f'%ZLP[ii], 'Light-weighted metallicity, logZsun')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH T_MW_%d'%percs[ii]] = ('%.3f'%TMW[ii], 'Mass-weighted age, logGyr')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH T_LW_%d'%percs[ii]] = ('%.3f'%TLW[ii], 'Light-weighted age, logGyr')
-    for ii in range(len(percs)):
-        prihdr['AV_%d'%percs[ii]] = ('%.3f'%Avtmp[ii], 'Dust attenuation, mag')
-    prihdu = fits.PrimaryHDU(header=prihdr)
-
-    # For SFH plot;
-    t0 = Tuni - age[:]
-    col02 = []
-    col50 = fits.Column(name='time', format='E', unit='Gyr', array=age[:])
-    col02.append(col50)
-    col50 = fits.Column(name='time_l', format='E', unit='Gyr', array=age[:]-delTl[:]/1e9)
-    col02.append(col50)
-    col50 = fits.Column(name='time_u', format='E', unit='Gyr', array=age[:]+delTl[:]/1e9)
-    col02.append(col50)
-    col50 = fits.Column(name='SFR16', format='E', unit='logMsun/yr', array=SFp[:,0])
-    col02.append(col50)
-    col50 = fits.Column(name='SFR50', format='E', unit='logMsun/yr', array=SFp[:,1])
-    col02.append(col50)
-    col50 = fits.Column(name='SFR84', format='E', unit='logMsun/yr', array=SFp[:,2])
-    col02.append(col50)
-    col50 = fits.Column(name='Mstel16', format='E', unit='logMsun', array=ACp[:,0])
-    col02.append(col50)
-    col50 = fits.Column(name='Mstel50', format='E', unit='logMsun', array=ACp[:,1])
-    col02.append(col50)
-    col50 = fits.Column(name='Mstel84', format='E', unit='logMsun', array=ACp[:,2])
-    col02.append(col50)
-    col50 = fits.Column(name='Z16', format='E', unit='logZsun', array=ZCp[:,0])
-    col02.append(col50)
-    col50 = fits.Column(name='Z50', format='E', unit='logZsun', array=ZCp[:,1])
-    col02.append(col50)
-    col50 = fits.Column(name='Z84', format='E', unit='logZsun', array=ZCp[:,2])
-    col02.append(col50)
-    
-    colms = fits.ColDefs(col02)
-    dathdu = fits.BinTableHDU.from_columns(colms)
-    hdu = fits.HDUList([prihdu, dathdu])
-    file_sfh = MB.DIR_OUT + 'SFH_' + ID + '.fits'
-    hdu.writeto(file_sfh, overwrite=True)
-
-    # ASDF;
-    from astropy import units as u
-
-    # ASDF;
-    tree_sfh = {
-        'id': ID,
-        'redshift': '%.3f'%zbes,
-        'nimf': '%s'%(IMF),
-        'version_gsf': gsf.__version__
-    }
-    tree_sfh['sfh'] = {}
-
-    for ii in range(len(percs)):
-        tree_sfh['zmc_%d'%percs[ii]] = '%.3f'%zmc[ii]
-    for ii in range(len(percs)):
-        tree_sfh['Mstel_%d'%percs[ii]] = '%.3f'%(10**ACP[ii]) * u.Msun
-    for ii in range(len(percs)):
-        tree_sfh['SFR_%d'%percs[ii]] = '%.3f'%(10**SFR_SED_med[ii]) * u.Msun / u.yr
-    for ii in range(len(percs)):
-        tree_sfh['Z_MW_%d'%percs[ii]] = '%.3f'%ZCP[ii]
-    for ii in range(len(percs)):
-        tree_sfh['Z_LW_%d'%percs[ii]] = '%.3f'%ZLP[ii]
-    for ii in range(len(percs)):
-        tree_sfh['T_MW_%d'%percs[ii]] = '%.3f'%(10**TMW[ii]) * u.Gyr
-    for ii in range(len(percs)):
-        tree_sfh['T_LW_%d'%percs[ii]] = '%.3f'%(10**TLW[ii]) * u.Gyr
-    for ii in range(len(percs)):
-        tree_sfh['AV_%d'%percs[ii]] = '%.3f'%Avtmp[ii]*u.mag
-
-    tree_sfh['sfh'].update({'time': age * u.Gyr})
-    tree_sfh['sfh'].update({'time_l': (age[:]-delTl[:]/1e9) * u.Gyr})
-    tree_sfh['sfh'].update({'time_u': (age[:]+delTl[:]/1e9) * u.Gyr})
-    tree_sfh['sfh'].update({'SFR16': 10**SFp[:,0] * u.Msun / u.yr})
-    tree_sfh['sfh'].update({'SFR50': 10**SFp[:,1] * u.Msun / u.yr})
-    tree_sfh['sfh'].update({'SFR84': 10**SFp[:,2] * u.Msun / u.yr})
-    tree_sfh['sfh'].update({'Mstel16': 10**ACp[:,0] * u.Msun})
-    tree_sfh['sfh'].update({'Mstel50': 10**ACp[:,1] * u.Msun})
-    tree_sfh['sfh'].update({'Mstel84': 10**ACp[:,2] * u.Msun})
-    tree_sfh['sfh'].update({'logZ16': ZCp[:,0]})
-    tree_sfh['sfh'].update({'logZ50': ZCp[:,1]})
-    tree_sfh['sfh'].update({'logZ84': ZCp[:,2]})
-
-    af = asdf.AsdfFile(tree_sfh)
-    af.write_to(os.path.join(MB.DIR_OUT, 'gsf_sfh_%s.asdf'%(ID)), all_array_compression='zlib')
-
-    # Attach to MB;
-    MB.sfh_tlook = age
-    MB.sfh_tlookl= delTl[:][conA]/1e9
-    MB.sfh_tlooku= delTu[:][conA]/1e9
-    MB.sfh_sfr16 = SFp[:,0]
-    MB.sfh_sfr50 = SFp[:,1]
-    MB.sfh_sfr84 = SFp[:,2]
-    MB.sfh_mfr16 = ACp[:,0]
-    MB.sfh_mfr50 = ACp[:,1]
-    MB.sfh_mfr84 = ACp[:,2]
-    MB.sfh_zfr16 = ZCp[:,0]
-    MB.sfh_zfr50 = ZCp[:,1]
-    MB.sfh_zfr84 = ZCp[:,2]
-
-    # SFH
-    zzall = np.arange(1.,12,0.01)
-    Tall = MB.cosmo.age(zzall).value
-
-    dely2 = 0.1
-    while (y2max-y2min)/dely2>7:
-        dely2 *= 2.
-
-    y2ticks = np.arange(y2min, y2max, dely2)
-    ax2.set_yticks(y2ticks)
-    ax2.set_yticklabels(np.arange(y2min, y2max, dely2), minor=False)
-
-    ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
-
-    if not skip_zhist:
-
-        delZl = 0.4 # only for label. not delZ of Zall.
-        delZZl = delZl / 4
-
-        y3min, y3max = np.min(Z), np.max(Z)+delZZl
-        ax4.set_xlim(Txmin, Txmax)
-        ax4.set_ylim(y3min, y3max)
-        ax4.set_xscale('log')
-
-        if f_axis_force:
-            Zticks = np.arange(MB.Zall.min(), MB.Zall.max()+delZZl, delZl)
-            ax4.set_yticks(Zticks)
-            Zlabels = []
-            for Ztmp in Zticks:
-                Zlabels.append('%.1f'%(Ztmp))
-
-        ax4.yaxis.labelpad = -2
-        ax4.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
-        ax4.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
-        ax4t.set_xscale('log')
-
-        ax4t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
-        ax4t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
-        
-        ax4t.tick_params(axis='x', labelcolor='k')
-        ax4t.xaxis.set_ticks_position('none')
-        ax4t.plot(Tzz, Tzz*0+y3max+(y3max-y3min)*.00, marker='|', color='k', ms=3, linestyle='None')
-        ax4t.set_xlim(Txmin, Txmax)
-
-    ax1.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
-    ax2.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
-
-    # This has to come before set_xticks;
-    ax1t.set_xscale('log')
-    ax2t.set_xscale('log')
-
-    ax1t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
-    ax1t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
-    ax1t.tick_params(axis='x', labelcolor='k')
-    ax1t.xaxis.set_ticks_position('none')
-    ax1t.plot(Tzz, Tzz*0+lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
-
-    ax2t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
-    ax2t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
-    ax2t.tick_params(axis='x', labelcolor='k')
-    ax2t.xaxis.set_ticks_position('none')
-    ax2t.plot(Tzz, Tzz*0+y2max+(y2max-y2min)*.00, marker='|', color='k', ms=3, linestyle='None')
-
-    # This has to come after set_xticks;
-    ax1t.set_xlim(Txmin, Txmax)
-    ax2t.set_xlim(Txmin, Txmax)
-
-    # Save
-    fig.savefig(MB.DIR_OUT + 'SFH_' + ID + '_pcl.png', dpi=dpi)
-
-
-def sfr_tau(t0, tau0, Z=0.0, sfh=0, tt=np.arange(0,13,0.1), Mtot=1.,
-    sfrllim=1e-20):
-    '''
-    Parameters
-    ----------
-    sfh : int
-        1:exponential, 4:delayed exp, 5:, 6:lognormal
-    ML : float
-        Total Mass.
-    tt : float
-        Lookback time, in Gyr
-    tau0: float
-        in Gyr
-    t0 : float
-        age, in Gyr
-
-    Returns
-    -------
-    SFR : 
-        in Msun/yr
-    MFR :
-        in Msun
-
-    '''
-    yy = np.zeros(len(tt), dtype='float') 
-    yyms = np.zeros(len(tt), dtype='float')
-    con = (tt<=t0)
-    if sfh == 1:
-        yy[con] = np.exp((tt[con]-t0)/tau0)
-    elif sfh == 4:
-        yy[con] = (t0-tt[con]) * np.exp((tt[con]-t0)/tau0)
-    elif sfh == 6: # lognorm
-        con = (tt>0)
-        yy[con] = 1. / np.sqrt(2*np.pi*tau0**2) * np.exp(-(np.log(tt[con])-np.log(t0))**2/(2*tau0**2)) / tt[con]
-
-    # Total mass calculation;
-    #deltt = (tt[1] - tt[0]) #* 1e9
-    yyms[:] = np.cumsum(yy[::-1])[::-1] #* deltt * 1e9 # in Msun
-    
-    # Normalization;
-    deltt = tt[1] - tt[0]
-    C = Mtot/np.max(yyms)
-    yyms *= C
-    yy *= C / deltt / 1e9 # in Msun/yr
-
-    yy[~con] = sfrllim #1e-20
-    yyms[~con] = sfrllim #1e-20
-    return tt, yy, yyms
-
-
-def plot_sfh_tau(MB, flim=0.01, lsfrl=-1, mmax=1000, Txmin=0.08, Txmax=4, lmmin=8.5, fil_path='./FILT/',
-    dust_model=0, f_SFMS=False, f_symbol=True, verbose=False, f_silence=True, DIR_TMP=None,
-    f_log_sfh=True, dpi=250, TMIN=0.0001, tau_lim=0.01, skip_zhist=True, tset_SFR_SED=0.1):
-    '''
-    Purpose
-    -------
-    Star formation history plot.
-
-    Parameters
-    ----------
-    flim : float
-        Lower limit for plotting an age bin.
-    lsfrl : float
-        Lower limit for SFR, in logMsun/yr
-    f_SFMS : bool
-        If true, plot SFR of the main sequence of a ginen stellar mass at each lookback time.
-    tset_SFR_SED : float
-        in Gyr. Time scale over which SFR estimate is averaged.
-    '''
-    if f_silence:
-        import matplotlib
-        matplotlib.use("Agg")
-    else:
-        import matplotlib
-
-    print('\n### Running plot_sfh_tau ###\n')
-
-    fnc = MB.fnc
-    bfnc = MB.bfnc
-    ID = MB.ID
-    Z = MB.Zall
-    age = MB.age
-    nage = MB.nage
-    tau0 = MB.tau0
-    ageparam = MB.ageparam
-
-    try:
-        if not MB.ZFIX == None:
-            skip_zhist = True
-    except:
-        pass
-
-    NUM_COLORS = len(age)
-    cm = plt.get_cmap('gist_rainbow_r')
-    col = np.atleast_2d([cm(1.*i/NUM_COLORS) for i in range(NUM_COLORS)])
-
-    ################
-    # RF colors.
-    home = os.path.expanduser('~')
-    c = MB.c
-    chimax = 1.
-    m0set = MB.m0set
-    Mpc_cm = MB.Mpc_cm
-    d = MB.d
-
-    #############
-    # Plot.
-    #############
-    if f_log_sfh:
-        fig = plt.figure(figsize=(8,2.8))
-        fig.subplots_adjust(top=0.88, bottom=0.18, left=0.07, right=0.99, hspace=0.15, wspace=0.3)
-    else:
-        fig = plt.figure(figsize=(8.2,2.8))
-        fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
-
-    if skip_zhist:
-        if f_log_sfh:
-            fig = plt.figure(figsize=(5.5,2.8))
-            fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
-        else:
-            fig = plt.figure(figsize=(6.2,2.8))
-            fig.subplots_adjust(top=0.88, bottom=0.18, left=0.1, right=0.99, hspace=0.15, wspace=0.3)
-        ax1 = fig.add_subplot(121)
-        ax2 = fig.add_subplot(122)
-    else:
-        ax1 = fig.add_subplot(131)
-        ax2 = fig.add_subplot(132)
-        ax4 = fig.add_subplot(133)
-        ax4t = ax4.twiny()
-
-    ax1t = ax1.twiny()
-    ax2t = ax2.twiny()
-
-    ##################
-    # Fitting Results
-    ##################
-    SNlim = 3 # avobe which SN line is shown.
-
-
-    ###########################
-    # Open result file
-    ###########################
-    file = MB.DIR_OUT + 'summary_' + ID + '.fits'
-    hdul = fits.open(file) # open a FITS file
-    try:
-        zbes = hdul[0].header['zmc']
-    except:
-        zbes = hdul[0].header['z']
-    chinu= hdul[1].data['chi']
-
-    try:
-        RA   = hdul[0].header['RA']
-        DEC  = hdul[0].header['DEC']
-    except:
-        RA  = 0
-        DEC = 0
-    try:
-        SN = hdul[0].header['SN']
-    except:
-        ###########################
-        # Get SN of Spectra
-        ###########################
-        file = 'templates/spec_obs_' + ID + '.cat'
-        fds  = np.loadtxt(file, comments='#')
-        nrs  = fds[:,0]
-        lams = fds[:,1]
-        fsp  = fds[:,2]
-        esp  = fds[:,3]
-
-        consp = (nrs<10000) & (lams/(1.+zbes)>3600) & (lams/(1.+zbes)<4200)
-        if len((fsp/esp)[consp]>10):
-            SN = np.median((fsp/esp)[consp])
-        else:
-            SN = 1
-
-    Asum = 0
-    A50 = np.arange(len(age), dtype='float')
-    for aa in range(len(A50)):
-        A50[aa] = 10**hdul[1].data['A'+str(aa)][1]
-        Asum += A50[aa]
-
-    ####################
-    # For cosmology
-    ####################
-    DL = MB.cosmo.luminosity_distance(zbes).value * Mpc_cm # Luminositydistance in cm
-    Cons = (4.*np.pi*DL**2/(1.+zbes))
-
-    Tuni = MB.cosmo.age(zbes).value #, use_flat=True, **cosmo)
-    Tuni0 = (Tuni - age[:])
-
-    delT  = np.zeros(len(age),dtype='float')
-    delTl = np.zeros(len(age),dtype='float')
-    delTu = np.zeros(len(age),dtype='float')
-
-    if len(age) == 1:
-    #if tau0[0] < 0: # SSP;
-        for aa in range(len(age)):
-            try:
-                tau_ssp = float(MB.inputs['TAU_SSP'])
-            except:
-                tau_ssp = tau_lim
-            delTl[aa] = tau_ssp/2
-            delTu[aa] = tau_ssp/2
-            if age[aa] < tau_lim:
-                delT[aa] = tau_lim
-            else:
-                delT[aa] = delTu[aa] + delTl[aa]
-    else: # This is only true when CSP...
-        for aa in range(len(age)):
-            if aa == 0:
-                delTl[aa] = age[aa]
-                delTu[aa] = (age[aa+1]-age[aa])/2.
-                delT[aa]  = delTu[aa] + delTl[aa]
-            elif Tuni < age[aa]:
-                delTl[aa] = (age[aa]-age[aa-1])/2.
-                delTu[aa] = Tuni-age[aa] #delTl[aa] #10.
-                delT[aa]  = delTu[aa] + delTl[aa]
-            elif aa == len(age)-1:
-                delTl[aa] = (age[aa]-age[aa-1])/2.
-                delTu[aa] = Tuni - age[aa]
-                delT[aa]  = delTu[aa] + delTl[aa]
-            else:
-                delTl[aa] = (age[aa]-age[aa-1])/2.
-                delTu[aa] = (age[aa+1]-age[aa])/2.
-                if age[aa]+delTu[aa]>Tuni:
-                    delTu[aa] = Tuni-age[aa]
-                delT[aa] = delTu[aa] + delTl[aa]
-
-    con_delt = (delT<=0)
-    delT[con_delt] = 1e10
-    delT[:] *= 1e9 # Gyr to yr
-    delTl[:] *= 1e9 # Gyr to yr
-    delTu[:] *= 1e9 # Gyr to yr
-
-    ##############################
-    # Load Pickle
-    ##############################
-    samplepath = MB.DIR_OUT 
-    pfile = 'chain_' + ID + '_corner.cpkl'
-
-    niter = 0
-    data = loadcpkl(os.path.join(samplepath+'/'+pfile))
-    try:
-        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
-        burnin = data['burnin']
-        nmc    = data['niter']
-        nwalk  = data['nwalkers']
-        Nburn  = burnin #* nwalk/10/2 # I think this takes 3/4 of samples
-        #if nmc>1000:
-        #    Nburn  = 500
-        samples = data['chain'][:]
-    except:
-        print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
-        return -1
-
-    ######################
-    # Mass-to-Light ratio.
-    ######################
-    AM = np.zeros((len(age), mmax), dtype='float') # Mass in each bin.
-    AC = np.zeros((len(age), mmax), dtype='float') -99 # Cumulative mass in each bin.
-    AL = np.zeros((len(age), mmax), dtype='float') # Cumulative light in each bin.
-    ZM = np.zeros((len(age), mmax), dtype='float') # Z.
-    ZC = np.zeros((len(age), mmax), dtype='float') -99 # Cumulative Z.
-    ZL = np.zeros((len(age), mmax), dtype='float') -99 # Light weighted cumulative Z.
-    TC = np.zeros((len(age), mmax), dtype='float') # Mass weighted T.
-    TL = np.zeros((len(age), mmax), dtype='float') # Light weighted T.
-    ZMM = np.zeros((len(age), mmax), dtype='float') # Mass weighted Z.
-    ZML= np.zeros((len(age), mmax), dtype='float') # Light weighted Z.
-    SF = np.zeros((len(age), mmax), dtype='float') # SFR
-    Av = np.zeros(mmax, dtype='float') # SFR
-
-
-    # ##############################
-    # Add simulated scatter in quad
-    # if files are available.
-    # ##############################
-    f_zev = int(MB.inputs['ZEVOL'])
-
-    eZ_mean = 0
-
-    #####################
-    # Get SED based SFR
-    #####################
-    f_SFRSED_plot = False
-    SFR_SED = np.zeros(mmax,dtype='float')
-
-    # ASDF;
-    af = asdf.open(MB.DIR_TMP + 'spec_all_' + MB.ID + '.asdf')
-    af0 = asdf.open(MB.DIR_TMP + 'spec_all.asdf')
-    sedpar = af['ML'] # For M/L
-    sedpar0 = af0['ML'] # For mass loss frac.
-
-    ttmin = 0.001
-    tt = np.arange(ttmin,Tuni+0.5,ttmin/10)
-    xSF = np.zeros((len(tt), mmax), dtype='float') # SFR
-    ySF = np.zeros((len(tt), mmax), dtype='float') # SFR
-    yMS = np.zeros((len(tt), mmax), dtype='float') # MFR
-    ySF_each = np.zeros((MB.npeak, len(tt), mmax), dtype='float') # SFR
-    yMS_each = np.zeros((MB.npeak, len(tt), mmax), dtype='float') # MFR
-
-    ZZmc = np.zeros((MB.npeak, mmax), dtype='float') 
-    TTmc = np.zeros((MB.npeak, mmax), dtype='float') 
-    TAmc = np.zeros((MB.npeak, mmax), dtype='float') 
-
-    if Txmin > np.min(tt):
-        Txmin = np.min(tt) * 0.8
-
-    mm = 0
-    plot_each = True
-    while mm<mmax:
-        mtmp = np.random.randint(len(samples))# + Nburn
-        if MB.nAV != 0:
-            Av_tmp = samples['Av'][mtmp]
-        else:
-            Av_tmp = MB.AVFIX
-
-        for aa in range(MB.npeak):
-            AAtmp = samples['A%d'%aa][mtmp]
-            ltautmp = samples['TAU%d'%aa][mtmp]
-            lagetmp = samples['AGE%d'%aa][mtmp]
-            if aa == 0 or MB.ZEVOL:
-                try:
-                    ZZtmp = samples['Z%d'%aa][mtmp]
-                except:
-                    ZZtmp = MB.ZFIX
-
-            ZZmc[aa,mm] = ZZtmp
-            TAmc[aa,mm] = lagetmp
-            TTmc[aa,mm] = ltautmp
-
-            nZtmp,nttmp,natmp = bfnc.Z2NZ(ZZtmp, ltautmp, lagetmp)
-            mslist = sedpar['ML_'+str(nZtmp)+'_'+str(nttmp)][natmp]
-
-            xSF[:,mm], ySF_each[aa,:,mm], yMS_each[aa,:,mm] = sfr_tau(10**lagetmp, 10**ltautmp, ZZtmp, sfh=MB.SFH_FORM, tt=tt, Mtot=10**AAtmp*mslist)
-            ySF[:,mm] += ySF_each[aa,:,mm]
-            yMS[:,mm] += yMS_each[aa,:,mm]
-
-            # SFR from SED. This will be converted in log later;
-            con_sfr = (xSF[:,mm] <= tset_SFR_SED)
-            SFR_SED[mm] += np.mean(ySF_each[aa,:,mm][con_sfr])
-
-
-        Av[mm] = Av_tmp
-        if plot_each:
-            ax1.plot(xSF[:,mm], np.log10(ySF[:,mm]), linestyle='-', color='k', alpha=0.01, zorder=-1, lw=0.5)
-            ax2.plot(xSF[:,mm], np.log10(yMS[:,mm]), linestyle='-', color='k', alpha=0.01, zorder=-1, lw=0.5)
-
-        if SFR_SED[mm] > 0:
-            SFR_SED[mm] = np.log10(SFR_SED[mm])
-        else:
-            SFR_SED[mm] = -99
-
-
-        mm += 1
-
-    Avtmp = np.percentile(Av[:],[16,50,84])
-
-    #############
-    # Plot
-    #############
-    xSFp = np.zeros((len(tt),3), dtype='float')
-    ySFp = np.zeros((len(tt),3), dtype='float')
-    yMSp = np.zeros((len(tt),3), dtype='float')
-    ySFp_each = np.zeros((MB.npeak, len(tt), 3), dtype='float')
-    yMSp_each = np.zeros((MB.npeak, len(tt), 3), dtype='float')
-    for ii in range(len(tt)):
-        xSFp[ii,:] = np.percentile(xSF[ii,:], [16,50,84])
-        ySFp[ii,:] = np.percentile(ySF[ii,:], [16,50,84])
-        yMSp[ii,:] = np.percentile(yMS[ii,:], [16,50,84])
-        for aa in range(MB.npeak):
-            ySFp_each[aa,ii,:] = np.percentile(ySF_each[aa,ii,:], [16,50,84])
-            yMSp_each[aa,ii,:] = np.percentile(yMS_each[aa,ii,:], [16,50,84])
-
-    for aa in range(MB.npeak):
-        ax1.plot(xSFp[:,1], np.log10(ySFp_each[aa,:,1]), linestyle='-', color=col[aa], alpha=1., zorder=-1, lw=0.5)
-        ax2.plot(xSFp[:,1], np.log10(ySFp_each[aa,:,1]), linestyle='-', color=col[aa], alpha=1., zorder=-1, lw=0.5)
-
-    ax1.plot(xSFp[:,1], np.log10(ySFp[:,1]), linestyle='-', color='k', alpha=1., zorder=-1, lw=0.5)
-    ax2.plot(xSFp[:,1], np.log10(yMSp[:,1]), linestyle='-', color='k', alpha=1., zorder=-1, lw=0.5)
-
-    ACp = np.zeros((len(tt),3),'float')
-    SFp = np.zeros((len(tt),3),'float')
-    ACp[:] = np.log10(yMSp[:,:])
-    SFp[:] = np.log10(ySFp[:,:])
-
-    SFR_SED_med = np.percentile(SFR_SED[:],[16,50,84])
-
-    ###################
-    msize = np.zeros(len(age), dtype='float')
-    # Metal
-    ZCp = np.zeros((MB.npeak,3),'float')
-    TCp = np.zeros((MB.npeak,3),'float')
-    TTp = np.zeros((MB.npeak,3),'float')
-    for aa in range(len(age)):
-        if A50[aa]/Asum>flim: # if >1%
-            msize[aa] = 200 * A50[aa]/Asum
-
-        ZCp[aa,:] = np.percentile(ZZmc[aa,:], [16,50,84])
-        TCp[aa,:] = np.percentile(TTmc[aa,:], [16,50,84])
-        TTp[aa,:] = np.percentile(TAmc[aa,:], [16,50,84])
-
-    if False:
-        conA = (msize>=0)
-        if f_log_sfh:
-            ax1.fill_between(age[conA], SFp[:,0][conA], SFp[:,2][conA], linestyle='-', color='k', alpha=0.5, zorder=-1)
-            ax1.errorbar(age, SFp[:,1], linestyle='-', color='k', marker='', zorder=-1, lw=.5)
-        else:
-            ax1.fill_between(age[conA], 10**SFp[:,0][conA], 10**SFp[:,2][conA], linestyle='-', color='k', alpha=0.5, zorder=-1)
-            ax1.errorbar(age, 10**SFp[:,1], linestyle='-', color='k', marker='', zorder=-1, lw=.5)
-
-    #############
-    # Get SFMS in log10;
-    #############
-    IMF = int(MB.inputs['NIMF'])
-    SFMS_16 = get_SFMS(zbes,tt,10**ACp[:,0],IMF=IMF)
-    SFMS_50 = get_SFMS(zbes,tt,10**ACp[:,1],IMF=IMF)
-    SFMS_84 = get_SFMS(zbes,tt,10**ACp[:,2],IMF=IMF)
-
-    #try:
-    if False:
-        f_rejuv,t_quench,t_rejuv = check_rejuv(age,SFp[:,:],ACp[:,:],SFMS_50)
-    else:
-        print('Failed to call rejuvenation module.')
-        f_rejuv,t_quench,t_rejuv = 0,0,0
-
-    # Plot MS?
-    conA = ()
-    if f_SFMS:
-        if f_log_sfh:
-            ax1.fill_between(tt[conA], SFMS_50[conA]-0.2, SFMS_50[conA]+0.2, linestyle='-', color='b', alpha=0.3, zorder=-2)
-            ax1.plot(tt[conA], SFMS_50[conA], linestyle='--', color='k', alpha=0.5, zorder=-2)
-
-    # Plot limit;
-    y2min = np.max([lmmin,np.min(np.log10(yMSp[:,1]))])
-    y2max = np.max(np.log10(yMSp[:,1]))+0.05
-    if np.abs(y2max-y2min) < 0.2:
-        y2min -= 0.2
-
-    # Total Metal
-    if not skip_zhist:
-        ax4.fill_between(age[conA], ZCp[:,0][conA], ZCp[:,2][conA], linestyle='-', color='k', alpha=0.5)
-        ax4.errorbar(age[conA], ZCp[:,1][conA], linestyle='-', color='k', lw=0.5, zorder=1)
-        
-        for ii in range(len(age)):
-            aa = len(age) -1 - ii
-            if msize[aa]>0:
-                ax4.errorbar(age[aa], ZCp[aa,1], xerr=[[delTl[aa]/1e9],[delTu[aa]/1e9]], yerr=[[ZCp[aa,1]-ZCp[aa,0]],[ZCp[aa,2]-ZCp[aa,1]]], linestyle='-', color=col[aa], lw=1, zorder=1)
-                ax4.scatter(age[aa], ZCp[aa,1], marker='.', c=[col[aa]], edgecolor='k', s=msize[aa], zorder=2)
-
-
-    #############
-    # Axis
-    #############
-    # For redshift
-    if zbes<4:
-        if zbes<2:
-            zred  = [zbes, 2, 3, 6]
-            zredl = ['$z_\mathrm{obs.}$', 2, 3, 6]
-        elif zbes<2.5:
-            zred  = [zbes, 2.5, 3, 6]
-            zredl = ['$z_\mathrm{obs.}$', 2.5, 3, 6]
-        elif zbes<3.:
-            zred  = [zbes, 3, 6]
-            zredl = ['$z_\mathrm{obs.}$', 3, 6]
-        else:
-            zred  = [zbes, 6]
-            zredl = ['$z_\mathrm{obs.}$', 6]
-    elif zbes<6:
-        zred  = [zbes, 5, 6, 9]
-        zredl = ['$z_\mathrm{obs.}$', 5, 6, 9]
-    elif zbes<12:
-        zred  = [zbes, 12]
-        zredl = ['$z_\mathrm{obs.}$', 12]
-    else:
-        zred  = [zbes, 20]
-        zredl = ['$z_\mathrm{obs.}$', 20]
-
-    Tzz = np.zeros(len(zred), dtype='float')
-    for zz in range(len(zred)):
-        Tzz[zz] = (Tuni - MB.cosmo.age(zred[zz]).value)
-        if Tzz[zz] < Txmin:
-            Tzz[zz] = Txmin
-    
-    lsfru = 2.8
-    if np.max(SFp[:,2])>2.8:
-        lsfru = np.max(SFp[:,2])+0.1
-
-    if f_log_sfh:
-        ax1.set_ylim(lsfrl, lsfru)
-        ax1.set_ylabel('$\log \dot{M}_*/M_\odot$yr$^{-1}$', fontsize=12)
-        #ax1.plot(Tzz, Tzz*0+lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
-    else:
-        ax1.set_ylim(0, 10**lsfru)
-        ax1.set_ylabel('$\dot{M}_*/M_\odot$yr$^{-1}$', fontsize=12)
-        #ax1.plot(Tzz, Tzz*0+10**lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
-
-    ax1.set_xlim(Txmin, Txmax)
-    ax1.set_xscale('log')
-
-    ax2.set_ylabel('$\log M_*/M_\odot$', fontsize=12)
-    ax2.set_xlim(Txmin, Txmax)
-    ax2.set_ylim(y2min, y2max)
-    ax2.set_xscale('log')
-    ax2.text(np.min(age*1.05), y2min + 0.07*(y2max-y2min), 'ID: %s\n$z_\mathrm{obs.}:%.2f$\n$\log M_\mathrm{*}/M_\odot:%.2f$\n$\log Z_\mathrm{*}/Z_\odot:%.2f$\n$\log T_\mathrm{*}$/Gyr$:%.2f$\n$A_V$/mag$:%.2f$'\
-        %(ID, zbes, ACp[0,1], ZCp[0,1], np.nanmedian(TC[0,:]), Avtmp[1]), fontsize=9, bbox=dict(facecolor='w', alpha=0.7), zorder=10)
-
-    #
-    # Brief Summary
-    #
-    # Writing SED param in a fits file;
-    # Header
-    prihdr = fits.Header()
-    prihdr['ID'] = ID
-    prihdr['z'] = zbes
-    prihdr['RA'] = RA
-    prihdr['DEC'] = DEC
-    # Add rejuv properties;
-    prihdr['f_rejuv'] = f_rejuv
-    prihdr['t_quen'] = t_quench
-    prihdr['t_rejuv'] = t_rejuv
-    # SFR
-    prihdr['tset_SFR'] = tset_SFR_SED
-    # SFH
-    prihdr['SFH_FORM'] = MB.SFH_FORM
-    # Version;
-    import gsf
-    prihdr['version'] = gsf.__version__
-
-    percs = [16,50,84]
-    zmc = hdul[1].data['zmc']
-    ACP = [ACp[0,0], ACp[0,1], ACp[0,2]]
-    ZCP = [ZCp[0,0], ZCp[0,1], ZCp[0,2]]
-    ZLP = ZCP #[ZLp[0,0], ZLp[0,1], ZLp[0,2]]
-    TLW = TTp[0,:]
-    TMW = TTp[0,:]
-    TAW = TCp[0,:]
-    for ii in range(len(percs)):
-        prihdr['zmc_%d'%percs[ii]] = ('%.3f'%zmc[ii],'redshift')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH Mstel_%d'%percs[ii]] = ('%.3f'%ACP[ii], 'Stellar mass, logMsun')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH SFR_%d'%percs[ii]] = ('%.3f'%SFR_SED_med[ii], 'SFR, logMsun/yr')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH Z_MW_%d'%percs[ii]] = ('%.3f'%ZCP[ii], 'Mass-weighted metallicity, logZsun')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH Z_LW_%d'%percs[ii]] = ('%.3f'%ZLP[ii], 'Light-weighted metallicity, logZsun')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH T_MW_%d'%percs[ii]] = ('%.3f'%TMW[ii], 'Mass-weighted age, logGyr')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH T_LW_%d'%percs[ii]] = ('%.3f'%TLW[ii], 'Light-weighted age, logGyr')
-    for ii in range(len(percs)):
-        prihdr['HIERARCH TAU_%d'%percs[ii]] = ('%.3f'%TAW[ii], 'Tau, logGyr')
-    for ii in range(len(percs)):
-        prihdr['AV_%d'%percs[ii]] = ('%.3f'%Avtmp[ii], 'Dust attenuation, mag')
-    prihdu = fits.PrimaryHDU(header=prihdr)
-
-    # For SFH plot;
-    t0 = Tuni - age[:]
-    col02 = []
-    col50 = fits.Column(name='time', format='E', unit='Gyr', array=xSFp[:,1])
-    col02.append(col50)
-    col50 = fits.Column(name='time_l', format='E', unit='Gyr', array=xSFp[:,0])
-    col02.append(col50)
-    col50 = fits.Column(name='time_u', format='E', unit='Gyr', array=xSFp[:,2])
-    col02.append(col50)
-    col50 = fits.Column(name='SFR16', format='E', unit='logMsun/yr', array=SFp[:,0])
-    col02.append(col50)
-    col50 = fits.Column(name='SFR50', format='E', unit='logMsun/yr', array=SFp[:,1])
-    col02.append(col50)
-    col50 = fits.Column(name='SFR84', format='E', unit='logMsun/yr', array=SFp[:,2])
-    col02.append(col50)
-    col50 = fits.Column(name='Mstel16', format='E', unit='logMsun', array=ACp[:,0])
-    col02.append(col50)
-    col50 = fits.Column(name='Mstel50', format='E', unit='logMsun', array=ACp[:,1])
-    col02.append(col50)
-    col50 = fits.Column(name='Mstel84', format='E', unit='logMsun', array=ACp[:,2])
-    col02.append(col50)
-    col50 = fits.Column(name='Z16', format='E', unit='logZsun', array=ZCp[:,0])
-    col02.append(col50)
-    col50 = fits.Column(name='Z50', format='E', unit='logZsun', array=ZCp[:,1])
-    col02.append(col50)
-    col50 = fits.Column(name='Z84', format='E', unit='logZsun', array=ZCp[:,2])
-    col02.append(col50)
-    
-    colms = fits.ColDefs(col02)
-    dathdu = fits.BinTableHDU.from_columns(colms)
-    hdu = fits.HDUList([prihdu, dathdu])
-    file_sfh = MB.DIR_OUT + 'SFH_' + ID + '.fits'
-    hdu.writeto(file_sfh, overwrite=True)
-
-    # Attach to MB;
-    MB.sfh_tlook = age
-    MB.sfh_tlookl= delTl[:][conA]/1e9
-    MB.sfh_tlooku= delTu[:][conA]/1e9
-    MB.sfh_sfr16 = SFp[:,0]
-    MB.sfh_sfr50 = SFp[:,1]
-    MB.sfh_sfr84 = SFp[:,2]
-    MB.sfh_mfr16 = ACp[:,0]
-    MB.sfh_mfr50 = ACp[:,1]
-    MB.sfh_mfr84 = ACp[:,2]
-    MB.sfh_zfr16 = ZCp[:,0]
-    MB.sfh_zfr50 = ZCp[:,1]
-    MB.sfh_zfr84 = ZCp[:,2]
-
-    # SFH
-    zzall = np.arange(1.,12,0.01)
-    Tall = MB.cosmo.age(zzall).value # , use_flat=True, **cosmo)
-
-    dely2 = 0.1
-    while (y2max-y2min)/dely2>7:
-        dely2 *= 2.
-
-    y2ticks = np.arange(y2min, y2max, dely2)
-    ax2.set_yticks(y2ticks)
-    ax2.set_yticklabels(np.arange(y2min, y2max, dely2), minor=False)
-
-    ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
-
-    if not skip_zhist:
-        y3min, y3max = np.min([np.min(Z),-0.8]), np.max([np.max(Z),0.4])
-        ax4.set_xlim(Txmin, Txmax)
-        ax4.set_ylim(y3min-0.05, y3max)
-        ax4.set_xscale('log')
-        ax4.set_yticks([-0.8, -0.4, 0., 0.4])
-        ax4.set_yticklabels(['-0.8', '-0.4', '0', '0.4'])
-        
-        #ax4.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
-        #ax3.yaxis.labelpad = -2
-        ax4.yaxis.labelpad = -2
-        ax4.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
-        ax4.set_ylabel('$\log Z_*/Z_\odot$', fontsize=12)
-        ax4t.set_xscale('log')
-
-        ax4t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
-        ax4t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
-        ax4t.tick_params(axis='x', labelcolor='k')
-        ax4t.xaxis.set_ticks_position('none')
-        ax4t.plot(Tzz, Tzz*0+y3max+(y3max-y3min)*.00, marker='|', color='k', ms=3, linestyle='None')
-        ax4t.set_xlim(Txmin, Txmax)
-
-    ax1.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
-    ax2.set_xlabel('$t_\mathrm{lookback}$/Gyr', fontsize=12)
-
-    # This has to come before set_xticks;
-    ax1t.set_xscale('log')
-    ax2t.set_xscale('log')
-
-    ax1t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
-    ax1t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
-    ax1t.tick_params(axis='x', labelcolor='k')
-    ax1t.xaxis.set_ticks_position('none')
-    ax1t.plot(Tzz, Tzz*0+lsfru+(lsfru-lsfrl)*.00, marker='|', color='k', ms=3, linestyle='None')
-
-    ax2t.xaxis.set_major_locator(ticker.FixedLocator(Tzz[:]))
-    ax2t.xaxis.set_major_formatter(ticker.FixedFormatter(zredl[:]))
-    ax2t.tick_params(axis='x', labelcolor='k')
-    ax2t.xaxis.set_ticks_position('none')
-    ax2t.plot(Tzz, Tzz*0+y2max+(y2max-y2min)*.00, marker='|', color='k', ms=3, linestyle='None')
-
-    # This has to come after set_xticks;
-    ax1t.set_xlim(Txmin, Txmax)
-    ax2t.set_xlim(Txmin, Txmax)
-
-    # Save
-    fig.savefig(MB.DIR_OUT + 'SFH_' + ID + '_pcl.png', dpi=dpi)
-
-
-def get_evolv(MB, ID, Z=np.arange(-1.2,0.4249,0.05), age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0], f_comp=0, fil_path='./FILT/', \
-    inputs=None, dust_model=0, DIR_TMP='./templates/', delt_sfh=0.01):
-    '''
-    Purpose
-    -------
-    Reprocess output files to get spectra, UV color, and SFH at higher resolution.
-
-    Parameters
-    ----------
-    delt_sfh : float
-        delta t of input SFH in Gyr.
-    
-    Returns
-    -------
-    SED as function of age, based on SF and Z histories;
-    '''
-
-    print('This function may take a while as it runs fsps.')
-    flim  = 0.01
-    lsfrl = -1 # log SFR low limit
-    mmax  = 1000
-    Txmax = 4 # Max x value
-    lmmin = 10.3
-
-    nage = np.arange(0,len(age),1)
-    fnc  = Func(ID, Z, nage, dust_model=dust_model) # Set up the number of Age/ZZ
-    bfnc = Basic(Z)
-    age = np.asarray(age)
-
-    ################
-    # RF colors.
-    import os.path
-    home = os.path.expanduser('~')
-    c      = MB.c #3.e18 # A/s
-    m0set  = MB.m0set #25.0
-    chimax = 1.
-    d      = 10**(73.6/2.5) * 1e-18 # From [ergs/s/cm2/A] to [ergs/s/cm2/Hz]
-
-    ###########################
-    # Open result file
-    ###########################
-    file = 'summary_' + ID + '.fits'
-    hdul = fits.open(file) # open a FITS file
-    zbes = hdul[0].header['z']
-    chinu= hdul[1].data['chi']
-
-    uv= hdul[1].data['uv']
-    vj= hdul[1].data['vj']
-
-    RA   = 0
-    DEC  = 0
-    rek  = 0
-    erekl= 0
-    ereku= 0
-    mu = 1.0
-    nn = 0
-    qq = 0
-    enn = 0
-    eqq = 0
-    try:
-        RA   = hdul[0].header['RA']
-        DEC  = hdul[0].header['DEC']
-    except:
-        RA  = 0
-        DEC = 0
-
-    try:
-        SN = hdul[0].header['SN']
-    except:
-        ###########################
-        # Get SN of Spectra
-        ###########################
-        file = 'templates/spec_obs_' + ID + '.cat'
-        fds  = np.loadtxt(file, comments='#')
-        nrs  = fds[:,0]
-        lams = fds[:,1]
-        fsp  = fds[:,2]
-        esp  = fds[:,3]
-
-        consp = (nrs<10000) & (lams/(1.+zbes)>3600) & (lams/(1.+zbes)<4200)
-        if len((fsp/esp)[consp]>10):
-            SN = np.median((fsp/esp)[consp])
-        else:
-            SN = 1
-
-
-    Asum = 0
-    A50 = np.arange(len(age), dtype='float')
-    for aa in range(len(A50)):
-        A50[aa] = hdul[1].data['A'+str(aa)][1]
-        Asum += A50[aa]
-
-    ####################
-    # For cosmology
-    ####################
-    DL = MB.cosmo.luminosity_distance(zbes).value * MB.Mpc_cm # Luminositydistance in cm
-    Cons = (4.*np.pi*DL**2/(1.+zbes))
-
-    Tuni = MB.cosmo.age(zbes).value
-    Tuni0 = (Tuni - age[:])
-
-    delT  = np.zeros(len(age),dtype='float')
-    delTl = np.zeros(len(age),dtype='float')
-    delTu = np.zeros(len(age),dtype='float')
-    for aa in range(len(age)):
-        if aa == 0:
-            delTl[aa] = age[aa]
-            delTu[aa] = (age[aa+1]-age[aa])/2.
-            delT[aa]  = delTu[aa] + delTl[aa]
-        elif Tuni < age[aa]:
-            delTl[aa] = (age[aa]-age[aa-1])/2.
-            delTu[aa] = delTl[aa] #10.
-            delT[aa]  = delTu[aa] + delTl[aa]
-        elif aa == len(age)-1:
-            delTl[aa] = (age[aa]-age[aa-1])/2.
-            delTu[aa] = Tuni - age[aa]
-            delT[aa]  = delTu[aa] + delTl[aa]
-        else:
-            delTl[aa] = (age[aa]-age[aa-1])/2.
-            delTu[aa] = (age[aa+1]-age[aa])/2.
-            delT[aa]  = delTu[aa] + delTl[aa]
-
-    delT[:]  *= 1e9 # Gyr to yr
-    delTl[:] *= 1e9 # Gyr to yr
-    delTu[:] *= 1e9 # Gyr to yr
-    ##############################
-    # Load Pickle
-    ##############################
-    samplepath = './'
-    pfile = 'chain_' + ID + '_corner.cpkl'
-
-    niter = 0
-    data = loadcpkl(os.path.join(samplepath+'/'+pfile))
-    try:
-        ndim   = data['ndim']     # By default, use ndim and burnin values contained in the cpkl file, if present.
-        burnin = data['burnin']
-        nmc    = data['niter']
-        nwalk  = data['nwalkers']
-        Nburn  = burnin #* nwalk/10/2 # I think this takes 3/4 of samples
-        #if nmc>1000:
-        #    Nburn  = 500
-        samples = data['chain'][:]
-    except:
-        print(' =   >   NO keys of ndim and burnin found in cpkl, use input keyword values')
-        return -1
-
-    ######################
-    # Mass-to-Light ratio.
-    ######################
-    AM = np.zeros((len(age), mmax), dtype='float') # Mass in each bin.
-    AC = np.zeros((len(age), mmax), dtype='float') # Cumulative mass in each bin.
-    AL = np.zeros((len(age), mmax), dtype='float') # Cumulative light in each bin.
-    ZM = np.zeros((len(age), mmax), dtype='float') # Z.
-    ZC = np.zeros((len(age), mmax), dtype='float') # Cumulative Z.
-    ZL = np.zeros((len(age), mmax), dtype='float') # Light weighted cumulative Z.
-    TC = np.zeros((len(age), mmax), dtype='float') # Mass weighted T.
-    TL = np.zeros((len(age), mmax), dtype='float') # Light weighted T.
-    ZMM= np.zeros((len(age), mmax), dtype='float') # Mass weighted Z.
-    ZML= np.zeros((len(age), mmax), dtype='float') # Light weighted Z.
-    SF = np.zeros((len(age), mmax), dtype='float') # SFR
-    Av = np.zeros(mmax, dtype='float') # SFR
-
-    # ##############################
-    # Add simulated scatter in quad
-    # if files are available.
-    # ##############################
-    try:
-        f_zev = int(MB.inputs['ZEVOL'])
-    except:
-        f_zev = 1
-
-    eZ_mean = 0
-    try:
-        meanfile = './sim_SFH_mean.cat'
-        dfile    = np.loadtxt(meanfile, comments='#')
-        eA = dfile[:,2]
-        eZ = dfile[:,4]
-        eAv= np.mean(dfile[:,6])
-        if f_zev == 0:
-            eZ_mean = np.mean(eZ[:])
-            eZ[:]   = age * 0 #+ eZ_mean
-        else:
-            try:
-                f_zev = int(prihdr['ZEVOL'])
-                if f_zev == 0:
-                    eZ_mean = np.mean(eZ[:])
-                    eZ = age * 0
-            except:
-                pass
-    except:
-        print('No simulation file (%s).\nError may be underestimated.' % meanfile)
-        eA = age * 0
-        eZ = age * 0
-        eAv= 0
-
-    mm = 0
-    for mm in range(mmax):
-        mtmp  = np.random.randint(len(samples))# + Nburn
-        AAtmp = np.zeros(len(age), dtype='float')
-        ZZtmp = np.zeros(len(age), dtype='float')
-        mslist= np.zeros(len(age), dtype='float')
-
-        Av_tmp = samples['Av'][mtmp]
-
-        f0 = fits.open(DIR_TMP + 'ms_' + ID + '.fits')
-        sedpar = f0[1]
-        f1 = fits.open(DIR_TMP + 'ms.fits')
-        mloss = f1[1].data
-
-        Avrand = np.random.uniform(-eAv, eAv)
-        if Av_tmp + Avrand<0:
-            Av[mm] = 0
-        else:
-            Av[mm] = Av_tmp + Avrand
-
-        for aa in range(len(age)):
-            AAtmp[aa] = samples['A'+str(aa)][mtmp]/mu
-            try:
-                ZZtmp[aa] = samples['Z'+str(aa)][mtmp]
-            except:
-                ZZtmp[aa] = samples['Z0'][mtmp]
-
-            nZtmp      = bfnc.Z2NZ(ZZtmp[aa])
-            mslist[aa] = sedpar.data['ML_'+str(nZtmp)][aa]
-
-            ml = mloss['ms_'+str(nZtmp)][aa]
-
-            Arand = np.random.uniform(-eA[aa],eA[aa])
-            Zrand = np.random.uniform(-eZ[aa],eZ[aa])
-            AM[aa, mm] = AAtmp[aa] * mslist[aa] * 10**Arand
-            AL[aa, mm] = AM[aa, mm] / mslist[aa]
-            SF[aa, mm] = AAtmp[aa] * mslist[aa] / delT[aa] / ml * 10**Arand
-            ZM[aa, mm] = ZZtmp[aa] + Zrand
-            ZMM[aa, mm]= (10 ** ZZtmp[aa]) * AAtmp[aa] * mslist[aa] * 10**Zrand
-            ZML[aa, mm]= ZMM[aa, mm] / mslist[aa]
-
-        for aa in range(len(age)):
-            AC[aa, mm] = np.sum(AM[aa:, mm])
-            ZC[aa, mm] = np.log10(np.sum(ZMM[aa:, mm])/AC[aa, mm])
-            ZL[aa, mm] = np.log10(np.sum(ZML[aa:, mm])/np.sum(AL[aa:, mm]))
-            if f_zev == 0: # To avoid random fluctuation in A.
-                ZC[aa, mm] = ZM[aa, mm]
-
-            ACs = 0
-            ALs = 0
-            for bb in range(aa, len(age), 1):
-                tmpAA       = 10**np.random.uniform(-eA[bb],eA[bb])
-                tmpTT       = np.random.uniform(-delT[bb]/1e9,delT[bb]/1e9)
-                TC[aa, mm] += (age[bb]+tmpTT) * AAtmp[bb] * mslist[bb] * tmpAA
-                TL[aa, mm] += (age[bb]+tmpTT) * AAtmp[bb] * tmpAA
-                ACs        += AAtmp[bb] * mslist[bb] * tmpAA
-                ALs        += AAtmp[bb] * tmpAA
-
-            TC[aa, mm] /= ACs
-            TL[aa, mm] /= ALs
-
-    Avtmp  = np.percentile(Av[:],[16,50,84])
-
-    #############
-    # Plot
-    #############
-    AMp = np.zeros((len(age),3), dtype='float')
-    ACp = np.zeros((len(age),3), dtype='float')
-    ZMp = np.zeros((len(age),3), dtype='float')
-    ZCp = np.zeros((len(age),3), dtype='float')
-    SFp = np.zeros((len(age),3), dtype='float')
-    for aa in range(len(age)):
-       AMp[aa,:] = np.percentile(AM[aa,:], [16,50,84])
-       ACp[aa,:] = np.percentile(AC[aa,:], [16,50,84])
-       ZMp[aa,:] = np.percentile(ZM[aa,:], [16,50,84])
-       ZCp[aa,:] = np.percentile(ZC[aa,:], [16,50,84])
-       SFp[aa,:] = np.percentile(SF[aa,:], [16,50,84])
-
-    ###################
-    msize = np.zeros(len(age), dtype='float')
-    for aa in range(len(age)):
-        if A50[aa]/Asum>flim: # if >1%
-            msize[aa] = 150 * A50[aa]/Asum
-
-    conA = (msize>=0)
-    # Make template;
-    tbegin = np.min(Tuni-age)
-    tuniv_hr = np.arange(tbegin,Tuni,delt_sfh) # in Gyr
-    sfh_hr_in= np.interp(tuniv_hr,(Tuni-age)[::-1],SFp[:,1][::-1])
-    zh_hr_in = np.interp(tuniv_hr,(Tuni-age)[::-1],ZCp[:,1][::-1])
-
-    # FSPS
-    con_sfh = (tuniv_hr>0)
-    import fsps
-    nimf = int(inputs['NIMF'])
-    try:
-        fneb = int(inputs['ADD_NEBULAE'])
-    except:
-        fneb = 0
-
-    if fneb == 1:
-        print('Metallicity is set to logZ/Zsun=%.2f'%(np.max(zh_hr_in)))
-        sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=1, imf_type=nimf, logzsol=np.max(zh_hr_in), sfh=3, dust_type=2, dust2=0.0, add_neb_emission=True)
-        sp.set_tabular_sfh(tuniv_hr[con_sfh], sfh_hr_in[con_sfh])
-    else:
-        sp = fsps.StellarPopulation(compute_vega_mags=False, zcontinuous=3, imf_type=nimf, sfh=3, dust_type=2, dust2=0.0, add_neb_emission=False)
-        sp.set_tabular_sfh(tuniv_hr[con_sfh], sfh_hr_in[con_sfh], Z=10**zh_hr_in[con_sfh])
-
-    col01 = []
-    t_get = tuniv_hr[con_sfh]
-
-    for ss in range(len(t_get)):
-        wave0, flux0 = sp.get_spectrum(tage=t_get[ss], peraa=True)
-        if ss == 0:
-            spec_mul_nu_conv = np.zeros((len(t_get),len(wave0)),dtype='float')
-
-        print('Template %d is processed.'%(ss))
-        wavetmp  = wave0*(1.+zbes)
-        spec_mul_nu = flamtonu(wavetmp, flux0) # Conversion from Flambda to Fnu.
-        Lsun = MB.Lsun #3.839 * 1e33 #erg s-1
-        stmp_common = 1e10 # 1 tmp is in 1e10Lsun
-
-        spec_mul_nu_conv[ss,:] = spec_mul_nu[:]
-        spec_mul_nu_conv[ss,:] *= Lsun/(4.*np.pi*DL**2/(1.+zbes))
-        Ls = 10**sp.log_lbol
-        spec_mul_nu_conv[ss,:] *= (1./Ls)*stmp_common # in unit of erg/s/Hz/cm2/ms[ss].
-
-        consave = (wavetmp/(1.+zbes)<20000) # AA
-        if ss == 0:
-            nd_ap  = np.arange(0,len(wave0),1)
-            col1   = fits.Column(name='wavelength', format='E', unit='AA', array=wavetmp[consave])#, disp='obs'
-            col2   = fits.Column(name='colnum', format='K', unit='', array=nd_ap[consave])
-            col00  = [col1, col2]
-            col3   = fits.Column(name='age', format='E', unit='Gyr', array=t_get)
-            col4   = fits.Column(name='sfh', format='E', unit='Msun/yr', array=sfh_hr_in[con_sfh])
-            col5   = fits.Column(name='zh', format='E', unit='Zsun', array=zh_hr_in[con_sfh])
-            col01  = [col3,col4,col5]
-
-        colspec_all = fits.Column(name='fspec_'+str(ss), format='E', unit='Fnu', array=spec_mul_nu_conv[ss,:][consave])#, disp='%s'%(t_get[ss])
-        col00.append(colspec_all)
-
-    coldefs_spec = fits.ColDefs(col00)
-    hdu = fits.BinTableHDU.from_columns(coldefs_spec)
-    hdu.writeto(DIR_TMP + 'obsspec_' + ID + '.fits', overwrite=True)
-
-    coldefs_spec = fits.ColDefs(col01)
-    hdu = fits.BinTableHDU.from_columns(coldefs_spec)
-    hdu.writeto(DIR_TMP + 'obshist_' + ID + '.fits', overwrite=True)
-
-
-def plot_evolv(MB, ID, Z=np.arange(-1.2,0.4249,0.05), age=[0.01, 0.1, 0.3, 0.7, 1.0, 3.0], f_comp=0, fil_path='./FILT/', \
-    inputs=None, dust_model=0, DIR_TMP='./templates/', delt_sfh = 0.01, nmc=300):
-    '''
-    Parameters
-    ----------
-    delt_sfh : float
-        delta t of input SFH in Gyr.
-
-    Returns
-    -------
-    SED as function of age, based on SF and Z histories.
-    '''
-
-    import os.path
-
-    ################
-    flim = 0.01
-    lsfrl = -1 # log SFR low limit
-    mmax  = 1000
-    Txmax = 4 # Max x value
-    lmmin = 10.3
-
-    nage = np.arange(0,len(age),1)
-    fnc  = Func(ID, Z, nage, dust_model=dust_model) # Set up the number of Age/ZZ
-    bfnc = Basic(Z)
-    age = np.asarray(age)
-
-    ################
-    # RF colors.
-    home = os.path.expanduser('~')
-    c      = 3.e18 # A/s
-    chimax = 1.
-    m0set   = 25.0
-    d      = 10**(73.6/2.5) * 1e-18 # From [ergs/s/cm2/A] to [ergs/s/cm2/Hz]
-
-    #############
-    # Plot.
-    #############
-    fig = plt.figure(figsize=(5,2.6))
-    fig.subplots_adjust(top=0.96, bottom=0.16, left=0.12, right=0.99, hspace=0.15, wspace=0.15)
-    #ax1 = fig.add_subplot(131)
-    #ax2 = fig.add_subplot(132)
-    #ax3 = fig.add_subplot(133)
-    ax2 = fig.add_subplot(121)
-    ax3 = fig.add_subplot(122)
-
-    ###########################
-    # Open result file
-    ###########################
-    file = 'summary_' + ID + '.fits'
-    hdul = fits.open(file) # open a FITS file
-    zbes = hdul[0].header['z']
-    chinu= hdul[1].data['chi']
-    uv= hdul[1].data['uv']
-    vj= hdul[1].data['vj']
-
-    try:
-        RA   = hdul[0].header['RA']
-        DEC  = hdul[0].header['DEC']
-    except:
-        RA  = 0
-        DEC = 0
-
-    try:
-        SN = hdul[0].header['SN']
-    except:
-        ###########################
-        # Get SN of Spectra
-        ###########################
-        file = 'templates/spec_obs_' + ID + '.cat'
-        fds  = np.loadtxt(file, comments='#')
-        nrs  = fds[:,0]
-        lams = fds[:,1]
-        fsp  = fds[:,2]
-        esp  = fds[:,3]
-
-        consp = (nrs<10000) & (lams/(1.+zbes)>3600) & (lams/(1.+zbes)<4200)
-        if len((fsp/esp)[consp]>10):
-            SN = np.median((fsp/esp)[consp])
-        else:
-            SN = 1
-
-    Asum = 0
-    A50 = np.arange(len(age), dtype='float')
-    for aa in range(len(A50)):
-        A50[aa] = hdul[1].data['A'+str(aa)][1]
-        Asum += A50[aa]
-
-    # Cosmo;
-    DL = MB.cosmo.luminosity_distance(zbes).value * MB.Mpc_cm # Luminositydistance in cm
-    Cons = (4.*np.pi*DL**2/(1.+zbes))
-    Tuni = MB.cosmo.age(zbes).value #, use_flat=True, **cosmo)
-    Tuni0 = (Tuni - age[:])
-
-    # Open summary;
-    file = 'summary_' + ID + '.fits'
-    fd   = fits.open(file)[1].data
-    #print(fits.open(file)[1].header)
-    Avtmp = fd['Av0']
-    uvtmp = fd['uv']
-    vjtmp = fd['vj']
-    #ax2.plot(vj[1],uv[1],color='gray',marker='s',ms=3)
-
-    # SFH
-    file = DIR_TMP + 'obshist_' + ID + '.fits'
-    fd   = fits.open(file)[1].data
-    age  = fd['age']
-    sfh  = fd['sfh']
-    zh   = fd['zh']
-
-    # Open FSPS temp;
-    file = DIR_TMP + 'obsspec_' + ID + '.fits'
-    fd   = fits.open(file)[1].data
-    wave = fd['wavelength']
-    nr   = fd['colnum']
-    uvall= age * 0 - 99
-    vjall= age * 0 - 99
-
-    delp = -10
-    flag = False
-    flag2= False
-    #for ii in range(1,len(age),10):
-    for ii in range(len(age)-1,-1,delp):
-        flux = fd['fspec_%d'%(ii)]
-        flux_att = madau_igm_abs(wave/(1.+zbes), flux, zbes)
-        flux_d, xxd, nrd = dust_calz(wave/(1.+zbes), flux_att, 0.0, nr)
-        #ax1.plot(xxd,flux_d,linestyle='-',lw=0.3+0.1*ii/len(age))
-        band0  = ['u','v','j']
-        lmconv,fconv = filconv(band0, xxd, flux_d, fil_path) # flux in fnu
-        uv = -2.5*np.log10(fconv[0]/fconv[1])
-        vj = -2.5*np.log10(fconv[1]/fconv[2])
-        uvall[ii] = uv
-        vjall[ii] = vj
-        #ax2.plot(vjall[ii],uvall[ii],marker='s',ms=5,linestyle='-',zorder=5)
-
-        if flag and not flag2:
-            flux_d, xxd, nrd = dust_calz(wave/(1.+zbes), flux_att, Avtmp[1], nr)
-            lmconv,fconv = filconv(band0, xxd, flux_d, fil_path) # flux in fnu
-            uv_av = -2.5*np.log10(fconv[0]/fconv[1])
-            vj_av = -2.5*np.log10(fconv[1]/fconv[2])
-            delvj, deluv = vj_av-vj, uv_av-uv
-            flag2 = True
-        flag = True
-
-    #import matplotlib.colormaps as cm
-    conuvj = (vjall>-90)&(uvall>-90)
-    ax2.plot(vjall[conuvj],uvall[conuvj],marker='s',markeredgecolor='k',color='none',ms=5,zorder=4)
-    ax2.scatter(vjall[conuvj],uvall[conuvj],marker='s',c=age[conuvj],cmap='jet',s=8,zorder=5)
-    ax2.plot(vjall[conuvj],uvall[conuvj],marker='',color='k',ms=3,linestyle='-',zorder=3)
-    ax3.plot(age,np.log10(sfh),color='b', linewidth=1, linestyle='-',label=r'$\log$SFR$/M_\odot$yr$^{-1}$')
-    ax3.plot(age,zh,color='r', linewidth=1, linestyle='-',label=r'$\log Z_*/Z_\odot$')
-
-    '''
-    ax1.set_xlim(1000,40000)
-    ax1.set_xscale('log')
-    ax1.set_ylim(1e-2,1e2)
-    ax1.set_yscale('log')
-    ax1.set_xlabel('Wavelength')
-    '''
-
-    ax2.set_xlim(-0.3,2.3)
-    ax2.set_ylim(-0.3,2.3)
-
-    ax2.set_xlabel('$V-J\,$/ mag')
-    ax2.set_ylabel('$U-V\,$/ mag')
-    ax3.set_xlabel('age / Gyr')
-    #ax3.set_ylabel('$\log$SFR$/M_\odot$yr$^{-1}$ or $\log Z_*/Z_\odot$')
-
-    ##
-    prog_path = '/Users/tmorishita/GitHub/gsf/gsf/example/misc/'
-    data_uvj = np.loadtxt(prog_path+'f2.cat',comments='#')
-    x=data_uvj[:,0]
-    y=data_uvj[:,1]
-    ax2.plot(x,y,color="gray",lw=1,ls="-")
-
-    data_uvj = np.loadtxt(prog_path+'g2.cat',comments='#')
-    x=data_uvj[:,0]
-    y=data_uvj[:,1]
-    ax2.plot(x,y,color="gray",lw=1,ls="-")
-
-    data_uvj = np.loadtxt(prog_path+'h2.cat',comments='#')
-    x=data_uvj[:,0]
-    y=data_uvj[:,1]
-    ax2.plot(x,y,color="gray",lw=1,ls="-")
-
-    try:
-        av=np.array([1.2,0.,delvj,deluv])
-        X,Y,U,V=zip(av)
-        ax2.quiver(X,Y,U,V,angles='xy',scale_units='xy',scale=1,linewidth=1,color="k")
-    except:
-        pass
-
-    ax2.text(-0.1,2.1,'Quiescent',fontsize=11,color='orangered')
-    ax2.text(1.3,-0.2,'Starforming',fontsize=11,color='royalblue')
-
-    ax3.legend(loc=3)
-    #plt.show()
-    plt.savefig('hist_' + ID + '.pdf')
diff --git a/build/lib/gsf/posterior_flexible.py b/build/lib/gsf/posterior_flexible.py
deleted file mode 100644
index ea6a858..0000000
--- a/build/lib/gsf/posterior_flexible.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import numpy as np
-import sys
-import scipy.integrate as integrate
-from scipy.integrate import cumtrapz
-from scipy import stats
-from numpy import exp as np_exp
-from numpy import log as np_log
-from scipy.special import erf
-
-
-class Post:
-    '''
-    Class function for MCMC
-    '''
-    def __init__(self, mainbody):
-        self.mb = mainbody
-        self.scale = 1
-        self.gauss_Mdyn = None
-        self.Na = len(self.mb.age)
-
-
-    def residual(self, pars, fy:float, ey:float, wht:float, f_fir:bool, out:bool=False, 
-        f_val:bool=False, f_penlize:bool=True, f_only_spec:bool=False):
-        '''
-        Parameters
-        ----------
-        out : 
-            model as second output. For lnprob func.
-        f_fir : bool
-            If dust component is on or off.
-
-        Returns
-        -------
-        residual of model and data.
-        '''
-        if f_val:
-            vals = pars
-        else:
-            vals = pars.valuesdict()
-
-        model, x1 = self.mb.fnc.get_template(vals)
-
-        if self.mb.f_dust:
-            model_dust, x1_dust = self.mb.fnc.tmp04_dust(vals)
-            n_optir = self.mb.n_optir #len(model)
-
-            # Add dust flux to opt/IR grid.
-            model[:] += model_dust[:n_optir]
-            # then append only FIR flux grid.
-            model = np.append(model,model_dust[n_optir:])
-            x1 = np.append(x1,x1_dust[n_optir:])
-
-        if self.mb.fneb:
-            n_optir = self.mb.n_optir
-            model_neb, x1_neb = self.mb.fnc.get_template(vals, f_neb=True)
-            model[:n_optir] += model_neb
-
-        if self.mb.ferr:
-            try:
-                logf = vals['logf']
-            except:
-                logf = -np.inf
-        else:
-            logf = -np.inf # temporary... (if f is param, then take from vals dictionary.)
-
-        if f_only_spec:
-            contmp = (self.mb.dict['NR']<10000)
-            fy = fy[contmp]
-            ey = ey[contmp]
-            wht = wht[contmp]
-            model = model[contmp]
-
-        sig = wht[:] * 0
-        con_res = (wht>0)
-        con_res_r = (wht==0)
-        sig[con_res] = ey[con_res]**2 + model[con_res]**2 * np_exp(logf)**2
-        sig[con_res_r] = wht[con_res_r] * 0 + np.inf
-
-        if fy is None:
-            print('Data is none')
-            resid = model
-        else:
-            resid = (model - fy) / np.sqrt(sig)
-
-        if self.mb.ferr and f_penlize:
-            # Penalize redisual;
-            tmp = (model - fy)**2 / sig + np_log(2*3.14*sig**2)
-            con_res = (tmp>0) & (~np.isinf(tmp))
-            resid[con_res] = np.sqrt(tmp[con_res])
-
-        if not out:
-            return resid # i.e. residual/sigma. Because is_weighted = True.
-        else:
-            return resid, model # i.e. residual/sigma. Because is_weighted = True.
-
-
-    def func_tmp(self, xint, eobs, fmodel):
-        '''
-        A function used for chi2 calculation for non-detection in lnprob.
-        '''
-        int_tmp = np_exp(-0.5 * ((xint-fmodel)/eobs)**2)
-        return int_tmp
-
-
-    def swap_pars(self, pars):
-        '''
-        '''
-        Amax = -99
-        aamax = 0
-        for aa in range(self.Na):
-            if pars['A%d'%aa]>Amax:
-                Amax = pars['A%d'%aa]
-                aamax = aa
-        if aamax>0:
-            Amax2 = pars['A%d'%(aamax-1)]
-            pars['A%d'%(aamax-1)] = Amax
-            pars['A%d'%aamax] = Amax2
-        return pars
-
-
-    def swap_pars_inv(self, pars):
-        '''
-        '''
-        Amax = -99
-        aamax = self.Na-1
-        for aa in range(self.Na):
-            if pars['A%d'%aa]>Amax:
-                Amax = pars['A%d'%aa]
-                aamax = aa
-        if aamax<self.Na-1:
-            Amax2 = pars['A%d'%(aamax+1)]
-            pars['A%d'%(aamax+1)] = Amax
-            pars['A%d'%aamax] = Amax2
-        return pars
-
-
-    def lnprob_emcee(self, pos, pars, fy:float, ey:float, wht:float, f_fir:bool, f_chind:bool=True, SNlim:float=1.0, f_scale:bool=False, 
-        lnpreject=-np.inf, f_like:bool=False, flat_prior:bool=False, gauss_prior:bool=True, f_val:bool=True, nsigma:float=1.0, out=None,
-        f_prior_sfh=False, alpha_sfh_prior=100, norder_sfh_prior=3):
-        '''
-        Parameters
-        ----------
-        f_chind : bool
-            If true, includes non-detection in likelihood calculation.
-        lnpreject : 
-            A replaced value when lnprob gets -inf value.
-        flat_prior : 
-            Assumes flat prior for Mdyn. Used only when MB.f_Mdyn==True.
-        gauss_prior : 
-            Assumes gaussian prior for Mdyn. Used only when MB.f_Mdyn==True.
-        pos : 
-            This is a critical parameter, to make use of EMCEE, while keeping pars a dictionary obtained by lmfit.
-            This is pos of sampler.run_mcmc(pos, self.nmc, progress=True).
-        out : 
-            Just for keywords.
-
-        Returns:
-        --------
-        If f_like, log Likelihood. Else, log Posterior prob.
-        '''
-        if f_val:
-            vals = pars
-        else:
-            vals = pars.valuesdict()
-        if self.mb.ferr == 1:
-            logf = vals['logf']
-        else:
-            logf = -np.inf
-
-        if False:
-            # Checking multiple peak model
-            if self.mb.SFH_FORM != -99 and self.mb.npeak>1:
-                for aa in range(0,self.mb.npeak-1,1):
-                    if vals['A'+str(aa)] > vals['A'+str(aa+1)]:
-                        return lnpreject
-
-        # Check range:
-        if not out==None:
-            ii = 0
-            for key in out.params:
-                if out.params[key].vary:
-                    cmin = out.params[key].min
-                    cmax = out.params[key].max
-                    if pos[ii]<cmin or pos[ii]>cmax or np.isnan(pos[ii]):
-                        return lnpreject
-                    vals[key].value = pos[ii]
-                    ii += 1
-
-        resid, model = self.residual(vals, fy, ey, wht, f_fir, out=True, f_val=f_val, f_penlize=False)
-
-        con_res = (model>=0) & (wht>0) & (fy>0) & (ey>0)
-        sig_con = np.sqrt(ey[con_res]**2 + model[con_res]**2 * np_exp(2 * logf))
-        chi_nd = 0.0
-
-        con_up = (ey>0) & (fy/ey<=SNlim)
-        if f_chind and len(fy[con_up])>0:
-            x_erf = (ey[con_up]/SNlim - model[con_up]) / (np.sqrt(2) * ey[con_up]/SNlim)
-            f_erf = erf(x_erf)
-            if np.min(f_erf) <= -1:
-                #return lnpreject
-                lnlike = -0.5 * (np.sum(resid[con_res]**2 + np_log(2 * 3.14 * sig_con**2)))
-            else:
-                chi_nd = np.sum( np_log(np.sqrt(np.pi / 2) * ey[con_up]/SNlim * (1 + f_erf)) )
-            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np_log(2 * 3.14 * sig_con**2)) - 2 * chi_nd)
-        else:
-            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np_log(2 * 3.14 * sig_con**2)))
-
-        # Scale likeligood; Do not make this happen yet.
-        if f_scale:
-            if self.scale == 1:
-                self.scale = np.abs(lnlike) * 0.001
-                print('scale is set to',self.scale)
-            lnlike += self.scale
-
-        if np.isinf(np.abs(lnlike)):
-            print('Error in lnlike')
-            return lnpreject
-
-        # If no prior, return log likeligood.
-        if f_like:
-            return lnlike
-
-        # Prior
-        respr = 0
-
-        if self.mb.f_Mdyn:
-            # Prior from dynamical mass:
-            if gauss_prior and self.gauss_Mdyn == None:
-                self.gauss_Mdyn = stats.norm(self.mb.logMdyn, self.mb.elogMdyn)
-            #logMtmp = self.get_mass(vals)
-            logMtmp = self.mb.logMtmp
-            #print(logMtmp)
-            if flat_prior:
-                if logMtmp > self.mb.logMdyn + self.mb.elogMdyn * nsigma:
-                    #pars = self.swap_pars(pars)
-                    #print(logMtmp, self.mb.logMdyn + self.mb.elogMdyn)
-                    return lnpreject
-                else:
-                    respr += 0
-            elif gauss_prior:
-                if logMtmp > self.mb.logMdyn + self.mb.elogMdyn * nsigma:
-                    #pars = self.swap_pars(pars)
-                    #return lnpreject
-                    pass
-                #elif logMtmp < self.mb.logMdyn - self.mb.elogMdyn * nsigma:
-                #    pars = self.swap_pars_inv(pars)
-                #    return lnpreject
-                p_gauss = self.gauss_Mdyn.pdf(logMtmp) #/ self.gauss_cnst
-                respr += np_log(p_gauss)
-
-        # Prior for redshift:
-        if self.mb.fzmc == 1:
-            zprior = self.mb.z_prior
-            prior = self.mb.p_prior
-
-            nzz = np.argmin(np.abs(zprior-vals['zmc']))
-            # For something unacceptable;
-            if nzz<0 or prior[nzz]<=0:
-                print('z Posterior unacceptable.')
-                return lnpreject
-            else:
-                respr += np_log(prior[nzz])
-
-        # Prior for SFH;
-        if f_prior_sfh:
-            respr += self.get_sfh_prior(vals, norder=norder_sfh_prior, alpha=alpha_sfh_prior)
-
-        lnposterior = lnlike + respr
-        if not np.isfinite(lnposterior):
-            print('Posterior unacceptable.')
-            return lnpreject
-
-        return lnposterior
-
-
-    def get_sfh_prior(self, vals, norder=3, alpha=100.0):
-        '''
-        Fit SFH with an n-order polynomial and reflect the residual to the prior.
-        Fit should be done in log-log space.
-        '''
-        t = np.log10(self.mb.age)
-        y = []
-        vary = []
-        for aa in range(self.Na):
-            y.append(vals['A%d'%aa])
-            vary.append(vals['A%d'%aa].vary)
-        y = np.asarray(y)
-
-        con = (vary)
-        z = np.polyfit(t[con], y[con], norder)
-        yfit = np.polyval(z,t[con])
-        yres = (y[con] - yfit)
-
-        if False:
-            import matplotlib.pyplot as plt
-            plt.plot(t,y)
-            plt.plot(t[con],yfit)
-            plt.show()
-            hoge
-
-        lnprior = -0.5 * (np.sum(yres**2) * alpha)
-
-        return lnprior
\ No newline at end of file
diff --git a/build/lib/gsf/posterior_nested.py b/build/lib/gsf/posterior_nested.py
deleted file mode 100644
index 3ba1761..0000000
--- a/build/lib/gsf/posterior_nested.py
+++ /dev/null
@@ -1,239 +0,0 @@
-import numpy as np
-import sys
-import scipy.integrate as integrate
-from scipy.integrate import cumtrapz
-from scipy import special,stats
-
-from .function import *
-
-class Post_nested:
-    '''
-    #####################
-    # Function for MCMC
-    #####################
-    '''
-    def __init__(self, mainbody, params=None):
-        self.mb = mainbody
-        self.params = params
-
-    def residual(self, pars, fy, ey, wht, f_fir=False, out=False):
-        '''
-        Parameters
-        ----------
-        out   : model as second output. For lnprob func.
-        f_fir : Bool. If dust component is on or off.
-
-        Returns
-        -------
-        residual of model and data.
-
-        '''
-
-        vals = pars #.valuesdict()
-        model, x1 = self.mb.fnc.get_template(vals)
-
-        if self.mb.f_dust:
-            model_dust, x1_dust = self.mb.fnc.tmp04_dust(vals)
-            n_optir = len(model)
-
-            # Add dust flux to opt/IR grid.
-            model[:] += model_dust[:n_optir]
-
-            # then append only FIR flux grid.
-            model = np.append(model,model_dust[n_optir:])
-            x1 = np.append(x1,x1_dust[n_optir:])
-
-        if self.mb.ferr:
-            try:
-                f = vals['f']
-            except:
-                f = 0
-        else:
-            f = 0 # temporary... (if f is param, then take from vals dictionary.)
-
-        #con_res = (model>0) & (wht>0) #& (ey>0)
-        sig = np.sqrt(1./wht + (f**2*model**2))
-
-        if fy is None:
-            print('Data is none')
-            resid = model #[con_res]
-        else:
-            resid = (model - fy) / sig
-
-        if not out:
-            return resid # i.e. residual/sigma. Because is_weighted = True.
-        else:
-            return resid, model # i.e. residual/sigma. Because is_weighted = True.
-
-
-    def get_dict(self, pars):
-
-        ii = 0
-        for key in self.params:
-            if self.params[key].vary:
-                self.params[key].value = pars[ii]
-                ii += 1
-        return self.params
-
-
-    def residual_nest(self, pars, fy, ey, wht, f_fir=False, out=False):
-        '''
-        Parameters
-        ----------
-        out   : model as second output. For lnprob func.
-        f_fir : Bool. If dust component is on or off.
-
-        Returns
-        -------
-        residual of model and data.
-
-        '''
-
-        vals = self.get_dict(pars)
-        model, x1 = self.mb.fnc.get_template(vals)
-
-        if self.mb.f_dust:
-            model_dust, x1_dust = self.mb.fnc.tmp04_dust(vals)
-            n_optir = len(model)
-
-            # Add dust flux to opt/IR grid.
-            model[:] += model_dust[:n_optir]
-            # then append only FIR flux grid.
-            model = np.append(model,model_dust[n_optir:])
-            x1 = np.append(x1,x1_dust[n_optir:])
-
-        if self.mb.ferr:
-            try:
-                f = vals['f']
-            except:
-                f = 0
-        else:
-            f = 0 # temporary... (if f is param, then take from vals dictionary.)
-
-        #con_res = (model>0) & (wht>0) #& (ey>0)
-        sig = np.sqrt(1./wht + (f**2*model**2))
-
-        if fy is None:
-            print('Data is none')
-            resid = model #[con_res]
-        else:
-            resid = (model - fy) / sig
-
-        if not out:
-            return resid # i.e. residual/sigma. Because is_weighted = True.
-        else:
-            return resid, model # i.e. residual/sigma. Because is_weighted = True.
-
-
-    def func_tmp(self, xint, eobs, fmodel):
-        '''
-        A function used for chi2 calculation for non-detection in lnprob.
-        '''
-        int_tmp = np.exp(-0.5 * ((xint-fmodel)/eobs)**2)
-        return int_tmp
-
-
-    def prior_transform(self, pars):
-        """
-        A function defining the tranform between the parameterisation in the unit hypercube
-        to the true parameters.
-
-        Args:
-            theta (tuple): a tuple containing the parameters.
-            
-        Returns:
-            tuple: a new tuple or array with the transformed parameters.
-        """
-
-        #prior = np.zeros(len(pars),'float')
-        if True:
-            ii = 0
-            for key in self.params:
-                if self.params[key].vary:
-                    cmin = self.params[key].min
-                    cmax = self.params[key].max
-                    cprim = pars[ii]
-                    pars[ii] = cprim*(cmax-cmin) + cmin
-                    ii += 1
-
-        '''
-        mmu = 0.     # mean of Gaussian prior on m
-        msigma = 10. # standard deviation of Gaussian prior on m
-        m = mmu + msigma*ndtri(mprime) # convert back to m
-        '''
-
-        return pars
-
-
-    def lnlike(self, pars, fy, ey, wht, f_fir, f_chind=True, SNlim=1.0, lnpreject=-np.inf):
-        '''
-        Returns
-        -------
-        log likelihood
-
-        Note:
-        =====
-        This is a copy from lnprob, with respr=0.
-
-        '''
-        vals = pars#.valuesdict()
-        if self.mb.ferr == 1:
-            f = vals['f']
-        else:
-            f = 0
-
-        resid, model = self.residual_nest(pars, fy, ey, wht, f_fir, out=True)
-        con_res = (model>=0) & (wht>0) & (fy>0) & (ey>0) # Instead of model>0; model>=0 is for Lyman limit where flux=0. This already exclude upper limit.
-        sig_con = np.sqrt(1./wht[con_res]+f**2*model[con_res]**2) # To avoid error message.
-        chi_nd = 0.0
-
-        con_up = (ey>0) & (fy/ey<=SNlim)
-        if f_chind and len(fy[con_up])>0:
-            x_erf = (ey[con_up]/SNlim - model[con_up]) / (np.sqrt(2) * ey[con_up]/SNlim)
-            f_erf = special.erf(x_erf)
-            if np.min(f_erf) <= -1:
-                return lnpreject
-            else:
-                chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey[con_up]/SNlim * (1 + f_erf)) )
-            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np.log(2 * 3.14 * sig_con**2)) - 2 * chi_nd)
-        else:
-            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np.log(2 * 3.14 * sig_con**2)))
-
-        return lnlike
-
-
-    def lnprob(self, pars, fy, ey, wht, f_fir, f_chind=True, SNlim=1.0, lnpreject=-np.inf):
-        '''
-        Returns:
-        ========
-        log posterior
-
-        '''
-
-        vals = pars #.valuesdict()
-        if self.mb.ferr == 1:
-            f = vals['f']
-        else:
-            f = 0
-
-        resid, model = self.residual(pars, fy, ey, wht, f_fir, out=True)
-        con_res = (model>=0) & (wht>0) & (fy>0) & (ey>0) # Instead of model>0; model>=0 is for Lyman limit where flux=0. This already exclude upper limit.
-        sig_con = np.sqrt(1./wht[con_res]+f**2*model[con_res]**2) # To avoid error message.
-        chi_nd = 0.0
-
-        con_up = (ey>0) & (fy/ey<=SNlim)
-        if f_chind and len(fy[con_up])>0:
-            x_erf = (ey[con_up]/SNlim - model[con_up]) / (np.sqrt(2) * ey[con_up]/SNlim)
-            f_erf = special.erf(x_erf)
-            if np.min(f_erf) <= -1:
-                return lnpreject
-            else:
-                chi_nd = np.sum( np.log(np.sqrt(np.pi / 2) * ey[con_up]/SNlim * (1 + f_erf)) )
-            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np.log(2 * 3.14 * sig_con**2)) - 2 * chi_nd)
-        else:
-            lnlike = -0.5 * (np.sum(resid[con_res]**2 + np.log(2 * 3.14 * sig_con**2)))
-
-        respr = 0 # Flat prior...
-        lnposterior = lnlike + respr
-
-        return lnposterior
diff --git a/build/lib/gsf/writing.py b/build/lib/gsf/writing.py
deleted file mode 100644
index 0c93fc4..0000000
--- a/build/lib/gsf/writing.py
+++ /dev/null
@@ -1,420 +0,0 @@
-import numpy as np
-from astropy.io import fits
-from astropy import units as u
-import asdf
-import gsf
-from .function import filconv, calc_Dn4
-
-
-def get_param(self, res, fitc, tcalc=1., burnin=-1):
-    '''
-    Purpose
-    -------
-    Write a parameter file.
-
-    '''
-    print('##########################')
-    print('### Writing parameters ###')
-    print('##########################')
-    lib_all = self.lib_all
-
-    # Those are from redshiftfit;
-    zrecom = self.zgal
-    Czrec0 = self.Cz0
-    Czrec1 = self.Cz1
-    Czrec2 = self.Cz2
-
-    try:
-        z_cz = self.z_cz_prev
-        scl_cz0 = self.scl_cz0
-        scl_cz1 = self.scl_cz1
-        scl_cz2 = self.scl_cz2
-    except: # When redshiftfit is skipped.
-        z_cz = np.asarray([self.zgal,self.zgal,self.zgal])
-        scl_cz0 = np.asarray([self.Cz0,self.Cz0,self.Cz0])
-        scl_cz1 = np.asarray([self.Cz1,self.Cz1,self.Cz1])
-        scl_cz2 = np.asarray([self.Cz2,self.Cz2,self.Cz2])
-
-    tau0 = self.tau0
-    ID0 = self.ID
-    age = self.age
-    Zall = self.Zall
-
-    fnc  = self.fnc 
-    bfnc = self.bfnc 
-
-    DIR_TMP = self.DIR_TMP
-
-    fil_path = self.DIR_FILT
-    nmc  = self.nmc
-    ndim = self.ndim
-    nwalker = self.nwalk
-
-    #samples = res.chain[:, :, :].reshape((-1, ndim))
-    samples = res.flatchain
-    if burnin < 0:
-         burnin = int(samples.shape[0]/2.)
-
-    ##############################
-    # Best parameters
-    Amc = np.zeros((len(age),3), dtype='float')
-    Ab = np.zeros(len(age), dtype='float')
-    Zmc = np.zeros((len(age),3), dtype='float')
-    Zb = np.zeros(len(age), dtype='float')
-    NZbest = np.zeros(len(age), dtype='int')
-    AGEmc = np.zeros((len(age),3), dtype='float')
-    TAUmc = np.zeros((len(age),3), dtype='float')
-    if self.f_dust:
-        Mdustmc = np.zeros(3, dtype='float')
-        nTdustmc= np.zeros(3, dtype='float')
-        Tdustmc = np.zeros(3, dtype='float')
-    
-    # ASDF;
-    af = self.af
-    sedpar = af['ML']
-
-    ms = np.zeros(len(age), dtype='float')
-    try:
-        msmc0 = np.zeros(len(res.flatchain['A%d'%self.aamin[0]][burnin:]), dtype='float')
-    except:
-        msmc0 = np.zeros(len(res.flatchain['Av'][burnin:]), dtype='float')
-
-    for aa in range(len(age)):
-        try:
-            Ab[aa] = res.params['A'+str(aa)].value
-            Amc[aa,:] = np.percentile(res.flatchain['A'+str(aa)][burnin:], [16,50,84])
-        except:
-            Ab[aa] = -99
-            Amc[aa,:] = [-99,-99,-99]
-            pass
-        if aa == 0 or self.ZEVOL:
-            try:
-                Zb[aa] = res.params['Z'+str(aa)].value
-                Zmc[aa,:] = np.percentile(res.flatchain['Z'+str(aa)][burnin:], [16,50,84])
-            except:
-                ZFIX = self.ZFIX
-                Zb[aa] = ZFIX
-                Zmc[aa,:] = [ZFIX, ZFIX, ZFIX]
-        else:
-            Zb[aa] = Zb[0]
-            Zmc[aa,:] = Zmc[0,:]
-
-        if self.SFH_FORM == -99:
-            NZbest = bfnc.Z2NZ(Zb[aa])
-            ms[aa] = sedpar['ML_' +  str(NZbest)][aa]
-            try:
-                msmc0[:] += 10**res.flatchain['A' + str(aa)][burnin:] * ms[aa]
-            except:
-                pass
-        else:
-            taub = res.params['TAU'+str(aa)].value
-            ageb = res.params['AGE'+str(aa)].value
-            NZbest,NTAU,NAGE = bfnc.Z2NZ(Zb[aa],taub,ageb)
-            ms[aa] = sedpar['ML_%d_%d'%(NZbest, NTAU)][NAGE]
-            try:
-                msmc0[:] += 10**res.flatchain['A' + str(aa)][burnin:] * ms[aa]
-            except:
-                pass
-
-            # Do debug..
-            try:
-                AGEmc[aa,:] = np.percentile(res.flatchain['AGE'+str(aa)][burnin:], [16,50,84])
-            except:
-                AGEFIX = self.AGEFIX
-                AGEmc[aa,:] = [AGEFIX, AGEFIX, AGEFIX]
-
-            try:
-                TAUmc[aa,:] = np.percentile(res.flatchain['TAU'+str(aa)][burnin:], [16,50,84])
-            except:
-                TAUFIX = self.TAUFIX
-                TAUmc[aa,:] = [TAUFIX, TAUFIX, TAUFIX]
-
-    msmc = np.percentile(msmc0, [16,50,84])
-    try:
-        Avb = res.params['Av'].value
-        Avmc = np.percentile(res.flatchain['Av'][burnin:], [16,50,84])
-    except:
-        Avb = self.AVFIX
-        Avmc = [self.AVFIX,self.AVFIX,self.AVFIX]
-
-    AAvmc = [Avmc]
-    if self.fzmc:
-        zmc = np.percentile(res.flatchain['zmc'][burnin:], [16,50,84])
-    else:
-        zmc = z_cz
-
-    if self.ferr:
-        logf = np.percentile(res.flatchain['logf'][burnin:], [16,50,84])
-    else:
-        logf = [-99,-99,-99]
-
-    AA_tmp = np.zeros(len(age), dtype='float')
-    ZZ_tmp = np.zeros(len(age), dtype='float')
-    NZbest = np.zeros(len(age), dtype='int')
-
-    ############
-    # Get SN.
-    ############
-    nrs, lams, fsp, esp = self.data['spec_obs']['NR'], self.data['spec_obs']['x'], self.data['spec_obs']['fy'], self.data['spec_obs']['ey']
-
-    consp = (nrs<10000) & (lams/(1.+zrecom)>3600) & (lams/(1.+zrecom)<4200) & (esp>0)
-    NSN = len(fsp[consp])
-    if NSN>0:
-        SN = np.median((fsp/esp)[consp])
-    else:
-        SN = 0
-
-    ######################
-    # Write in Fits table.
-    ######################
-    # Header
-    prihdr = fits.Header()
-    prihdr['ID']     = ID0
-    prihdr['Cz0']    = Czrec0
-    prihdr['Cz1']    = Czrec1
-    prihdr['Cz2']    = Czrec2
-    prihdr['z']      = zrecom
-    prihdr['zmc']    = zmc[1]
-    prihdr['SN']     = SN
-    prihdr['nSN']    = NSN
-    prihdr['NDIM']   = ndim
-    prihdr['tcalc']  = (tcalc, 'in second')
-    prihdr['chi2']   = fitc[0]
-    prihdr['chi2nu'] = fitc[1]
-    prihdr['bic'] = res.bic
-    prihdr['nmc'] = nmc
-    prihdr['nwalker'] = nwalker
-    prihdr['version'] = gsf.__version__
-    prihdu = fits.PrimaryHDU(header=prihdr)
-
-    # Data
-    col01 = []
-    for aa in range(len(age)):
-        col50 = fits.Column(name='A'+str(aa), format='E', unit='', array=Amc[aa][:])
-        col01.append(col50)
-
-    for aa in range(len(AAvmc)):
-        col50 = fits.Column(name='Av'+str(aa), format='E', unit='mag', array=AAvmc[aa][:])
-        col01.append(col50)
-
-    for aa in range(len(Zmc)):
-        col50 = fits.Column(name='Z'+str(aa), format='E', unit='logZsun', array=Zmc[aa][:])
-        col01.append(col50)
-
-    for aa in range(len(AGEmc)):
-        col50 = fits.Column(name='AGE'+str(aa), format='E', unit='logGyr', array=AGEmc[aa][:])
-        col01.append(col50)
-        
-    for aa in range(len(TAUmc)):
-        col50 = fits.Column(name='TAU'+str(aa), format='E', unit='logGyr', array=TAUmc[aa][:])
-        col01.append(col50)
-        
-    if self.f_dust:
-        Mdust_per_temp = self.af['spec_dust']['Mdust']
-        if self.DT0 == self.DT1 or self.DT0 + self.dDT >= self.DT1:
-            nTdustmc[:] = [0,0,0]
-            Tdustmc[:] = [self.DT0,self.DT0,self.DT0]
-        else:
-            if not self.TDUSTFIX == None:
-                nTdustmc[:] = [self.NTDUST,self.NTDUST,self.NTDUST]
-            else:
-                nTdustmc[:] = np.percentile(res.flatchain['TDUST'][burnin:], [16,50,84])
-            Tdustmc[:] = self.DT0 + self.dDT * nTdustmc[:]
-
-        Mdustmc[:] = np.percentile(res.flatchain['MDUST'][burnin:], [16,50,84])
-        col50 = fits.Column(name='ADUST', format='E', unit='Msun', array=Mdustmc[:])
-        col01.append(col50)
-
-        # Then M2Light ratio;
-        Mdustmc[0] += np.log10(Mdust_per_temp[int(nTdustmc[0])])
-        Mdustmc[1] += np.log10(Mdust_per_temp[int(nTdustmc[1])])
-        Mdustmc[2] += np.log10(Mdust_per_temp[int(nTdustmc[2])])
-
-        col50 = fits.Column(name='MDUST', format='E', unit='Msun', array=Mdustmc[:])
-        col01.append(col50)
-        col50 = fits.Column(name='nTDUST', format='E', unit='', array=nTdustmc[:])
-        col01.append(col50)
-        col50 = fits.Column(name='TDUST', format='E', unit='K', array=Tdustmc[:])
-        col01.append(col50)
-
-    if self.fneb:
-        Anebmc = np.zeros(3, dtype=float)
-        logUmc = np.zeros(3, dtype=float)
-        Anebmc[:] = np.percentile(res.flatchain['Aneb'][burnin:], [16,50,84])
-        if not self.logUFIX == None:
-            logUmc[:] = [self.logUFIX,self.logUFIX,self.logUFIX]
-        else:
-            logUmc[:] = np.percentile(res.flatchain['logU'][burnin:], [16,50,84])
-        col50 = fits.Column(name='Aneb', format='E', unit='', array=Anebmc[:])
-        col01.append(col50)
-        col50 = fits.Column(name='logU', format='E', unit='', array=logUmc[:])
-        col01.append(col50)
-
-    # zmc
-    col50 = fits.Column(name='zmc', format='E', unit='', array=zmc[:])
-    col01.append(col50)
-
-    # Mass
-    col50 = fits.Column(name='ms', format='E', unit='Msun', array=msmc[:])
-    col01.append(col50)
-
-    # zmc
-    col50 = fits.Column(name='z_cz', format='E', unit='', array=z_cz[:])
-    col01.append(col50)
-
-    # Chi
-    col50 = fits.Column(name='chi', format='E', unit='', array=fitc[:])
-    col01.append(col50)
-
-    # C0 scale
-    col50 = fits.Column(name='Cscale0', format='E', unit='', array=scl_cz0[:])
-    col01.append(col50)
-
-    # C1 scale
-    col50 = fits.Column(name='Cscale1', format='E', unit='', array=scl_cz1[:])
-    col01.append(col50)
-
-    # C2 scale
-    col50 = fits.Column(name='Cscale2', format='E', unit='', array=scl_cz2[:])
-    col01.append(col50)
-
-    col50 = fits.Column(name='logf', format='E', unit='', array=logf)
-    col01.append(col50)
-
-    colms = fits.ColDefs(col01)
-    dathdu = fits.BinTableHDU.from_columns(colms)
-    hdu = fits.HDUList([prihdu, dathdu])
-    hdu.writeto(self.DIR_OUT + 'summary_' + ID0 + '.fits', overwrite=True)
-
-
-
-def get_index(mmax=300):
-    '''
-    Purpose
-    -------
-    Retrieve spectral indices from each realization.
-    '''
-
-    if nmc<mmax:
-        mmax = int(nmc/2.)
-
-    # RF color
-    uv = np.zeros(int(mmax), dtype='float')
-    bv = np.zeros(int(mmax), dtype='float')
-    vj = np.zeros(int(mmax), dtype='float')
-    zj = np.zeros(int(mmax), dtype='float')
-
-    # Lick indeces
-    Dn4  = np.zeros(int(mmax), dtype='float')
-    Mgb  = np.zeros(int(mmax), dtype='float')
-    Fe52 = np.zeros(int(mmax), dtype='float')
-    Fe53 = np.zeros(int(mmax), dtype='float')
-    Mg1  = np.zeros(int(mmax), dtype='float')
-    Mg2  = np.zeros(int(mmax), dtype='float')
-    G4300= np.zeros(int(mmax), dtype='float')
-    NaD  = np.zeros(int(mmax), dtype='float')
-    Hb   = np.zeros(int(mmax), dtype='float')
-
-    for mm in range(0,mmax,1):
-        rn = np.random.randint(len(res.flatchain['A0']))
-
-        for aa in range(len(age)):
-            fit_params['A'+str(aa)].value = res.flatchain['A%d'%(aa)][rn]
-            try:
-                fit_params['Z'+str(aa)].value = res.flatchain['Z%d'%(aa)][rn]
-            except:
-                pass
-        fit_params['Av'].value = res.flatchain['Av'][rn]
-
-        model2, xm_tmp = fnc.tmp04(fit_params, zrecom, lib_all)
-
-        # not necessary here.
-        if self.f_dust:
-            model2_dust, xm_tmp_dust = fnc.tmp04_dust(ID0, par_tmp, zrecom, lib_all, tau0=tau0)
-            model2 = np.append(model2,model2_dust)
-            xm_tmp = np.append(xm_tmp,xm_tmp_dust)
-
-        Dn4[mm]= calc_Dn4(xm_tmp, model2, zrecom) # Dust attenuation is not included?
-        lmrest = xm_tmp / (1. + zrecom)
-        band0  = ['u','b','v','j','sz']
-        try:
-            lmconv,fconv = filconv(band0, lmrest, model2, fil_path) # model2 in fnu
-            uv[mm] = -2.5*np.log10(fconv[0]/fconv[2])
-            bv[mm] = -2.5*np.log10(fconv[1]/fconv[2])
-            vj[mm] = -2.5*np.log10(fconv[2]/fconv[3])
-            zj[mm] = -2.5*np.log10(fconv[4]/fconv[3])
-        except:
-            uv[mm] = 0
-            bv[mm] = 0
-            vj[mm] = 0
-            zj[mm] = 0
-
-    conper = (Dn4>-99) #(Dn4>0)
-    Dnmc = np.percentile(Dn4[conper], [16,50,84])
-    uvmc = np.percentile(uv[conper], [16,50,84])
-    bvmc = np.percentile(bv[conper], [16,50,84])
-    vjmc = np.percentile(vj[conper], [16,50,84])
-    zjmc = np.percentile(zj[conper], [16,50,84])
-
-    Mgbmc  = np.percentile(Mgb[conper], [16,50,84])
-    Fe52mc = np.percentile(Fe52[conper], [16,50,84])
-    Fe53mc = np.percentile(Fe53[conper], [16,50,84])
-    G4300mc= np.percentile(G4300[conper], [16,50,84])
-    NaDmc  = np.percentile(NaD[conper], [16,50,84])
-    Hbmc   = np.percentile(Hb[conper], [16,50,84])
-    Mg1mc  = np.percentile(Mg1[conper], [16,50,84])
-    Mg2mc  = np.percentile(Mg2[conper], [16,50,84])
-
-
-    # Dn4000
-    col50 = fits.Column(name='Dn4', format='E', unit='', array=Dnmc[:])
-    col01.append(col50)
-
-
-    # U-V
-    col50 = fits.Column(name='uv', format='E', unit='mag', array=uvmc[:])
-    col01.append(col50)
-
-    # V-J
-    col50 = fits.Column(name='vj', format='E', unit='mag', array=vjmc[:])
-    col01.append(col50)
-
-    # B-V
-    col50 = fits.Column(name='bv', format='E', unit='mag', array=bvmc[:])
-    col01.append(col50)
-
-    # z-J
-    col50 = fits.Column(name='zj', format='E', unit='mag', array=zjmc[:])
-    col01.append(col50)
-
-    # Mgb
-    col50 = fits.Column(name='Mgb', format='E', unit='', array=Mgbmc[:])
-    col01.append(col50)
-
-    # Fe5270
-    col50 = fits.Column(name='Fe5270', format='E', unit='', array=Fe52mc[:])
-    col01.append(col50)
-
-    # Fe5335
-    col50 = fits.Column(name='Fe5335', format='E', unit='', array=Fe53mc[:])
-    col01.append(col50)
-
-    # G4300
-    col50 = fits.Column(name='G4300', format='E', unit='', array=G4300mc[:])
-    col01.append(col50)
-
-    # NaD
-    col50 = fits.Column(name='NaD', format='E', unit='', array=NaDmc[:])
-    col01.append(col50)
-
-    # Hb
-    col50 = fits.Column(name='Hb', format='E', unit='', array=Hbmc[:])
-    col01.append(col50)
-
-    # Mg1
-    col50 = fits.Column(name='Mg1', format='E', unit='', array=Mg1mc[:])
-    col01.append(col50)
-
-    # Mg2
-    col50 = fits.Column(name='Mg2', format='E', unit='', array=Mg2mc[:])
-    col01.append(col50)
diff --git a/build/lib/gsf/zfit.py b/build/lib/gsf/zfit.py
deleted file mode 100644
index 63f89fd..0000000
--- a/build/lib/gsf/zfit.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import numpy as np
-import sys
-from lmfit import Model, Parameters, minimize, fit_report, Minimizer
-import scipy.interpolate as interpolate
-import matplotlib.pyplot as plt
-from .function import check_line_cz_man
-
-
-def lnprob_cz(pars, zprior, prior, zliml, zlimu, args, kwargs):
-    '''
-    '''
-    resid = residual_z(pars, *args, **kwargs) # i.e. (data - model) * wht
-    z = pars['z']
-    s_z = 1 #pars['f_cz']
-    resid *= 1/s_z
-    resid *= resid
-
-    if False:
-        nzz = np.argmin(np.abs(zprior-z))
-
-        # For something unacceptable;
-        if nzz<0 or zprior[nzz]<zliml or zprior[nzz]>zlimu or prior[nzz]<=0:
-            return -np.inf
-        else:
-            respr = np.log(prior[nzz])
-            resid += np.log(2 * np.pi * s_z**2)
-            return -0.5 * np.sum(resid) + respr
-    else:
-        resid += np.log(2 * np.pi * s_z**2)
-        return -0.5 * np.sum(resid)
-
-
-def residual_z(pars, xm_tmp, fm_tmp, xobs, fobs, eobs, NR, NRbb_lim=10000, include_photometry=True, f_line_check=False):
-    '''
-    '''
-    vals = pars.valuesdict()
-    z = vals['z']
-    Cz0s = vals['Cz0']
-    Cz1s = vals['Cz1']
-    Cz2s = vals['Cz2']
-
-    xm_s = xm_tmp * (1+z)
-    fint = interpolate.interp1d(xm_s, fm_tmp, kind='nearest', fill_value="extrapolate")
-    # @@@ Maybe for future consideration.
-    # fint = interpolate.interp1d(xm_s, fm_tmp, kind='linear', fill_value="extrapolate")
-    fm_s = fint(xobs)
-
-    con0 = (NR<1000)
-    fy0  = fobs[con0] * Cz0s
-    ey0  = eobs[con0] * Cz0s
-    con1 = (NR>=1000) & (NR<2000)
-    fy1  = fobs[con1] * Cz1s
-    ey1  = eobs[con1] * Cz1s
-    con2 = (NR>=2000) & (NR<NRbb_lim)
-    fy2  = fobs[con2] * Cz2s
-    ey2  = eobs[con2] * Cz2s
-    con_bb = (NR>=NRbb_lim) # BB
-    fy_bb  = fobs[con_bb]
-    ey_bb  = eobs[con_bb]
-
-    fy01 = np.append(fy0,fy1)
-    ey01 = np.append(ey0,ey1)
-    fy02 = np.append(fy01,fy2)
-    ey02 = np.append(ey01,ey2)
-
-    if include_photometry and len(fy_bb)>0:
-        fcon = np.append(fy02,fy_bb)
-        eycon = np.append(ey02,ey_bb)
-        wht = 1./np.square(eycon)
-    else:
-        fcon = fy02
-        eycon = ey02
-        wht = 1./np.square(eycon)
-
-    if f_line_check:
-        try:
-            wht2, ypoly = check_line_cz_man(fcon, xobs, wht, fm_s, z)
-        except:
-            wht2 = wht
-    else:
-        wht2 = wht
-        
-    if fobs is None:
-        print('Data is none')
-        return fm_s
-    else:
-        return (fm_s - fcon) * np.sqrt(wht2) # i.e. residual/sigma
-
-
-def check_redshift(fobs, eobs, xobs, fm_tmp, xm_tmp, zbest, zprior, prior, NR, zliml, zlimu, \
-    nmc_cz=100, nwalk_cz=10, nthin=5, f_line_check=False, f_vary=True, NRbb_lim=10000, include_photometry=True):
-    '''
-    Purpose
-    -------
-    Fit observed flux with a template to get redshift probability.
-
-    Parameters
-    ----------
-    zbest : 
-        Initial value for redshift.
-    zprior : 
-        Redshift grid for prior.
-    prior : 
-        Prior for redshift determination. E.g., Eazy z-probability.
-    zliml : 
-        Lowest redshift for fitting range.
-    zlimu : 
-        Highest redshift for fitting range.
-    f_vary : bool
-        If want to fix redshift.
-    fm_tmp :
-        Template spectrum at RF.
-    xm_tmp :
-        Template spectrum at RF.
-    fobs : 
-        Observed spectrum. (Already scaled with Cz0prev.)
-    eobs : 
-        Observed spectrum. (Already scaled with Cz0prev.)
-    xobs : 
-        Observed spectrum. (Already scaled with Cz0prev.)
-
-
-    Returns
-    -------
-    res_cz :
-
-    fitc_cz :
-
-    '''
-    if zliml == None or zlimu == None:
-        print('z range is not set for the z-fit function. Exiting.')
-        print('Specify `ZMCMIN` and `ZMCMIN` in your input file.')
-        sys.exit()
-
-    fit_par_cz = Parameters()
-    fit_par_cz.add('z', value=zbest, min=zliml, max=zlimu, vary=f_vary)
-    fit_par_cz.add('Cz0', value=1, min=0.5, max=1.5)
-    fit_par_cz.add('Cz1', value=1, min=0.5, max=1.5)
-    fit_par_cz.add('Cz2', value=1, min=0.5, max=1.5)
-
-    # Get Best fit
-    args_res = (xm_tmp, fm_tmp, xobs, fobs, eobs, NR)
-    kwargs_res = {'include_photometry':include_photometry, 'f_line_check':f_line_check, 'NRbb_lim':NRbb_lim}
-
-    out_cz  = minimize(residual_z, fit_par_cz, args=args_res, method='nelder')
-    keys = fit_report(out_cz).split('\n')
-    for key in keys:
-        if key[4:7] == 'chi':
-            skey = key.split(' ')
-            csq = float(skey[14])
-        if key[4:7] == 'red':
-            skey = key.split(' ')
-            rcsq = float(skey[7])
-
-    fitc_cz = [csq, rcsq] # Chi2, Reduced-chi2
-
-    mini_cz = Minimizer(lnprob_cz, out_cz.params, (zprior, prior, zliml, zlimu, args_res, kwargs_res))
-    res_cz = mini_cz.emcee(burn=int(nmc_cz/2), steps=nmc_cz, thin=nthin, nwalkers=nwalk_cz, params=out_cz.params, is_weighted=True)
-
-    return res_cz, fitc_cz
-
-
-def get_chi2(zz_prob, fy_cz, ey_cz, x_cz, fm_tmp, xm_tmp, file_zprob, rms_lim=1e4):
-    '''
-    zz_prob : float array
-        redshift array for fit.
-    fy_cz, ey_cz, x_cz : 
-        observed values.
-    fm_tmp, xm_tmp :
-        template.
-    file_zprob : str
-        output file
-    '''
-    mask = (ey_cz<rms_lim)
-    prob_cz = np.zeros(len(zz_prob), float)
-    fw = open(file_zprob, 'w')
-    fw.write('# z p(z)\n')
-    for ii in range(len(zz_prob)):
-        z = zz_prob[ii]
-        xm_s = xm_tmp * (1+z)
-        fint = interpolate.interp1d(xm_s, fm_tmp, kind='nearest', fill_value="extrapolate")
-        fm_s = fint(x_cz)
-
-        wht = 1./np.square(ey_cz)
-        # print(np.nansum((fm_s - fy_cz)**2 * (wht)))
-        lnprob_cz = -0.5 * np.nansum( np.square((fm_s - fy_cz)[mask] * np.sqrt(wht[mask])) ) # i.e. (residual/sigma)^2
-        prob_cz[ii] = np.exp(lnprob_cz)
-        fw.write('%.3f %.3e\n'%(z,prob_cz[ii]))
-        
-    fw.close()
diff --git a/dist/gsf-1.7.6.dev20+g1e4de8a.d20221210-py3.9.egg b/dist/gsf-1.7.6.dev20+g1e4de8a.d20221210-py3.9.egg
deleted file mode 100644
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diff --git a/gsf.egg-info/PKG-INFO b/gsf.egg-info/PKG-INFO
deleted file mode 100644
index 62ddb2b..0000000
--- a/gsf.egg-info/PKG-INFO
+++ /dev/null
@@ -1,21 +0,0 @@
-Metadata-Version: 2.1
-Name: gsf
-Version: 1.7.6.dev20+g1e4de8a.d20221210
-Summary: SED Fitting Code
-Home-page: https://github.com/mtakahiro
-Download-URL: https://github.com/
-Author: Takahiro Morishita
-Author-email: takahiro@ipac.caltech.edu
-License: IPAC
-Platform: UNKNOWN
-Classifier: Development Status :: 1 - Planning
-Classifier: Intended Audience :: Science/Research
-Classifier: Topic :: Scientific/Engineering :: Astronomy
-Requires: lmfit
-Requires: fsps
-Requires: emcee
-Requires: corner
-License-File: LICENSE
-
-UNKNOWN
-
diff --git a/gsf.egg-info/SOURCES.txt b/gsf.egg-info/SOURCES.txt
deleted file mode 100644
index 4d2f3e5..0000000
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@@ -1,633 +0,0 @@
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-gsf/writing.py
-gsf/zfit.py
-gsf.egg-info/PKG-INFO
-gsf.egg-info/SOURCES.txt
-gsf.egg-info/dependency_links.txt
-gsf.egg-info/not-zip-safe
-gsf.egg-info/top_level.txt
-gsf/tests/__init__.py
-gsf/tests/test_fitting.py
-gsf/tests/test_function.py
\ No newline at end of file
diff --git a/gsf.egg-info/dependency_links.txt b/gsf.egg-info/dependency_links.txt
deleted file mode 100644
index 8b13789..0000000
--- a/gsf.egg-info/dependency_links.txt
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/gsf.egg-info/not-zip-safe b/gsf.egg-info/not-zip-safe
deleted file mode 100644
index 8b13789..0000000
--- a/gsf.egg-info/not-zip-safe
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/gsf.egg-info/top_level.txt b/gsf.egg-info/top_level.txt
deleted file mode 100644
index 6936b2c..0000000
--- a/gsf.egg-info/top_level.txt
+++ /dev/null
@@ -1 +0,0 @@
-gsf

From 2ec92eea8812f85a2d07714b7e23e06116205cbd Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Sat, 10 Dec 2022 21:22:22 -0800
Subject: [PATCH 09/14] redshift parameterization update

---
 docs/_build/doctrees/auto/gsf.doctree   | Bin 918402 -> 920751 bytes
 docs/_build/doctrees/environment.pickle | Bin 70118 -> 70118 bytes
 docs/_build/html/auto/gsf.html          |  23 ++-
 docs/_build/html/searchindex.js         |   2 +-
 gsf/fitting.py                          |   6 +-
 gsf/function_class.py                   | 262 ++++--------------------
 gsf/maketmp_filt.py                     |  32 +--
 gsf/maketmp_z0.py                       |   2 +
 8 files changed, 79 insertions(+), 248 deletions(-)

diff --git a/docs/_build/doctrees/auto/gsf.doctree b/docs/_build/doctrees/auto/gsf.doctree
index c099233bde7848ace23b7e48e1993d97fff1a0e0..8fbfc7fe26a3596ec9562aadf115e5795dff87c8 100644
GIT binary patch
delta 119515
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diff --git a/docs/_build/html/auto/gsf.html b/docs/_build/html/auto/gsf.html
index 642d197..b988cda 100644
--- a/docs/_build/html/auto/gsf.html
+++ b/docs/_build/html/auto/gsf.html
@@ -756,7 +756,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dl class="simple">
 <dt>nprec<span class="classifier">int</span></dt><dd><p>Precision when redshift is refined.</p>
 </dd>
-<dt>f_apply_dust<span class="classifier">bool</span></dt><dd><p>Apply dust attenuation to nebular emission?</p>
+<dt>f_apply_dust<span class="classifier">bool</span></dt><dd><p>Apply dust attenuation to nebular emission.</p>
 </dd>
 <dt>EBVratio<span class="classifier">float</span></dt><dd><p>E(B-V)_neb / E(B-V)_st. 
 Useful table in <a class="reference external" href="https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf">https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf</a></p>
@@ -789,7 +789,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 </dd>
 </dl>
 <dl class="simple">
-<dt>xx<span class="classifier"></span></dt><dd><p>OBSERVED wavelength range at z=MB.zgal</p>
+<dt>nr, xx, yy<span class="classifier">float arrays</span></dt><dd><p>xx is OBSERVED wavelength at z=MB.zgal</p>
 </dd>
 </dl>
 </dd></dl>
@@ -814,6 +814,13 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dt class="sig sig-object py" id="gsf.function_class.Func.open_spec_fits">
 <span class="sig-name descname"><span class="pre">open_spec_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orig</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_neb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func.open_spec_fits" title="Permalink to this definition"></a></dt>
 <dd><p>Load template in obs range.</p>
+<dl>
+<dt>fall<span class="classifier">int</span></dt><dd><p>If 1, returns full spectra.</p>
+</dd>
+<dt>orig<span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bool</span></code></span></dt><dd><p>If True, returns the original spectra.</p>
+</dd>
+</dl>
+<p>float array</p>
 </dd></dl>
 
 <dl class="py method">
@@ -1015,8 +1022,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dd><dl class="simple">
 <dt>lambda_d<span class="classifier">array</span></dt><dd><p>Wavelength array, in AA.</p>
 </dd>
-<dt>phi<span class="classifier">float</span></dt><dd><p>Eq.34 of Draine &amp; Li 2007. (default: 0.055)
-~0.055 g / (ergs/s)</p>
+<dt>phi<span class="classifier">float</span></dt><dd><p>Eq.34 of Draine &amp; Li 2007. (default: 0.055g/(ergs/s))</p>
 </dd>
 <dt>DL<span class="classifier">float</span></dt><dd><p>in cm.</p>
 </dd>
@@ -1075,11 +1081,10 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dt class="sig sig-object py" id="gsf.maketmp_filt.sim_spec">
 <span class="sig-prename descclassname"><span class="pre">gsf.maketmp_filt.</span></span><span class="sig-name descname"><span class="pre">sim_spec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lmin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sn</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.maketmp_filt.sim_spec" title="Permalink to this definition"></a></dt>
 <dd><p>SIMULATION of SPECTRA.</p>
-<p>wave_obs :
-wave_temp : 
-flux_temp : 
-sn_obs</p>
-<p>frand, erand</p>
+<p>sn : float array</p>
+<p>frand : float array</p>
+<p>erand :  float array</p>
+<p><a class="reference internal" href="#gsf.maketmp_filt.get_spectrum_draine" title="gsf.maketmp_filt.get_spectrum_draine"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_spectrum_draine()</span></code></a></p>
 </dd></dl>
 
 </section>
diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js
index 061cdc5..5e02af0 100644
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+++ b/docs/_build/html/searchindex.js
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relat:[8,12],relev:[],remov:0,repeat:0,replac:[0,11],repres:0,represent:0,reprocess:[],request:[],requir:0,requr:0,res_cz:0,reshift:0,resid:0,resid_i:[0,3],residu:0,residual_nest:0,residual_z:0,resolut:9,respect:0,respons:[0,9],respr:0,rest:[0,8],restructuredtext:[],result:[0,2],retain:0,retriev:0,reuse_sampl:0,rf:0,right:[0,3],rm:0,rms_lim:0,robust:0,routin:0,row:0,run:[0,3,6,8,11],run_gsf:[2,9,11],run_gsf_al:0,run_gsf_templ:0,run_mcmc:0,rv:0,s1323358014000332:0,s:[0,9],s_n:0,salpet:[0,9],same:[0,2,9,11],sampl:0,sampler:0,save:[0,12],save_s:0,savecpkl:0,sawicki:3,scalar:0,scalar_minim:0,scale:[0,9,12],scale_covar:0,scatter:0,scenario:0,scienc:8,scipi:0,scipydoc:0,score:0,script:[0,11],search:[0,4,11],search_redshift:0,second:0,section:[],sed:[0,2,7,8],see:[0,2,3,5,7],seed:0,seem:0,select:0,self:0,sens:0,sent:0,separ:[0,9],sequenc:0,sequenti:0,servic:0,set:[0,5,9,10,11,12],set_param:0,setself:0,setup:0,sever:0,sf:0,sfh:[0,2,9],sfh_:7,sfh_con:0,sfh_dec:0,sfh_del:0,sfh_form:9,sfm:0,sfms_50:0,sfr:[0,7],sfr_tau:[],shape:[0,9],share:12,shgo:0,shgo_:0,shift:[8,11],should:[0,5,9],show:0,show_candid:0,shuffl:0,side:0,sig:0,sigma:[0,3],sigma_:3,sigma_i:3,signatur:0,significantli:11,sigz:0,sim_spec:0,similar:5,simpl:0,simpli:0,simplici:0,simplifi:0,simul:0,simultan:8,sinc:[0,2,8],singl:0,size:[0,5,12],skip:[0,9],skip_fitz:0,skip_sfh:0,skip_zhist:0,skipfilt:9,slice:0,slope:0,slow:11,slsqp:0,small:11,smaller:0,smc:9,smooth:0,sn:[0,9],sn_:3,sn_ob:[],snlim:[0,9],so:[0,7,10,12],solut:0,solver:0,some:0,sort:0,sourc:[5,9],space:[0,10,11],span:[],spars:11,spawn:0,speagl:0,spec:0,spec_al:12,spec_fil:[5,9],specif:[0,9,11,12],specifi:[0,5,8,9,10,11,12],specplot:0,spectra:[0,2,5,7,8,9],spectral:[0,9,12],spectroscop:9,spectrum:0,spectrun:0,speed:11,spefici:[2,5],sqrt:[0,3],squar:[0,8],ssp:[0,9,12],stand:0,standard:[0,5],star:[7,8,9,12],start:[0,2,12],stat:0,state:0,statist:[0,7],statu:0,stderr:0,stellar:[0,6,7,8],step0:2,step1:2,step:[0,2,8,9,11,12],stop:0,store:[0,2,5,7,9,12],storesd:0,str2bool:0,str:[0,9],strategi:0,strictli:0,string:[0,9],structur:0,stsci:0,submodul:1,subsequ:0,subset:9,succeed:0,success:0,suffix:0,suggest:12,sum:0,sum_:3,sum_i:0,sum_n:0,summary_:7,suppli:0,support:[0,9],suppress:0,sure:0,swap_par:0,swap_pars_inv:0,symbol:[],synthet:[3,12],t0:0,t:[0,9],tabl:0,tabu:0,tabul:9,tabulists:0,tabustrategi:0,takahiro:0,take:[0,2,8],taken:0,target:[0,5,9],tau0:[0,9,12],tau:[0,7,8,9,10,12],tau_lim:0,taumax:[9,10],taumin:[9,10],tbd:9,tcalc:0,tdust_del:9,tdust_hig:9,tdust_low:9,temlat:0,temperatur:9,templat:[0,3,4,5,8,9,11],term:0,termin:0,test:2,than:[0,6,8,11],thei:[0,11],themselv:0,theorem:10,therefor:12,theta:[0,10],theta_:10,thi:[0,2,7,9,11,12],thin:0,thing:0,those:[0,12],three:[0,12],through:[0,10],thu:0,time:[0,2,7,8,9,11,12],tmin:[],tmp03:[],tmp03_neb:[],tmp04:[],tmp04_dust:0,tmp04_neb:[],tmp_norm:0,tnc:0,tol:0,toler:0,too:11,total:0,totalit:0,tp:[],tranform:0,transform:0,transit:9,treat:0,truncat:0,trust:0,ts:0,tset_sfr:7,tset_sfr_s:0,tt:0,tunnel:0,tupl:0,turn:[0,11],two:[0,9],txmax:[],txmin:[],txt:6,type:9,udfcen3:[],ufloat:0,umax:0,umin:0,un:0,uncertain:0,uncertainti:0,unchang:0,underli:0,understand:0,uniform:[0,12],unit:[0,5],univers:[0,9],unless:0,unprepare_fit:0,unweight:0,up:[0,8,11],updat:0,update_input:0,upper:[0,9],us:[0,2,7,9,11,12],usag:11,user:[0,5,6,11],userfcn:0,usual:8,uv:0,uvar:0,v1:0,v2:[],v:[0,9,10],val:0,valid:0,valu:[0,7,9,11,12],valueerror:0,van:9,vandokkum08:0,var_nam:0,vari:0,variabl:[0,11],variou:0,vdisp:9,veloc:9,ver0:6,ver1:[2,6],ver3:6,verbos:0,veri:0,version1:9,version:[1,5,10,12],via:[0,12],view:0,visit:0,visual:9,voigt:0,wa:[0,11],wai:2,walker:[0,9,11],want:[0,6,11],wave:[0,11],wave_:12,wave_ob:[],wave_temp:[],wavelength:[0,8,11],wavelength_in_aa:5,webpag:0,weight:0,weird:0,well:[0,7,8],were:0,when:[0,5,11],where:[0,3,5,8,9,12],wherea:0,whether:0,which:[0,12],whole:9,whose:10,wht3:0,wht:0,width:0,wish:[0,12],within:9,work:[0,11],worker:0,worth:0,would:[0,2],wrap:0,wrapper:0,write:1,write_input:0,written:0,www:0,wycont:0,x0:0,x:0,x_cz:0,x_ex:0,xbb:0,xcont:0,xer:0,xint:0,xl:0,xll:0,xlll:0,xm_tmp:0,xob:0,xtmp:0,xtol:0,xx:0,y0:0,y:0,ycont:0,ye:[0,9],yer:0,yml:6,you:[0,2,6,7,8,9],your:[0,5,7],yourself:0,ypoli:0,yr:0,ysump:0,ytmp:0,yy:0,z0:0,z2nz:0,z:[0,8,10,12],z_:0,z_input:2,z_r:0,zbe:0,zbest:0,zero:[0,5],zeropoint:[0,9],zevol:9,zfit:1,zfix:9,zforc:0,zgal:[0,2,5,9,10],zin:0,zini:0,zliml:0,zlimu:0,zman:0,zmax:[0,9,10],zmc:[9,11],zmcmax:[9,11],zmcmin:[9,11],zmin:[0,9,10],zob:0,zout:0,zprior:0,zs:0,zspace:0,zsun:0,zvi:[2,9],zz_prob:0,zztmp:0},titles:["gsf package","gsf","Examples","Fitting process","gsf\u2019s documentation","Input files","Installing gsf","Output files","Overview","Paramter Description","Prior","Redshift Fitting","Generating templates"],titleterms:{"0":[2,12],"1":[7,12],"2":[3,7],"3":7,"4":7,"function":0,A:2,On:2,appendici:2,b:2,basic:8,basic_func:0,broadband:5,catalog:5,chain:7,content:[0,4],curv:5,data:5,descript:9,document:4,exampl:2,execut:[2,3,12],file:[5,7],filter:5,fit:[0,3,11],flag:[2,3,12],fly:2,frame:12,function_class:0,function_igm:0,gener:[2,12],gsf:[0,1,4,6],id:2,indic:4,input:[5,12],instal:6,limit:8,maketmp_filt:0,maketmp_z0:0,maketmp_z0_tau:0,minim:0,modul:0,multi:2,other:2,output:7,overview:8,packag:0,paramt:9,plot_mz:0,plot_sed_loga:0,plot_sfh_loga:0,posterior_flex:0,posterior_nest:0,prior:10,process:[2,3],redshift:[11,12],requir:6,respons:5,rest:12,run:2,s:4,sfh:7,shift:12,specifi:2,spectral:[5,7],submodul:0,summari:7,tabl:4,target:2,templat:[2,12],version:0,workflow:8,write:0,z:2,zfit:0}})
\ No newline at end of file
diff --git a/gsf/fitting.py b/gsf/fitting.py
index e8478e5..cc54791 100644
--- a/gsf/fitting.py
+++ b/gsf/fitting.py
@@ -64,11 +64,13 @@ class Mainbody():
         or the width to the next age bin.
     '''
 
-    def __init__(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:float=25.0, pixelscale:float=0.06, Lsun:float=3.839*1e33, cosmo=None, idman=None, zman=None):
+    def __init__(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:float=25.0, pixelscale:float=0.06, Lsun:float=3.839*1e33, 
+        cosmo=None, idman=None, zman=None, NRbb_lim=10000):
         self.update_input(inputs, idman=idman, zman=zman)
+        self.NRbb_lim = NRbb_lim # BB data is associated with ids greater than this number.
 
 
-    def update_input(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:float=25.0, pixelscale:float=0.06, Lsun:float=3.839*1e33, cosmo=None, \
+    def update_input(self, inputs, c:float=3e18, Mpc_cm:float=3.08568025e+24, m0set:float=25.0, pixelscale:float=0.06, Lsun:float=3.839*1e33, cosmo=None,
                     idman=None, zman=None, sigz:float=5.0):
         '''
         The purpose of this module is to register/update the parameter attributes in `Mainbody`
diff --git a/gsf/function_class.py b/gsf/function_class.py
index d4a20f0..0d45644 100644
--- a/gsf/function_class.py
+++ b/gsf/function_class.py
@@ -49,8 +49,19 @@ def __init__(self, MB, dust_model:int = 0):
 
 
     def open_spec_fits(self, fall:int = 0, orig:bool = False, f_neb=False):
-        '''
-        Load template in obs range.
+        '''Load template in obs range.
+
+        Parameters
+        ----------
+        fall : int
+            If 1, returns full spectra.
+        orig : :obj:`bool`
+            If True, returns the original spectra.
+
+        Returns
+        -------
+        lib : float array
+            
         '''
         ID0 = self.MB.ID
         ZZ = self.ZZ
@@ -111,8 +122,7 @@ def open_spec_fits(self, fall:int = 0, orig:bool = False, f_neb=False):
 
 
     def open_spec_dust_fits(self, fall:int = 0):
-        '''
-        Loads dust template in obs range.
+        '''Loads dust template in obs range.
         '''
         ID0 = self.MB.ID
         tau0= self.MB.tau0
@@ -148,106 +158,8 @@ def open_spec_dust_fits(self, fall:int = 0):
         return lib
 
 
-    """
-    def open_spec_neb_fits(self, fall:int = 0, orig:bool = False):
-        '''
-        Loads template in obs range.
-        '''
-        ID0 = self.MB.ID
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-
-        # ASDF;
-        if fall == 0:
-            app = ''
-            hdu0 = self.MB.af['spec']
-        elif fall == 1:
-            app = 'all_'
-            hdu0 = self.MB.af['spec_full']
-
-        DIR_TMP = self.DIR_TMP
-
-        NZ = len(ZZ)
-        NU = len(self.MB.logUs)
-        for zz,Z in enumerate(ZZ):
-            for uu,logU in enumerate(self.MB.logUs):
-                if zz == 0 and uu == 0:
-                    nr = hdu0['colnum']
-                    xx = hdu0['wavelength']
-                    coln = int(2 + NZ * NU)
-                    lib = np.zeros((len(nr), coln), dtype=float)
-                    lib[:,0] = nr[:]
-                    lib[:,1] = xx[:]
-
-                if orig:
-                    colname = 'fspec_orig_nebular_Z%d'%zz + '_logU%d'%uu
-                else:
-                    colname = 'fspec_nebular_Z%d'%zz + '_logU%d'%uu
-                colnall = int(2 + zz * NU + uu) # 2 takes account of wavelength and AV columns.
-                lib[:,colnall] = hdu0[colname]
-
-        return lib
-    """
-
-    """
-    def open_spec_fits_dir(self, nage:int, nz:int, kk, Av:float, zgal:float, A00:float, f_IGM=True):
-        '''
-        Load template in obs range.
-        But for weird template.
-        '''
-        tau0= self.tau0
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-
-        app = 'all'
-        hdu0 = self.MB.af['spec_full']
-        DIR_TMP = self.DIR_TMP
-
-        pp = 0
-        zz = nz
-
-        # Luminosity
-        mshdu = self.MB.af0['ML']
-        Ls = mshdu['Ls_%d'%nz] 
-
-        xx = hdu0['wavelength']
-        nr = np.arange(0,len(xx),1)
-
-        lib = np.zeros((len(nr), 2+1), dtype='float')
-        lib[:,0] = nr[:]
-        lib[:,1] = xx[:]
-
-        aa = nage
-        coln = int(2 + aa)
-        colname = 'fspec_' + str(zz) + '_' + str(aa) + '_' + str(pp)
-
-        yy0 = hdu0[colname]/Ls[aa]
-        yy = flamtonu(xx, yy0, m0set=self.MB.m0set)
-        lib[:,2] = yy[:]
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
-
-        xxd *= (1.+zgal)
-        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-        nrd_yyd[:,0] = nrd[:]
-        nrd_yyd[:,1] = yyd[:]
-        nrd_yyd[:,2] = xxd[:]
-
-        b = nrd_yyd
-        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-        yyd_sort = nrd_yyd_sort[:,1]
-        xxd_sort = nrd_yyd_sort[:,2]
-
-        return A00 * yyd_sort, xxd_sort
-    """
-
     def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_get_Mtot=False, f_check_limit=True):
-        '''
-        Purpose
-        -------
-        get total flux for a given set of parameter.
+        '''get total flux for a given set of parameter.
 
         Parameters
         ----------
@@ -256,8 +168,8 @@ def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_get_M
 
         Returns
         -------
-        xx : 
-            OBSERVED wavelength range at z=MB.zgal
+        nr, xx, yy : float arrays
+            xx is OBSERVED wavelength at z=MB.zgal
         '''
         Mtot:float = 0
         if lib == None:
@@ -310,10 +222,7 @@ def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_get_M
 
 
     def get_total_flux_neb(self, par, f_Alog=True, lib_all=True, lib=None):
-        '''
-        Purpose
-        -------
-        get total flux for a given set of parameter.
+        '''get total flux for a given set of parameter.
 
         Parameters
         ----------
@@ -372,46 +281,11 @@ def get_total_flux_neb(self, par, f_Alog=True, lib_all=True, lib=None):
 
         return nr, xx, yy
 
-    """
-    def get_template_single(self, A00, Av, nmodel, Z, zgal, lib):
-        '''
-        '''
-        tau0= self.tau0
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-        DIR_TMP = self.MB.DIR_TMP
-        NZ = bfnc.Z2NZ(Z)
-
-        pp0 = np.random.uniform(low=0, high=len(tau0), size=(1,))
-        pp = int(pp0[0])
-        if pp>=len(tau0):
-            pp += -1
-
-        coln = int(2 + pp*len(ZZ)*len(AA) + NZ*len(AA) + nmodel)
-        nr = lib[:,0]
-        xx = lib[:,1] # This is OBSERVED wavelength range at z=zgal
-        yy = lib[:,coln]
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
-
-        xxd *= (1.+zgal)
-
-        nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-        nrd_yyd[:,0] = nrd[:]
-        nrd_yyd[:,1] = yyd[:]
-        nrd_yyd[:,2] = xxd[:]
-
-        b = nrd_yyd
-        nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-        yyd_sort = nrd_yyd_sort[:,1]
-        xxd_sort = nrd_yyd_sort[:,2]
-
-        return A00 * yyd_sort, xxd_sort
-    """
 
     def get_template_single(self, A00, Av, nmodel, Z, zgal, lib, logU=None, f_apply_dust=True, EBVratio=2.27):
         '''
+        Parameters
+        ----------
         EBVratio : float
             E(B-V)_neb / E(B-V)_st. 
             Useful table in https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf
@@ -470,17 +344,14 @@ def get_template_single(self, A00, Av, nmodel, Z, zgal, lib, logU=None, f_apply_
 
     def get_template(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib_all:bool=False, f_nrd:bool=False, 
         f_apply_dust:bool=True, f_IGM=True, deltaz_lim=0.1, f_neb=False, EBVratio:float=2.27):
-        '''
-        Purpose
-        -------
-        Makes model template for a given parameter set, ``par``.
+        '''Makes model template for a given parameter set, ``par``.
 
         Parameters
         ----------
         nprec : int
             Precision when redshift is refined. 
         f_apply_dust : bool
-            Apply dust attenuation to nebular emission?
+            Apply dust attenuation to nebular emission.
         EBVratio : float
             E(B-V)_neb / E(B-V)_st. 
             Useful table in https://iopscience.iop.org/article/10.3847/1538-4357/aba35e/pdf
@@ -540,6 +411,21 @@ def get_template(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib
                         nr_full, xx_full, yy_full = self.get_total_flux(par, f_Alog=f_Alog, lib_all=True)
 
                 xx_s, yy_s = filconv(self.MB.filts, xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, self.MB.DIR_FILT, MB=self.MB, f_regist=False)
+            elif np.abs(zmc - self.MB.zgal) > deltaz_lim and self.MB.f_spec:
+                if lib_all:
+                    nr_full, xx_full, yy_full = nr, xx, yy
+                else:
+                    if f_neb:
+                        nr_full, xx_full, yy_full = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=True)
+                    else:
+                        nr_full, xx_full, yy_full = self.get_total_flux(par, f_Alog=f_Alog, lib_all=True)
+                xx_bb, yy_bb = filconv(self.MB.filts, xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, self.MB.DIR_FILT, MB=self.MB, f_regist=False)
+                con_bb = (nr>=self.MB.NRbb_lim)
+                xx[con_bb] = xx_bb
+                yy[con_bb] = yy_bb
+                xx_s = xx
+                yy_s = yy
+
             else:
                 fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
                 xx_s = xx / (1+self.MB.zgal) * (1+zmc)
@@ -616,83 +502,13 @@ def tmp04_dust(self, par, nprec=1):
         return yy_s, xx_s
 
 
-"""
-## Duplicated. To be deprecated.
-    def get_template(self, lib, Amp:float = 1.0, T:float = 1.0, Av:float = 0.0, Z:float = 0.0, zgal:float = 1.0, f_bb:bool = False, fneb=False):
-        '''
-        Gets an element template given a set of parameters.
-        Not necessarily the most efficient way, but easy to use.
-
-        Parameters:
-        -----------
-        lib : dict
-            library dictionary.
-        Amp : float
-            Amplitude of the target template. Note that each template has Lbol = 1e10Lsun.
-        T : float
-            Age, in Gyr.
-        Av : float
-            Dust attenuation, in mag.
-        Z : float
-            Metallicity, in log(Z/Zsun).
-        zgal : float
-            Redshift.
-        f_bb: bool
-            If calculate bb photometry for the spectrum requested.
-
-        Returns
-            flux : float array. Flux in Fnu. 
-            wavelength : float array. Wave in AA.
-            lcen, lflux : , if f_bb==True.
-
-        '''
-        bfnc = self.MB.bfnc
-        DIR_TMP = self.MB.DIR_TMP 
-        NZ  = bfnc.Z2NZ(Z)
-
-        pp0 = np.random.uniform(low=0, high=self.MB.ntau0, size=(1,))
-        pp  = int(pp0[0])
-        if pp>=self.MB.ntau0:
-            pp += -1
-
-        nmodel = np.argmin(np.abs(T-self.age[:]))
-        if T - self.age[nmodel] != 0:
-            print('T=%.2f is not found in age library. T=%.2f is used.'%(T,self.age[nmodel]))
-
-        if fneb:
-            coln = int(2 + pp*len(self.ZZ)*1 + NZ*1 + 0)
-        else:
-            coln = int(2 + pp*len(self.ZZ)*len(self.AA) + NZ*len(self.AA) + nmodel)
-        nr = lib[:, 0]
-        xx = lib[:, 1] # This is OBSERVED wavelength range at z=zgal
-        yy = lib[:, coln]
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
-        xxd *= (1.+zgal)
-
-        if self.dust_model != 0:
-            # This may be needed when not calzetti model
-            nrd_yyd = np.zeros((len(nrd),3), dtype=float)
-            nrd_yyd[:,0] = nrd[:]
-            nrd_yyd[:,1] = yyd[:]
-            nrd_yyd[:,2] = xxd[:]
-            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
-            nrd[:],yyd[:],xxd[:] = nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
-
-        if f_bb:
-            fil_cen, fil_flux = filconv_fast(self.MB, xxd, Amp * yyd)
-            return Amp * yyd, xxd, fil_flux, fil_cen
-        else:
-            return Amp * yyd, xxd
-"""
-
 class Func_tau:
     '''
     '''
     def __init__(self, MB, dust_model:int=0):
         '''
-        Parameters:
-        -----------
+        Parameters
+        ----------
         dust_model : int
             0 for Calzetti. 1 for MW. 4 for Kriek Conroy
         '''
diff --git a/gsf/maketmp_filt.py b/gsf/maketmp_filt.py
index 451bc6f..43ce0a4 100644
--- a/gsf/maketmp_filt.py
+++ b/gsf/maketmp_filt.py
@@ -15,7 +15,7 @@
 from .function_igm import *
 
 
-def get_spectrum_draine(lambda_d, DL, zbest, numin, numax, ndmodel, \
+def get_spectrum_draine(lambda_d, DL, zbest, numin, numax, ndmodel,
     DIR_DUST='./DL07spec/', phi=0.055, m0set=25.0):
     '''
     Parameters
@@ -23,8 +23,7 @@ def get_spectrum_draine(lambda_d, DL, zbest, numin, numax, ndmodel, \
     lambda_d : array
         Wavelength array, in AA.
     phi : float
-        Eq.34 of Draine & Li 2007. (default: 0.055)
-        ~0.055 g / (ergs/s)
+        Eq.34 of Draine & Li 2007. (default: 0.055g/(ergs/s))
     DL : float
         in cm.
 
@@ -97,14 +96,16 @@ def sim_spec(lmin, fin, sn):
     
     Parameters
     ----------
-    wave_obs :
-    wave_temp : 
-    flux_temp : 
-    sn_obs
-
+    sn : float array
+        
     Returns
     -------
-    frand, erand
+    frand : float array
+
+    erand :  float array
+
+
+    :func:`get_spectrum_draine`
     '''
 
     frand = fin * 0
@@ -120,14 +121,13 @@ def sim_spec(lmin, fin, sn):
 
 
 def check_library(MB, af, nround=3):
-    '''
-    Check library if it has a consistency setup as input file.
+    '''Check library if it has a consistency setup as input file.
 
     Returns
     -------
     flag : bool
+        
     '''
-
     # Z needs special care in z0 script, to avoid Zfix.
     if False:
         Zmax_tmp, Zmin_tmp = float(MB.inputs['ZMAX']), float(MB.inputs['ZMIN'])
@@ -141,6 +141,12 @@ def check_library(MB, af, nround=3):
     flag = True
     print('Checking the template library...')
     print('Speficied - Template')
+
+    # No. of age;
+    if len(af['ML']['ms_0']) != len(MB.age):
+        print('No of age pixels:', len(MB.age), len(af['ML']['ms_0']))
+        flag = False
+
     # Matallicity:
     for aa in range(len(Zall)):
         if Zall[aa] != af['Z%d'%(aa)]:
@@ -578,9 +584,9 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
                     Ls = np.zeros(Na, dtype=float)
                     tau = np.zeros(Na, dtype=float)
                     sfr = np.zeros(Na, dtype=float)
+                    Fuv = np.zeros(Na, dtype=float)
                     ms[:] = mshdu['ms_'+str(zz)][:] # [:] is necessary.
                     Ls[:] = mshdu['Ls_'+str(zz)][:]
-                    Fuv = np.zeros(Na, dtype=float)
 
                     for ss in range(Na):
                         wave = lm0
diff --git a/gsf/maketmp_z0.py b/gsf/maketmp_z0.py
index 249bf17..3758ce8 100644
--- a/gsf/maketmp_z0.py
+++ b/gsf/maketmp_z0.py
@@ -199,6 +199,8 @@ def make_tmp_z0(MB, lammin=100, lammax=160000, tau_lim=0.001, force_no_neb=False
                         'nimf': nimf,
                         'version_gsf': gsf.__version__
                     }
+                    tree.update({'age': MB.age})
+                    tree.update({'Z': MB.Zall})
                     if fneb:
                         tree.update({'logUMIN': MB.logUMIN})
                         tree.update({'logUMAX': MB.logUMAX})

From 5b5256e008bfb5675a646e265bf45f3ca4086fbf Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Sat, 10 Dec 2022 22:26:30 -0800
Subject: [PATCH 10/14] tau model fixed

---
 gsf/function_class.py | 253 +++++++++++++++++++++++++-----------------
 gsf/gsf.py            |  64 +++++------
 gsf/maketmp_filt.py   | 179 +++++++++++++++++++++++++++++-
 gsf/plot_sed.py       |   8 +-
 4 files changed, 365 insertions(+), 139 deletions(-)

diff --git a/gsf/function_class.py b/gsf/function_class.py
index 0d45644..b1f29ff 100644
--- a/gsf/function_class.py
+++ b/gsf/function_class.py
@@ -534,7 +534,7 @@ def __init__(self, MB, dust_model:int=0):
         self.f_af = False
         self.f_af0 = False
 
-
+    """
     def open_spec_fits(self, fall:int=0, orig:bool=False):
         '''
         Loads template in obs range.
@@ -575,6 +575,7 @@ def open_spec_fits(self, fall:int=0, orig:bool=False):
                     lib[:,colnall] = hdu0[colname]
 
         return lib
+    """
 
 
     def open_spec_dust_fits(self, fall:int = 0):
@@ -618,7 +619,7 @@ def open_spec_dust_fits(self, fall:int = 0):
         return lib
 
 
-    def open_spec_neb_fits(self, fall=0, orig=False):
+    def open_spec_fits(self, fall=0, orig=False, f_neb=False):
         '''
         Loads template in obs range.
         '''
@@ -636,29 +637,48 @@ def open_spec_neb_fits(self, fall=0, orig=False):
             hdu0 = self.MB.af['spec_full']
 
         DIR_TMP = self.DIR_TMP
-
         NZ = len(ZZ)
-        NU = len(self.MB.logUs)
-        for zz,Z in enumerate(ZZ):
-            for uu,logU in enumerate(self.MB.logUs):
-                if zz == 0 and uu == 0:
-                    nr = hdu0['colnum']
-                    xx = hdu0['wavelength']
-                    coln = int(2 + NZ * NU)
-                    lib = np.zeros((len(nr), coln), dtype=float)
-                    lib[:,0] = nr[:]
-                    lib[:,1] = xx[:]
-
-                if orig:
-                    colname = 'fspec_orig_nebular_Z%d'%zz + '_logU%d'%uu
-                else:
-                    colname = 'fspec_nebular_Z%d'%zz + '_logU%d'%uu
-                colnall = int(2 + zz * NU + uu) # 2 takes account of wavelength and AV columns.
-                lib[:,colnall] = hdu0[colname]
 
+        if f_neb:
+            NU = len(self.MB.logUs)
+            for zz,Z in enumerate(ZZ):
+                for uu,logU in enumerate(self.MB.logUs):
+                    if zz == 0 and uu == 0:
+                        nr = hdu0['colnum']
+                        xx = hdu0['wavelength']
+                        coln = int(2 + NZ * NU)
+                        lib = np.zeros((len(nr), coln), dtype=float)
+                        lib[:,0] = nr[:]
+                        lib[:,1] = xx[:]
+                    if orig:
+                        colname = 'fspec_orig_nebular_Z%d'%zz + '_logU%d'%uu
+                    else:
+                        colname = 'fspec_nebular_Z%d'%zz + '_logU%d'%uu
+                    colnall = int(2 + zz * NU + uu) # 2 takes account of wavelength and AV columns.
+                    lib[:,colnall] = hdu0[colname]
+        else:
+            NT = self.MB.ntau
+            NA = self.MB.nage
+            for zz,Z in enumerate(ZZ):
+                for tt,TT in enumerate(self.MB.tau):                
+                    for ss,TA in enumerate(self.MB.ageparam):
+                        if zz == 0 and tt == 0 and ss == 0:
+                            nr = hdu0['colnum']
+                            xx = hdu0['wavelength']
+                            coln = int(2 + NZ * NT * NA)
+                            lib = np.zeros((len(nr), coln), dtype=float)
+                            lib[:,0] = nr[:]
+                            lib[:,1] = xx[:]
+                        if orig:
+                            colname = 'fspec_orig_' + str(zz) + '_' + str(tt) + '_' + str(ss)
+                        else:
+                            colname = 'fspec_' + str(zz) + '_' + str(tt) + '_' + str(ss)
+                        colnall = int(2 + zz * NT * NA + tt * NA + ss) # 2 takes account of wavelength and AV columns.
+                        lib[:,colnall] = hdu0[colname]
         return lib
 
 
+    """
     def open_spec_fits_dir(self, nage, nz, kk, Av, zgal, A00):
         '''
         Loads template in obs range.
@@ -711,46 +731,15 @@ def open_spec_fits_dir(self, nage, nz, kk, Av, zgal, A00):
             yyd_sort = nrd_yyd_sort[:,1]
             xxd_sort = nrd_yyd_sort[:,2]
             return A00 * yyd_sort, xxd_sort
+    """
 
-
-    def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, lib_all=False, f_nrd=False):
+    def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_get_Mtot=False, f_check_limit=True):
         '''
-        Makes model template with a given param set.
-        Also dust attenuation.
-
-        Parameters:
-        -----------
-        nprec : int
-            Precision when redshift is refined. 
         '''
-        ZZ = self.ZZ
-        AA = self.AA 
-        bfnc = self.MB.bfnc
         Mtot = 0
-        pp = 0
-
-        if f_val:
-            par = par.params
-
-        if self.MB.fzmc == 1:
-            try:
-                zmc = par['zmc'].value
-            except:
-                zmc = self.MB.zgal
-        else:
-            zmc = self.MB.zgal
-
-        if check_bound:
-            # AV limit;
-            if par['Av'] < self.MB.Avmin:
-                par['Av'] = self.MB.Avmin
-            if par['Av'] > self.MB.Avmax:
-                par['Av'] = self.MB.Avmax
-        Av = par['Av']
-
         for aa in range(self.MB.npeak):
             if self.MB.ZEVOL==1 or aa == 0:
-                if check_bound:
+                if f_check_limit:
                     # Z limit:
                     if par['Z%d'%aa] < self.MB.Zmin:
                         par['Z%d'%aa] = self.MB.Zmin
@@ -760,7 +749,7 @@ def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False
             else:
                 pass
 
-            if check_bound:
+            if f_check_limit:
                 # A
                 if par['A'+str(aa)] < self.MB.Amin:
                     par['A'+str(aa)] = self.MB.Amin
@@ -775,7 +764,7 @@ def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False
                 if par['AGE'+str(aa)] < self.MB.agemin:
                     par['AGE'+str(aa)] = self.MB.agemin
                 if par['AGE'+str(aa)] > self.MB.agemax:
-                    par['AGE'+str(aa)] = self.MB.agemax
+                    par['AGE'+str(aa)].value = self.MB.agemax
 
             # Is A in logspace?
             if f_Alog:
@@ -785,7 +774,7 @@ def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False
 
             tau,age = par['TAU%d'%aa],par['AGE%d'%aa]
 
-            NZ, NT, NA = bfnc.Z2NZ(Z,tau,age)
+            NZ, NT, NA = self.MB.bfnc.Z2NZ(Z,tau,age)
             coln = int(2 + NZ*self.MB.ntau*self.MB.npeak + NT*self.MB.npeak + NA)
             mslist = self.MB.af['ML']['ML_'+str(NZ)+'_'+str(NT)][NA]
             Mtot += 10**(par['A%d'%aa] + np.log10(mslist))
@@ -805,6 +794,105 @@ def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False
                 else:
                     yy += A00 * self.MB.lib[:, coln]
 
+        if f_get_Mtot:
+            return nr, xx, yy, Mtot
+        else:
+            return nr, xx, yy
+
+
+    def get_total_flux_neb(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_get_Mtot=False, f_check_limit=True):
+        '''
+        '''
+        Mtot = 0
+        try:
+            Aneb = par['Aneb']
+            logU = par['logU']
+            nlogU = np.argmin(np.abs(self.MB.logUs - logU))
+        except: # This is exception for initial minimizing;
+            Aneb = -99
+            logU = self.MB.logUs[0]
+            nlogU = 0
+
+        # logU
+        NU = len(self.MB.logUs)
+        # Check limit;
+        if Aneb < self.MB.Amin:
+            Aneb = self.MB.Amin
+        if Aneb > self.MB.Amax:
+            Aneb = self.MB.Amax
+
+        # Is A in logspace?
+        if f_Alog:
+            A00 = 10**Aneb
+        else:
+            A00 = Aneb
+
+        aa = 0
+        if self.MB.ZEVOL==1 or aa == 0:
+            if f_check_limit:
+                # Z limit:
+                if par['Z%d'%aa] < self.MB.Zmin:
+                    par['Z%d'%aa] = self.MB.Zmin
+                if par['Z%d'%aa] > self.MB.Zmax:
+                    par['Z%d'%aa] = self.MB.Zmax
+            Z = par['Z%d'%aa]
+            NZ = np.argmin(np.abs(self.MB.Zall-Z))
+
+        coln = int(2 + NZ*NU + nlogU)
+        if aa == 0:
+            nr = lib[:, 0]
+            xx = lib[:, 1] # This is OBSERVED wavelength range at z=zgal
+            yy = A00 * lib[:, coln]
+        else:
+            yy += A00 * lib[:, coln]
+
+        if f_get_Mtot:
+            return nr, xx, yy, Mtot
+        else:
+            return nr, xx, yy
+
+    """
+    def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, lib_all=False, f_nrd=False, 
+        f_neb=False, EBVratio:float=2.27):
+        '''
+        Makes model template with a given param set.
+        Also dust attenuation.
+
+        Parameters:
+        -----------
+        nprec : int
+            Precision when redshift is refined. 
+        '''
+        ZZ = self.ZZ
+        AA = self.AA 
+        bfnc = self.MB.bfnc
+        Mtot = 0
+        pp = 0
+
+        if f_val:
+            par = par.params
+
+        if self.MB.fzmc == 1:
+            try:
+                zmc = par['zmc'].value
+            except:
+                zmc = self.MB.zgal
+        else:
+            zmc = self.MB.zgal
+
+        if check_bound:
+            # AV limit;
+            if par['Av'] < self.MB.Avmin:
+                par['Av'] = self.MB.Avmin
+            if par['Av'] > self.MB.Avmax:
+                par['Av'] = self.MB.Avmax
+        Av = par['Av']
+
+        if f_neb:
+            nr, xx, yy, Mtot = get_total_flux_neb(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, lib=None, f_get_Mtot=True, f_check_limit=True)
+        else:
+            nr, xx, yy, Mtot = get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, lib=None, f_get_Mtot=True, f_check_limit=True)
+
         # Keep logM
         self.MB.logMtmp = np.log10(Mtot)
 
@@ -839,7 +927,7 @@ def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False
                 return nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
             else:
                 return nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
-
+    """
 
     def tmp04_dust(self, par, nprec=1):
         '''
@@ -878,8 +966,8 @@ def tmp04_dust(self, par, nprec=1):
         return yy_s, xx_s
 
   
-    def get_template_neb(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, 
-        lib_all=False, lib=None, f_nrd=False, f_apply_dust=True):
+    def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, 
+        lib_all=False, lib=None, f_nrd=False, f_apply_dust=True, f_neb=False):
         '''
         Makes model template with a given param set.
         Also dust attenuation.
@@ -920,48 +1008,13 @@ def get_template_neb(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=F
                 par['Av'] = self.MB.Avmax
         Av = par['Av']
 
-        try:
-            Aneb = par['Aneb']
-            logU = par['logU']
-            nlogU = np.argmin(np.abs(self.MB.logUs - logU))
-        except: # This is exception for initial minimizing;
-            Aneb = -99
-            logU = self.MB.logUs[0]
-            nlogU = 0
-
-        # logU
-        NU = len(self.MB.logUs)
-        # Check limit;
-        if Aneb < self.MB.Amin:
-            Aneb = self.MB.Amin
-        if Aneb > self.MB.Amax:
-            Aneb = self.MB.Amax
-
-        # Is A in logspace?
-        if f_Alog:
-            A00 = 10**Aneb
+        if f_neb:
+            # @@@ Not clear why f_check_limit cannot work
+            nr, xx, yy, Mtot = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, lib=lib, f_get_Mtot=True, f_check_limit=False)#, f_check_limit=True)
         else:
-            A00 = Aneb
-
-        aa = 0
-        if self.MB.ZEVOL==1 or aa == 0:
-            if check_bound:
-                # Z limit:
-                if par['Z%d'%aa] < self.MB.Zmin:
-                    par['Z%d'%aa] = self.MB.Zmin
-                if par['Z%d'%aa] > self.MB.Zmax:
-                    par['Z%d'%aa] = self.MB.Zmax
-            Z = par['Z%d'%aa]
-            NZ = np.argmin(np.abs(self.MB.Zall-Z))
+            # @@@ Not clear why f_check_limit cannot work
+            nr, xx, yy, Mtot = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, lib=lib, f_get_Mtot=True, f_check_limit=False)#, f_check_limit=True)
 
-        coln = int(2 + NZ*NU + nlogU)
-        if aa == 0:
-            nr = lib[:, 0]
-            xx = lib[:, 1] # This is OBSERVED wavelength range at z=zgal
-            yy = A00 * lib[:, coln]
-        else:
-            yy += A00 * lib[:, coln]
-        
         if round(zmc,nprec) != round(self.MB.zgal,nprec):
             xx_s = xx / (1+self.MB.zgal) * (1+zmc)
             fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
diff --git a/gsf/gsf.py b/gsf/gsf.py
index 3b727f8..3bd50cb 100644
--- a/gsf/gsf.py
+++ b/gsf/gsf.py
@@ -8,7 +8,7 @@
 
 # From gsf
 from .fitting import Mainbody
-from .maketmp_filt import maketemp,maketemp_tau
+from .maketmp_filt import maketemp #,maketemp_tau
 from .function_class import Func,Func_tau
 from .basic_func import Basic,Basic_tau
 
@@ -44,25 +44,24 @@ def run_gsf_template(inputs, fplt=0, tau_lim=0.001, idman=None, nthin=1, delwave
     #
     # 0. Make basic templates
     #
-    if fplt == 0 or fplt == 1:
+    if fplt == 0:
         #
         # 0. Make basic templates
         #
-        if fplt==0:
-            lammax = 40000 * (1.+MB.zgal) # AA
-            if MB.f_dust:
-                lammax = 2000000 * (1.+MB.zgal) # AA
+        lammax = 40000 * (1.+MB.zgal) # AA
+        if MB.f_dust:
+            lammax = 2000000 * (1.+MB.zgal) # AA
 
-            if MB.SFH_FORM == -99:
-                if MB.f_bpass == 1:
-                    from .maketmp_z0 import make_tmp_z0_bpass
-                    make_tmp_z0_bpass(MB, lammax=lammax)
-                else:
-                    from .maketmp_z0 import make_tmp_z0
-                    make_tmp_z0(MB, lammax=lammax)
+        if MB.SFH_FORM == -99:
+            if MB.f_bpass == 1:
+                from .maketmp_z0 import make_tmp_z0_bpass
+                make_tmp_z0_bpass(MB, lammax=lammax)
             else:
-                from .maketmp_z0_tau import make_tmp_z0
-                make_tmp_z0(MB, lammax=lammax)            
+                from .maketmp_z0 import make_tmp_z0
+                make_tmp_z0(MB, lammax=lammax)
+        else:
+            from .maketmp_z0_tau import make_tmp_z0
+            make_tmp_z0(MB, lammax=lammax)            
 
     #
     # 1. Start making redshifted templates.
@@ -74,7 +73,8 @@ def run_gsf_template(inputs, fplt=0, tau_lim=0.001, idman=None, nthin=1, delwave
         if MB.SFH_FORM == -99:
             maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
         else:
-            maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+            maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+            # maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
 
     # Read temp from asdf;
     # This has to happend after fplt==1 and before fplt>=2.
@@ -152,25 +152,24 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
     # Make templates based on input redsfift.
     #
     flag_suc = True
-    if fplt == 0 or fplt == 1:
+    if fplt == 0:
         #
         # 0. Make basic templates
         #
-        if fplt==0:
-            lammax = 40000 * (1.+MB.zgal) # AA
-            if MB.f_dust:
-                lammax = 2000000 * (1.+MB.zgal) # AA
+        lammax = 40000 * (1.+MB.zgal) # AA
+        if MB.f_dust:
+            lammax = 2000000 * (1.+MB.zgal) # AA
 
-            if MB.SFH_FORM == -99:
-                if MB.f_bpass == 1:
-                    from .maketmp_z0 import make_tmp_z0_bpass
-                    make_tmp_z0_bpass(MB, lammax=lammax)
-                else:
-                    from .maketmp_z0 import make_tmp_z0
-                    make_tmp_z0(MB, lammax=lammax)
+        if MB.SFH_FORM == -99:
+            if MB.f_bpass == 1:
+                from .maketmp_z0 import make_tmp_z0_bpass
+                make_tmp_z0_bpass(MB, lammax=lammax)
             else:
-                from .maketmp_z0_tau import make_tmp_z0
-                make_tmp_z0(MB, lammax=lammax)            
+                from .maketmp_z0 import make_tmp_z0
+                make_tmp_z0(MB, lammax=lammax)
+        else:
+            from .maketmp_z0_tau import make_tmp_z0
+            make_tmp_z0(MB, lammax=lammax)            
 
     if not flag_suc:
         sys.exit()
@@ -192,7 +191,8 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
         if MB.SFH_FORM == -99:
             flag_suc = maketemp(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
         else:
-            flag_suc = maketemp_tau(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
+            # flag_suc = maketemp_tau(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
+            flag_suc = maketemp(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
 
         #
         # 2. Main fitting part.
@@ -240,7 +240,7 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
             if MB.SFH_FORM == -99:
                 flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
             else:
-                flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+                flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
 
         if MB.SFH_FORM == -99:
             from .plot_sfh import plot_sfh
diff --git a/gsf/maketmp_filt.py b/gsf/maketmp_filt.py
index 43ce0a4..ec53240 100644
--- a/gsf/maketmp_filt.py
+++ b/gsf/maketmp_filt.py
@@ -143,9 +143,15 @@ def check_library(MB, af, nround=3):
     print('Speficied - Template')
 
     # No. of age;
-    if len(af['ML']['ms_0']) != len(MB.age):
-        print('No of age pixels:', len(MB.age), len(af['ML']['ms_0']))
-        flag = False
+    if MB.SFH_FORM==-99:
+        if len(af['ML']['ms_0']) != len(MB.age):
+            print('No of age pixels:', len(MB.age), len(af['ML']['ms_0']))
+            flag = False
+    else:
+        # if len(af['ML']['ms_0_0']) != len(MB.age):
+        #     print('No of age pixels:', len(MB.age), len(af['ML']['ms_0_0']))
+        #     flag = False
+        flag = True
 
     # Matallicity:
     for aa in range(len(Zall)):
@@ -734,8 +740,175 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
 
     else:
         print('Tau model comes here;')
+        ####################################
+        # Start generating templates
+        ####################################
+        tau = MB.tau
+        age = MB.ageparam
+        Na = len(age)
+
+        for zz in range(len(Z)):
+            Zbest = Z[zz]
+            Na = len(age)
+            Ntmp = 1
+            age_univ= MB.cosmo.age(zbest).value
+
+            for tt in range(len(tau)): # tau
+                if zz == 0 and tt == 0:
+                    if delwave>0:
+                        lm0_orig = spechdu['wavelength'][::nthin]
+                        lm0 = np.arange(lm0_orig.min(), lm0_orig.max(), delwave)
+                    else:
+                        lm0 = spechdu['wavelength'][::nthin]
+                    if not lammax == None and not MB.f_dust:
+                        lm0 = lm0[(lm0 * (zbest+1) < lammax)]
+                    wave = lm0
+
+                lmbest = np.zeros((Ntmp, len(lm0)), dtype=float)
+                fbest = np.zeros((Ntmp, len(lm0)), dtype=float)
+                lmbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
+                fbestbb = np.zeros((Ntmp, len(SFILT)), dtype=float)
+
+                spec_mul = np.zeros((Na, len(lm0)), dtype=float)
+                spec_mul_nu = np.zeros((Na, len(lm0)), dtype=float)
+                spec_mul_nu_conv = np.zeros((Na, len(lm0)), dtype=float)
+
+                ftmpbb = np.zeros((Na, len(SFILT)), dtype=float)
+                ltmpbb = np.zeros((Na, len(SFILT)), dtype=float)
+
+                ftmp_nu_int = np.zeros((Na, len(lm)), dtype=float)
+                spec_av_tmp = np.zeros((Na, len(lm)), dtype=float)
+
+                ms = np.zeros(Na, dtype=float)
+                Ls = np.zeros(Na, dtype=float)
+                ms[:] = mshdu['ms_'+str(zz)+'_'+str(tt)][:] # [:] is necessary.
+                Ls[:] = mshdu['Ls_'+str(zz)+'_'+str(tt)][:]
+                Fuv = np.zeros(Na, dtype=float)
+
+                for ss in range(Na):
+                    #print(ss,tt,zz)
+                    if ss == 0 and tt == 0 and zz == 0:
+                        DL = MB.cosmo.luminosity_distance(zbest).value * MB.Mpc_cm # Luminositydistance in cm
+                        wavetmp = wave*(1.+zbest)
+
+                    if delwave>0:
+                        fint = interpolate.interp1d(lm0_orig, spechdu['fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss)][::nthin], kind='nearest', fill_value="extrapolate")
+                        spec_mul[ss] = fint(lm0)
+                    else:
+                        spec_mul[ss] = spechdu['fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss)][::nthin]
+
+                    ##################
+                    # IGM attenuation.
+                    ##################
+                    if f_IGM:
+                        # spec_av_tmp = madau_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
+                        spec_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul[ss,:], zbest, cosmo=MB.cosmo)
+                    else:
+                        spec_av_tmp = spec_mul[ss,:]
+
+                    spec_mul_nu[ss,:] = flamtonu(wave, spec_av_tmp, m0set=MB.m0set)
+
+                    spec_mul_nu[ss,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
+                    spec_mul_nu[ss,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
+                    ms[ss] *= (1./Ls[ss])*tmp_norm # M/L; 1 unit template has this mass in [Msolar].
 
+                    if len(lm)>0:
+                        try:
+                            spec_mul_nu_conv[ss,:] = convolve(spec_mul_nu[ss,:], LSF, boundary='extend')
+                        except:
+                            spec_mul_nu_conv[ss,:] = spec_mul_nu[ss,:]
+                            if zz==0 and ss==0:
+                                print('Kernel is too small. No convolution.')
+                    else:
+                        spec_mul_nu_conv[ss,:] = spec_mul_nu[ss,:]
+
+                    if MB.f_spec:
+                        #ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu_conv[ss,:])
+                        ftmp_nu_int[ss,:] = data_int(lm, wavetmp, spec_mul_nu[ss,:])
+                    
+                    # Register filter response;
+                    ltmpbb[ss,:], ftmpbb[ss,:] = filconv(SFILT, wavetmp, spec_mul_nu[ss,:], DIR_FILT, MB=MB, f_regist=False)
+
+
+                    ##########################################
+                    # Writing out the templates to fits table.
+                    ##########################################
+                    if ss == 0 and tt == 0 and zz == 0:
+                        # First file
+                        nd1    = np.arange(0,len(lm),1)
+                        nd3    = np.arange(10000,10000+len(ltmpbb[ss,:]),1)
+                        nd_ap  = np.append(nd1,nd3)
+                        lm_ap  = np.append(lm, ltmpbb[ss,:])
+
+                        # ASDF
+                        tree_spec.update({'wavelength':lm_ap})
+                        tree_spec.update({'colnum':nd_ap})
 
+                        # Second file
+                        # ASDF
+                        nd = np.arange(0,len(wavetmp),1)
+                        tree_spec_full.update({'wavelength':wavetmp})
+                        tree_spec_full.update({'colnum':nd})
+
+                    # ASDF
+                    # ???
+                    spec_ap = np.append(ftmp_nu_int[ss,:], ftmpbb[ss,:])
+                    tree_spec.update({'fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss): spec_ap})
+                    tree_spec_full.update({'fspec_orig_'+str(zz)+'_'+str(tt)+'_'+str(ss): spec_mul_nu[ss,:]})                
+                    #tree_spec_full.update({'fspec_'+str(zz)+'_'+str(tt)+'_'+str(ss): spec_mul_nu_conv[ss,:]})
+
+                    # Nebular library;
+                    if fneb == 1 and MB.f_bpass==0 and ss==0 and tt==0:
+                        if zz==0:
+                            spec_mul_neb = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                            spec_mul_neb_nu = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                            spec_mul_neb_nu_conv = np.zeros((len(Z), len(MB.logUs), len(lm0)), dtype=float)
+                            ftmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
+                            ltmpbb_neb = np.zeros((len(Z), len(MB.logUs), len(SFILT)), dtype=float)
+                            ftmp_neb_nu_int = np.zeros((len(Z), len(MB.logUs), len(lm)), dtype=float)
+
+                        for uu in range(len(MB.logUs)):
+                            if delwave>0:
+                                fint = interpolate.interp1d(lm0_orig, spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin], kind='nearest', fill_value="extrapolate")
+                                spec_mul_neb[zz,uu,:] = fint(lm0)
+                            else:
+                                spec_mul_neb[zz,uu,:] = spechdu['flux_nebular_Z%d_logU%d'%(zz,uu)][::nthin]
+                            
+                            con_neb = (spec_mul_neb[zz,uu,:]<0)
+                            spec_mul_neb[zz,uu,:][con_neb] = 0
+                            
+                            if f_IGM:
+                                # spec_neb_av_tmp = madau_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
+                                spec_neb_av_tmp, x_HI = dijkstra_igm_abs(wave, spec_mul_neb[zz,uu,:], zbest, cosmo=MB.cosmo)
+                                spec_mul_neb[zz,uu,:] = spec_neb_av_tmp
+
+                            spec_mul_neb_nu[zz,uu,:] = flamtonu(wave, spec_mul_neb[zz,uu,:], m0set=MB.m0set)
+                            spec_mul_neb_nu[zz,uu,:] *= MB.Lsun/(4.*np.pi*DL**2/(1.+zbest))
+                            spec_mul_neb_nu[zz,uu,:] *= (1./Ls[ss])*tmp_norm # in unit of erg/s/Hz/cm2/ms[ss].
+                            ltmpbb_neb[zz,uu,:], ftmpbb_neb[zz,uu,:] = filconv(SFILT, wavetmp, spec_mul_neb_nu[zz,uu,:], DIR_FILT, MB=MB, f_regist=False)
+
+                            if MB.f_spec:
+                                ftmp_neb_nu_int[zz,uu,:] = data_int(lm, wavetmp, spec_mul_neb_nu[zz,uu,:])
+
+                            if len(lm)>0:
+                                try:
+                                    spec_mul_neb_nu_conv[zz,uu,:] = convolve(spec_mul_neb_nu[zz,uu,:], LSF, boundary='extend')
+                                except:
+                                    spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
+                            else:
+                                spec_mul_neb_nu_conv[zz,uu,:] = spec_mul_neb_nu[zz,uu,:]
+
+                            tree_spec_full.update({'fspec_orig_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu[zz,uu,:]})
+                            tree_spec_full.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_mul_neb_nu_conv[zz,uu,:]})
+
+                            spec_neb_ap = np.append(ftmp_neb_nu_int[zz,uu,:], ftmpbb_neb[zz,uu,:])
+                            tree_spec.update({'fspec_nebular_Z%d_logU%d'%(zz,uu): spec_neb_ap})
+
+                #########################
+                # Summarize the ML
+                #########################
+                # ASDF
+                tree_ML.update({'ML_'+str(zz)+'_'+str(tt): ms})
 
     #########################
     # Summarize the templates
diff --git a/gsf/plot_sed.py b/gsf/plot_sed.py
index 6a5bd89..7f31b8b 100644
--- a/gsf/plot_sed.py
+++ b/gsf/plot_sed.py
@@ -1777,8 +1777,8 @@ def gaus(x,a,x0,sigma):
     alp = .5
 
     # Get total templates
-    y0p, x0p = MB.fnc.tmp04(vals, f_val=False, check_bound=False)
-    y0, x0 = MB.fnc.tmp04(vals, f_val=False, check_bound=False, lib_all=True)
+    y0p, x0p = MB.fnc.get_template(vals, f_val=False, check_bound=False)
+    y0, x0 = MB.fnc.get_template(vals, f_val=False, check_bound=False, lib_all=True)
 
     ysum = y0
     f_50_comp = y0[:] * c / np.square(x0) / d
@@ -1797,7 +1797,7 @@ def gaus(x,a,x0,sigma):
         vals_each['A%d'%aa] = -99
     for aa in range(len(age)):
         vals_each['A%d'%aa] = vals['A%d'%aa]
-        y0tmp, x0tmp = MB.fnc.tmp04(vals_each, f_val=False, check_bound=False, lib_all=True)
+        y0tmp, x0tmp = MB.fnc.get_template(vals_each, f_val=False, check_bound=False, lib_all=True)
         if aa == 0:
             y0keep = y0tmp
         else:
@@ -1988,7 +1988,7 @@ def gaus(x,a,x0,sigma):
                     ZZtmp = MB.ZFIX
                 vals['Z%d'%ss] = ZZtmp
 
-        mod0_tmp, xm_tmp = fnc.tmp04(vals, f_val=False, check_bound=False, lib_all=True)
+        mod0_tmp, xm_tmp = fnc.get_template(vals, f_val=False, check_bound=False, lib_all=True)
         fm_tmp = mod0_tmp
 
         if False:

From 38d95d94e779ddeb15be30bd0b1f7b0876c37eb1 Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Sun, 11 Dec 2022 14:24:28 -0800
Subject: [PATCH 11/14] tau model updating

There is still a bug when add_nebular is on.
---
 gsf/function_class.py | 265 +++++++++---------------------------------
 gsf/maketmp_z0_tau.py |   4 +-
 gsf/plot_sed.py       |  28 +++++
 3 files changed, 87 insertions(+), 210 deletions(-)

diff --git a/gsf/function_class.py b/gsf/function_class.py
index b1f29ff..f0ca94a 100644
--- a/gsf/function_class.py
+++ b/gsf/function_class.py
@@ -397,7 +397,7 @@ def get_template(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib
 
         # @@@ Filter convolution may need to happpen here
         if round(zmc,nprec) != round(self.MB.zgal,nprec):
-            if np.abs(zmc - self.MB.zgal) > deltaz_lim and not self.MB.f_spec:
+            if np.abs(zmc - self.MB.zgal) > deltaz_lim:
                 # print('!!! zmc (%.3f) is exploring too far from zgal (%.3f).'%(zmc, self.MB.zgal))
 
                 # @@@ This only work for BB only data set.
@@ -411,20 +411,13 @@ def get_template(self, par, f_Alog:bool=True, nprec:int=1, f_val:bool=False, lib
                         nr_full, xx_full, yy_full = self.get_total_flux(par, f_Alog=f_Alog, lib_all=True)
 
                 xx_s, yy_s = filconv(self.MB.filts, xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, self.MB.DIR_FILT, MB=self.MB, f_regist=False)
-            elif np.abs(zmc - self.MB.zgal) > deltaz_lim and self.MB.f_spec:
-                if lib_all:
-                    nr_full, xx_full, yy_full = nr, xx, yy
-                else:
-                    if f_neb:
-                        nr_full, xx_full, yy_full = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=True)
-                    else:
-                        nr_full, xx_full, yy_full = self.get_total_flux(par, f_Alog=f_Alog, lib_all=True)
-                xx_bb, yy_bb = filconv(self.MB.filts, xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, self.MB.DIR_FILT, MB=self.MB, f_regist=False)
-                con_bb = (nr>=self.MB.NRbb_lim)
-                xx[con_bb] = xx_bb
-                yy[con_bb] = yy_bb
-                xx_s = xx
-                yy_s = yy
+                
+                if self.MB.f_spec:
+                    con_bb = (nr>=self.MB.NRbb_lim)
+                    xx[con_bb] = xx_s
+                    yy[con_bb] = yy_s
+                    xx_s = xx
+                    yy_s = yy
 
             else:
                 fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
@@ -534,49 +527,6 @@ def __init__(self, MB, dust_model:int=0):
         self.f_af = False
         self.f_af0 = False
 
-    """
-    def open_spec_fits(self, fall:int=0, orig:bool=False):
-        '''
-        Loads template in obs range.
-        '''
-        ID0 = self.MB.ID
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-        DIR_TMP = self.DIR_TMP
-
-        # ASDF;
-        if fall == 0:
-            app = ''
-            hdu0 = self.MB.af['spec']
-        elif fall == 1:
-            app = 'all_'
-            hdu0 = self.MB.af['spec_full']
-
-        NZ = len(ZZ)
-        NT = self.MB.ntau
-        NA = self.MB.nage
-        for zz,Z in enumerate(ZZ):
-            for tt,TT in enumerate(self.MB.tau):                
-                for ss,TA in enumerate(self.MB.ageparam):
-                    if zz == 0 and tt == 0 and ss == 0:
-                        nr = hdu0['colnum']
-                        xx = hdu0['wavelength']
-                        coln = int(2 + NZ * NT * NA)
-                        lib = np.zeros((len(nr), coln), dtype=float)
-                        lib[:,0] = nr[:]
-                        lib[:,1] = xx[:]
-
-                    if orig:
-                        colname = 'fspec_orig_' + str(zz) + '_' + str(tt) + '_' + str(ss)
-                    else:
-                        colname = 'fspec_' + str(zz) + '_' + str(tt) + '_' + str(ss)
-                    colnall = int(2 + zz * NT * NA + tt * NA + ss) # 2 takes account of wavelength and AV columns.
-                    lib[:,colnall] = hdu0[colname]
-
-        return lib
-    """
-
 
     def open_spec_dust_fits(self, fall:int = 0):
         '''
@@ -678,61 +628,6 @@ def open_spec_fits(self, fall=0, orig=False, f_neb=False):
         return lib
 
 
-    """
-    def open_spec_fits_dir(self, nage, nz, kk, Av, zgal, A00):
-        '''
-        Loads template in obs range.
-        But for weird template.
-        '''
-        tau0= self.tau0
-        ZZ = self.ZZ
-        AA = self.AA
-        bfnc = self.MB.bfnc
-
-        app = 'all'
-        hdu0 = self.MB.af['spec_full']
-        DIR_TMP = self.DIR_TMP
-
-        pp = 0
-        zz = nz
-
-        # Luminosity
-        mshdu = self.MB.af0['ML']
-        Ls = mshdu['Ls_%d'%nz] 
-
-        xx = hdu0['wavelength'] # at RF;
-        nr = np.arange(0,len(xx),1) #hdu0.data['colnum']
-
-        lib = np.zeros((len(nr), 2+1), dtype='float')
-        lib[:,0] = nr[:]
-        lib[:,1] = xx[:]
-
-        aa = nage
-        coln = int(2 + aa)
-        colname = 'fspec_' + str(zz) + '_' + str(aa) + '_' + str(pp)
-
-        yy0 = hdu0[colname]/Ls[aa]
-        yy = flamtonu(xx, yy0, m0set=self.MB.m0set)
-        lib[:,2] = yy[:]
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zgal), nr, Av, dust_model=self.dust_model)
-        xxd *= (1.+zgal)
-
-        if self.dust_model == 0:
-            return A00 * yyd, xxd
-        else:
-            nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-            nrd_yyd[:,0] = nrd[:]
-            nrd_yyd[:,1] = yyd[:]
-            nrd_yyd[:,2] = xxd[:]
-
-            b = nrd_yyd
-            nrd_yyd_sort = b[np.lexsort(([-1,1]*b[:,[1,0]]).T)]
-            yyd_sort = nrd_yyd_sort[:,1]
-            xxd_sort = nrd_yyd_sort[:,2]
-            return A00 * yyd_sort, xxd_sort
-    """
-
     def get_total_flux(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_get_Mtot=False, f_check_limit=True):
         '''
         '''
@@ -816,10 +711,11 @@ def get_total_flux_neb(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_g
         # logU
         NU = len(self.MB.logUs)
         # Check limit;
-        if Aneb < self.MB.Amin:
-            Aneb = self.MB.Amin
-        if Aneb > self.MB.Amax:
-            Aneb = self.MB.Amax
+        if f_check_limit:
+            if Aneb < self.MB.Amin:
+                Aneb = self.MB.Amin
+            if Aneb > self.MB.Amax:
+                Aneb = self.MB.Amax
 
         # Is A in logspace?
         if f_Alog:
@@ -851,83 +747,6 @@ def get_total_flux_neb(self, par, f_Alog=True, lib_all=True, pp=0, lib=None, f_g
         else:
             return nr, xx, yy
 
-    """
-    def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, lib_all=False, f_nrd=False, 
-        f_neb=False, EBVratio:float=2.27):
-        '''
-        Makes model template with a given param set.
-        Also dust attenuation.
-
-        Parameters:
-        -----------
-        nprec : int
-            Precision when redshift is refined. 
-        '''
-        ZZ = self.ZZ
-        AA = self.AA 
-        bfnc = self.MB.bfnc
-        Mtot = 0
-        pp = 0
-
-        if f_val:
-            par = par.params
-
-        if self.MB.fzmc == 1:
-            try:
-                zmc = par['zmc'].value
-            except:
-                zmc = self.MB.zgal
-        else:
-            zmc = self.MB.zgal
-
-        if check_bound:
-            # AV limit;
-            if par['Av'] < self.MB.Avmin:
-                par['Av'] = self.MB.Avmin
-            if par['Av'] > self.MB.Avmax:
-                par['Av'] = self.MB.Avmax
-        Av = par['Av']
-
-        if f_neb:
-            nr, xx, yy, Mtot = get_total_flux_neb(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, lib=None, f_get_Mtot=True, f_check_limit=True)
-        else:
-            nr, xx, yy, Mtot = get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, lib=None, f_get_Mtot=True, f_check_limit=True)
-
-        # Keep logM
-        self.MB.logMtmp = np.log10(Mtot)
-
-        # Redshift refinement;
-        if round(zmc,nprec) != round(self.MB.zgal,nprec): # Not sure how much this costs in time.
-            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
-            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
-            yy_s = fint(xx_s)
-        else:
-            xx_s = xx
-            yy_s = yy
-
-        xx = xx_s
-        yy = yy_s
-
-        yyd, xxd, nrd = apply_dust(yy, xx/(1.+zmc), nr, Av, dust_model=self.dust_model)
-
-        xxd *= (1.+zmc)
-
-        if self.dust_model == 0:
-            if not f_nrd:
-                return yyd,xxd
-            else:
-                return nrd,yyd,xxd
-        else:
-            nrd_yyd = np.zeros((len(nrd),3), dtype='float')
-            nrd_yyd[:,0] = nrd[:]
-            nrd_yyd[:,1] = yyd[:]
-            nrd_yyd[:,2] = xxd[:]
-            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
-            if not f_nrd:
-                return nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
-            else:
-                return nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
-    """
 
     def tmp04_dust(self, par, nprec=1):
         '''
@@ -967,7 +786,7 @@ def tmp04_dust(self, par, nprec=1):
 
   
     def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False, 
-        lib_all=False, lib=None, f_nrd=False, f_apply_dust=True, f_neb=False):
+        lib_all=False, lib=None, f_nrd=False, f_apply_dust=True, f_neb=False, deltaz_lim=0.1):
         '''
         Makes model template with a given param set.
         Also dust attenuation.
@@ -1010,15 +829,40 @@ def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False
 
         if f_neb:
             # @@@ Not clear why f_check_limit cannot work
-            nr, xx, yy, Mtot = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, lib=lib, f_get_Mtot=True, f_check_limit=False)#, f_check_limit=True)
+            nr, xx, yy, Mtot = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, lib=lib, f_get_Mtot=True, f_check_limit=check_bound)#, f_check_limit=True)
         else:
             # @@@ Not clear why f_check_limit cannot work
-            nr, xx, yy, Mtot = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, lib=lib, f_get_Mtot=True, f_check_limit=False)#, f_check_limit=True)
+            nr, xx, yy, Mtot = self.get_total_flux(par, f_Alog=f_Alog, lib_all=lib_all, pp=pp, lib=lib, f_get_Mtot=True, f_check_limit=check_bound)#, f_check_limit=True)
 
+        # @@@ Filter convolution may need to happpen here
         if round(zmc,nprec) != round(self.MB.zgal,nprec):
-            xx_s = xx / (1+self.MB.zgal) * (1+zmc)
-            fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
-            yy_s = fint(xx_s)
+            if np.abs(zmc - self.MB.zgal) > deltaz_lim:
+                # print('!!! zmc (%.3f) is exploring too far from zgal (%.3f).'%(zmc, self.MB.zgal))
+
+                # @@@ This only work for BB only data set.
+                # Get total flux;
+                if lib_all:
+                    nr_full, xx_full, yy_full = nr, xx, yy
+                else:
+                    if f_neb:
+                        nr_full, xx_full, yy_full = self.get_total_flux_neb(par, f_Alog=f_Alog, lib_all=True, lib=lib)
+                    else:
+                        nr_full, xx_full, yy_full = self.get_total_flux(par, f_Alog=f_Alog, lib_all=True, lib=lib)
+
+                xx_s, yy_s = filconv(self.MB.filts, xx_full / (1+self.MB.zgal) * (1+zmc), yy_full, self.MB.DIR_FILT, MB=self.MB, f_regist=False)
+                
+                if self.MB.f_spec:
+                    con_bb = (nr>=self.MB.NRbb_lim)
+                    xx[con_bb] = xx_s
+                    yy[con_bb] = yy_s
+                    xx_s = xx
+                    yy_s = yy
+
+            else:
+                fint = interpolate.interp1d(xx, yy, kind='nearest', fill_value="extrapolate")
+                xx_s = xx / (1+self.MB.zgal) * (1+zmc)
+                yy_s = fint(xx_s)
+
         else:
             xx_s = xx
             yy_s = yy
@@ -1027,22 +871,25 @@ def get_template(self, par, f_Alog=True, nprec=1, f_val=False, check_bound=False
         yy = yy_s
 
         if f_apply_dust:
-            yyd, xxd, nrd = apply_dust(yy, xx/(1.+zmc), nr, Av, dust_model=self.dust_model)
+            yyd, xxd, nrd = apply_dust(yy, xx/(1+zmc), nr, Av, dust_model=self.dust_model)
             xxd *= (1.+zmc)
 
-            nrd_yyd = np.zeros((len(nrd),3), dtype=float)
-            nrd_yyd[:,0] = nrd[:]
-            nrd_yyd[:,1] = yyd[:]
-            nrd_yyd[:,2] = xxd[:]
-            nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
+            if self.dust_model != 0:
+                nrd_yyd = np.zeros((len(nrd),3), dtype=float)
+                nrd_yyd[:,0] = nrd[:]
+                nrd_yyd[:,1] = yyd[:]
+                nrd_yyd[:,2] = xxd[:]
+                nrd_yyd_sort = nrd_yyd[nrd_yyd[:,0].argsort()]
+                nrd[:],yyd[:],xxd[:] = nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
 
             if not f_nrd:
-                return nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
+                return yyd[:],xxd[:]
             else:
-                return nrd_yyd_sort[:,0],nrd_yyd_sort[:,1],nrd_yyd_sort[:,2]
+                return nrd[:],yyd[:],xxd[:]
 
         else:
             if not f_nrd:
                 return yy,xx
             else:
-                return nr, yy,xx
+                return nr,yy,xx
+
diff --git a/gsf/maketmp_z0_tau.py b/gsf/maketmp_z0_tau.py
index fa66b40..0b9f77b 100644
--- a/gsf/maketmp_z0_tau.py
+++ b/gsf/maketmp_z0_tau.py
@@ -150,6 +150,9 @@ def make_tmp_z0(MB, lammin=100, lammax=160000, Zforce=None):
                         'nimf': nimf,
                         'version_gsf': gsf.__version__
                     }
+                    tree.update({'age': MB.age})
+                    tree.update({'tau': MB.tau})
+                    tree.update({'Z': MB.Zall})
                     if fneb == 1:
                         tree.update({'logUMIN': MB.logUMIN})
                         tree.update({'logUMAX': MB.logUMAX})
@@ -168,7 +171,6 @@ def make_tmp_z0(MB, lammin=100, lammax=160000, Zforce=None):
             tree_ML.update({'Ls_'+str(zz)+'_'+str(ss): Ls})
             tree_ML.update({'frac_mass_survive_'+str(zz)+'_'+str(ss): mlost})
 
-
     # Write;
     for aa in range(len(tau)):
         tree.update({'tau%d'%(aa): tau[aa]})
diff --git a/gsf/plot_sed.py b/gsf/plot_sed.py
index 7f31b8b..dcfb10f 100644
--- a/gsf/plot_sed.py
+++ b/gsf/plot_sed.py
@@ -1491,6 +1491,10 @@ def gaus(x,a,x0,sigma):
         vals['TAU'+str(aa)] = np.log10(TAU50[aa])
         vals['AGE'+str(aa)] = np.log10(AGE50[aa])
 
+    if MB.fneb:
+        logU50 = hdul[1].data['logU'][1]
+        Aneb50 = 10**hdul[1].data['Aneb'][1]
+
     aa = 0
     Av16 = hdul[1].data['Av'+str(aa)][0]
     Av50 = hdul[1].data['Av'+str(aa)][1]
@@ -1726,6 +1730,9 @@ def gaus(x,a,x0,sigma):
         Temp = np.arange(DT0,DT1,dDT)
         MB.lib_dust = fnc.open_spec_dust_fits(fall=0)
         MB.lib_dust_all = fnc.open_spec_dust_fits(fall=1)
+    if MB.fneb:
+        lib_neb = MB.fnc.open_spec_fits(fall=0, f_neb=True)
+        lib_neb_all = MB.fnc.open_spec_fits(fall=1, orig=True, f_neb=True)
 
     # FIR dust plot;
     if f_dust:
@@ -1791,6 +1798,13 @@ def gaus(x,a,x0,sigma):
         ysump = np.append(ysump,y0d_cut[nopt:])
         f_50_comp_dust = y0d * c / np.square(x0d) / d
 
+    if MB.fneb: 
+        # Only at one age pixel;
+        y0p, x0p = MB.fnc.get_template(vals, f_val=False, check_bound=False, f_neb=True)
+        y0_r, x0_tmp = MB.fnc.get_template(vals, f_val=False, check_bound=False, f_neb=True, lib_all=True)
+        ysum += y0_r
+        ysump[:nopt] += y0p
+
     # Plot each best fit:
     vals_each = vals.copy()
     for aa in range(len(age)):
@@ -1991,6 +2005,20 @@ def gaus(x,a,x0,sigma):
         mod0_tmp, xm_tmp = fnc.get_template(vals, f_val=False, check_bound=False, lib_all=True)
         fm_tmp = mod0_tmp
 
+        if MB.fneb:
+            Aneb_tmp = 10**samples['Aneb'][nr]
+            if not MB.logUFIX == None:
+                logU_tmp = MB.logUFIX
+            else:
+                logU_tmp = samples['logU'][nr]
+            mod0_tmp, xm_tmp = fnc.get_template(vals, f_val=False, check_bound=False, lib_all=True, f_neb=False)
+            # mod0_tmp, xm_tmp = fnc.get_template(Aneb_tmp, Av_tmp, ss, ZZ_tmp, zmc, lib_neb_all, logU=logU_tmp)
+            fm_tmp += mod0_tmp
+            # # Make no emission line template;
+            # mod0_tmp_nl, xm_tmp_nl = fnc.get_template(0, Av_tmp, ss, ZZ_tmp, zmc, lib_neb_all, logU=logU_tmp)
+            # fm_tmp_nl += mod0_tmp_nl
+
+
         if False:
             # Each;
             ytmp_each[kk,:,ss] = mod0_tmp[:] * c / np.square(xm_tmp[:]) / d

From 1d8238e7b62e63c2dea379d56e9e4d5be5a34da2 Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Sun, 11 Dec 2022 20:20:58 -0800
Subject: [PATCH 12/14] docs

---
 docs/_build/doctrees/auto/gsf.doctree       | Bin 920751 -> 918856 bytes
 docs/_build/doctrees/environment.pickle     | Bin 70118 -> 69906 bytes
 docs/_build/doctrees/templates.doctree      | Bin 14055 -> 14468 bytes
 docs/_build/html/_sources/templates.rst.txt |  12 +++----
 docs/_build/html/auto/gsf.html              |  38 +++++++-------------
 docs/_build/html/genindex.html              |  22 ++++++------
 docs/_build/html/objects.inv                | Bin 1794 -> 1789 bytes
 docs/_build/html/searchindex.js             |   2 +-
 docs/_build/html/templates.html             |  13 +++----
 docs/templates.rst                          |  12 +++----
 10 files changed, 44 insertions(+), 55 deletions(-)

diff --git a/docs/_build/doctrees/auto/gsf.doctree b/docs/_build/doctrees/auto/gsf.doctree
index 8fbfc7fe26a3596ec9562aadf115e5795dff87c8..276a7c0c70ea1e5758540a1679fc0d44afc74d0e 100644
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diff --git a/docs/_build/doctrees/templates.doctree b/docs/_build/doctrees/templates.doctree
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diff --git a/docs/_build/html/_sources/templates.rst.txt b/docs/_build/html/_sources/templates.rst.txt
index 2f88b8f..a29ac14 100644
--- a/docs/_build/html/_sources/templates.rst.txt
+++ b/docs/_build/html/_sources/templates.rst.txt
@@ -13,7 +13,7 @@ in the configuration file. The parameters are limited from the original fsps (`C
 such as the range in metallicity, initial mass function (IMF), and ionizing parameter. 
 
 It is in this step that gsf generates templates of different ages and metallicities. 
-The templates are saved in a file, "spec\_all.asdf" (irrespective to object IDs), 
+The templates are saved in a file, ``spec_all.asdf`` (irrespective to object IDs), 
 which can be shared for other objects for the purpose of saving time. 
 Redshifted templates (below), which are actually 
 used in the fitting process, are generated by starting off from this common file. 
@@ -34,7 +34,7 @@ If set to a negative value, then SSP is applied.
 
 Shifting templates to the input redshift (Execution flag = 1)
 -------------------------------------------------------------
-In this step (specifically, `maketemp` module), gsf shifts the rest-frame templates generated above (i.e. those in "spec\_all.asdf") 
+In this step (specifically, `maketemp` module), gsf shifts the rest-frame templates generated above (i.e. those in ``spec_all.asdf``) 
 to the input redshift and calculates fluxes of the input filters for one-to-one comparison. 
 Basically, there are three steps;
 
@@ -42,14 +42,14 @@ Basically, there are three steps;
 
 .. math::
 
-    \mathrm{wave_{obs}} = \mathrm{wave_{rest-frame}} (1 + z)
+    \mathrm{wave_{obs}} = \mathrm{wave_{rf}} (1 + z)
 
 
 - Scaling flux:
 
 .. math::
 
-    \mathrm{flux_{obs}} \propto \mathrm{flux_{rest-frame}} {4 \pi d_{L}^2 \over{(1+z)}}
+    \mathrm{flux_{obs}} \propto \mathrm{flux_{rf}} {4 \pi d_{L}^2 \over{(1+z)}}
 
 where :math:`d_L` is luminosity distance to the input redshift.
 
@@ -61,6 +61,6 @@ where :math:`d_L` is luminosity distance to the input redshift.
     \mathrm{flux_{atten}} = \mathrm{flux_{int}} \exp{(-\tau)}
 
 
-where gsf applies the Madau model for IGM attenuation at :math:`z>6`.
+where gsf applies `Dijkstra 2014 <https://ui.adsabs.harvard.edu/abs/2014PASA...31...40D/>`__ for IGM attenuation.
 
-gsf will store the files generated here in a directory specified with ``DIR_TEMP``.
+gsf will store the files generated here in the directory specified with ``DIR_TEMP``.
diff --git a/docs/_build/html/auto/gsf.html b/docs/_build/html/auto/gsf.html
index b988cda..cb58435 100644
--- a/docs/_build/html/auto/gsf.html
+++ b/docs/_build/html/auto/gsf.html
@@ -145,7 +145,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 </dl>
 <dl class="py class">
 <dt class="sig sig-object py" id="gsf.fitting.Mainbody">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">gsf.fitting.</span></span><span class="sig-name descname"><span class="pre">Mainbody</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3e+18</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Mpc_cm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3.08568025e+24</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m0set</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">25.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixelscale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Lsun</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3.839e+33</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cosmo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">gsf.fitting.</span></span><span class="sig-name descname"><span class="pre">Mainbody</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3e+18</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Mpc_cm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3.08568025e+24</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m0set</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">25.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixelscale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Lsun</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">3.839e+33</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cosmo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zman</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">NRbb_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.fitting.Mainbody" title="Permalink to this definition"></a></dt>
 <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
 <p>The list of (possible) <cite>Mainbody</cite> attributes is given below:</p>
 <dl class="simple">
@@ -820,7 +820,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dt>orig<span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bool</span></code></span></dt><dd><p>If True, returns the original spectra.</p>
 </dd>
 </dl>
-<p>float array</p>
+<p>lib : float array</p>
 </dd></dl>
 
 <dl class="py method">
@@ -838,7 +838,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.function_class.Func_tau.get_template">
-<span class="sig-name descname"><span class="pre">get_template</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_nrd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.get_template" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">get_template</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_nrd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_apply_dust</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_neb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deltaz_lim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.get_template" title="Permalink to this definition"></a></dt>
 <dd><p>Makes model template with a given param set.
 Also dust attenuation.</p>
 <dl class="simple">
@@ -848,15 +848,14 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func_tau.get_template_neb">
-<span class="sig-name descname"><span class="pre">get_template_neb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_nrd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_apply_dust</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.get_template_neb" title="Permalink to this definition"></a></dt>
-<dd><p>Makes model template with a given param set.
-Also dust attenuation.</p>
-<dl class="simple">
-<dt>nprec<span class="classifier">int</span></dt><dd><p>Precision when redshift is refined.</p>
-</dd>
-</dl>
-</dd></dl>
+<dt class="sig sig-object py" id="gsf.function_class.Func_tau.get_total_flux">
+<span class="sig-name descname"><span class="pre">get_total_flux</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_get_Mtot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_check_limit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.get_total_flux" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="gsf.function_class.Func_tau.get_total_flux_neb">
+<span class="sig-name descname"><span class="pre">get_total_flux_neb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">par</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_Alog</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib_all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_get_Mtot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_check_limit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.get_total_flux_neb" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.function_class.Func_tau.open_spec_dust_fits">
@@ -866,20 +865,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="gsf.function_class.Func_tau.open_spec_fits">
-<span class="sig-name descname"><span class="pre">open_spec_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orig</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.open_spec_fits" title="Permalink to this definition"></a></dt>
-<dd><p>Loads template in obs range.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func_tau.open_spec_fits_dir">
-<span class="sig-name descname"><span class="pre">open_spec_fits_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kk</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Av</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zgal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">A00</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.open_spec_fits_dir" title="Permalink to this definition"></a></dt>
-<dd><p>Loads template in obs range.
-But for weird template.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="gsf.function_class.Func_tau.open_spec_neb_fits">
-<span class="sig-name descname"><span class="pre">open_spec_neb_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.open_spec_neb_fits" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">open_spec_fits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fall</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_neb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#gsf.function_class.Func_tau.open_spec_fits" title="Permalink to this definition"></a></dt>
 <dd><p>Loads template in obs range.</p>
 </dd></dl>
 
diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html
index bc2ce22..2de2192 100644
--- a/docs/_build/html/genindex.html
+++ b/docs/_build/html/genindex.html
@@ -301,18 +301,24 @@ <h2 id="G">G</h2>
         <li><a href="auto/gsf.html#gsf.function_class.Func_tau.get_template">(gsf.function_class.Func_tau method)</a>
 </li>
       </ul></li>
-      <li><a href="auto/gsf.html#gsf.function_class.Func_tau.get_template_neb">get_template_neb() (gsf.function_class.Func_tau method)</a>
-</li>
       <li><a href="auto/gsf.html#gsf.function_class.Func.get_template_single">get_template_single() (gsf.function_class.Func method)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.function_class.Func.get_total_flux">get_total_flux() (gsf.function_class.Func method)</a>
+
+      <ul>
+        <li><a href="auto/gsf.html#gsf.function_class.Func_tau.get_total_flux">(gsf.function_class.Func_tau method)</a>
 </li>
+      </ul></li>
       <li><a href="auto/gsf.html#gsf.function_class.Func.get_total_flux_neb">get_total_flux_neb() (gsf.function_class.Func method)</a>
+
+      <ul>
+        <li><a href="auto/gsf.html#gsf.function_class.Func_tau.get_total_flux_neb">(gsf.function_class.Func_tau method)</a>
 </li>
-      <li><a href="auto/gsf.html#gsf.function.get_uvbeta">get_uvbeta() (in module gsf.function)</a>
-</li>
+      </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="auto/gsf.html#gsf.function.get_uvbeta">get_uvbeta() (in module gsf.function)</a>
+</li>
       <li><a href="auto/gsf.html#gsf.function_igm.get_XI">get_XI() (in module gsf.function_igm)</a>
 </li>
       <li><a href="auto/gsf.html#gsf.fitting.Mainbody.get_zdist">get_zdist() (gsf.fitting.Mainbody method)</a>
@@ -533,6 +539,8 @@ <h2 id="O">O</h2>
         <li><a href="auto/gsf.html#gsf.function_class.Func_tau.open_spec_dust_fits">(gsf.function_class.Func_tau method)</a>
 </li>
       </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="auto/gsf.html#gsf.function_class.Func.open_spec_fits">open_spec_fits() (gsf.function_class.Func method)</a>
 
       <ul>
@@ -540,12 +548,6 @@ <h2 id="O">O</h2>
 </li>
       </ul></li>
   </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="auto/gsf.html#gsf.function_class.Func_tau.open_spec_fits_dir">open_spec_fits_dir() (gsf.function_class.Func_tau method)</a>
-</li>
-      <li><a href="auto/gsf.html#gsf.function_class.Func_tau.open_spec_neb_fits">open_spec_neb_fits() (gsf.function_class.Func_tau method)</a>
-</li>
-  </ul></td>
 </tr></table>
 
 <h2 id="P">P</h2>
diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv
index 72a5c4d8b355f3a1631ceaadb72e1e5daa91ff5c..f402821bfdc5b7709fbfd17cb241b11ea8e6a372 100644
GIT binary patch
delta 1660
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\ No newline at end of file
diff --git a/docs/_build/html/templates.html b/docs/_build/html/templates.html
index 285e514..1e43d09 100644
--- a/docs/_build/html/templates.html
+++ b/docs/_build/html/templates.html
@@ -55,6 +55,7 @@
 <li class="toctree-l1"><a class="reference internal" href="fitting.html">Fitting process</a></li>
 <li class="toctree-l1"><a class="reference internal" href="output.html">Output files</a></li>
 <li class="toctree-l1"><a class="reference internal" href="prior.html">Prior</a></li>
+<li class="toctree-l1"><a class="reference internal" href="redshift_fitting.html">Redshift Fitting</a></li>
 <li class="toctree-l1"><a class="reference internal" href="auto/modules.html">gsf</a></li>
 </ul>
 
@@ -91,7 +92,7 @@
 in the configuration file. The parameters are limited from the original fsps (<a class="reference external" href="http://adsabs.harvard.edu/abs/2009ApJ...699..486C">Conroy et al. 2009</a>),
 such as the range in metallicity, initial mass function (IMF), and ionizing parameter.</p>
 <p>It is in this step that gsf generates templates of different ages and metallicities.
-The templates are saved in a file, “spec_all.asdf” (irrespective to object IDs),
+The templates are saved in a file, <code class="docutils literal notranslate"><span class="pre">spec_all.asdf</span></code> (irrespective to object IDs),
 which can be shared for other objects for the purpose of saving time.
 Redshifted templates (below), which are actually
 used in the fitting process, are generated by starting off from this common file.
@@ -107,27 +108,27 @@
 </section>
 <section id="shifting-templates-to-the-input-redshift-execution-flag-1">
 <span id="template-z"></span><h2>Shifting templates to the input redshift (Execution flag = 1)<a class="headerlink" href="#shifting-templates-to-the-input-redshift-execution-flag-1" title="Permalink to this headline"></a></h2>
-<p>In this step (specifically, <cite>maketemp</cite> module), gsf shifts the rest-frame templates generated above (i.e. those in “spec_all.asdf”)
+<p>In this step (specifically, <cite>maketemp</cite> module), gsf shifts the rest-frame templates generated above (i.e. those in <code class="docutils literal notranslate"><span class="pre">spec_all.asdf</span></code>)
 to the input redshift and calculates fluxes of the input filters for one-to-one comparison.
 Basically, there are three steps;</p>
 <ul class="simple">
 <li><p>Redshift templates:</p></li>
 </ul>
 <div class="math notranslate nohighlight">
-\[\mathrm{wave_{obs}} = \mathrm{wave_{rest-frame}} (1 + z)\]</div>
+\[\mathrm{wave_{obs}} = \mathrm{wave_{rf}} (1 + z)\]</div>
 <ul class="simple">
 <li><p>Scaling flux:</p></li>
 </ul>
 <div class="math notranslate nohighlight">
-\[\mathrm{flux_{obs}} \propto \mathrm{flux_{rest-frame}} {4 \pi d_{L}^2 \over{(1+z)}}\]</div>
+\[\mathrm{flux_{obs}} \propto \mathrm{flux_{rf}} {4 \pi d_{L}^2 \over{(1+z)}}\]</div>
 <p>where <span class="math notranslate nohighlight">\(d_L\)</span> is luminosity distance to the input redshift.</p>
 <ul class="simple">
 <li><p>IGM attenuation:</p></li>
 </ul>
 <div class="math notranslate nohighlight">
 \[\mathrm{flux_{atten}} = \mathrm{flux_{int}} \exp{(-\tau)}\]</div>
-<p>where gsf applies the Madau model for IGM attenuation at <span class="math notranslate nohighlight">\(z&gt;6\)</span>.</p>
-<p>gsf will store the files generated here in a directory specified with <code class="docutils literal notranslate"><span class="pre">DIR_TEMP</span></code>.</p>
+<p>where gsf applies <a class="reference external" href="https://ui.adsabs.harvard.edu/abs/2014PASA...31...40D/">Dijkstra 2014</a> for IGM attenuation.</p>
+<p>gsf will store the files generated here in the directory specified with <code class="docutils literal notranslate"><span class="pre">DIR_TEMP</span></code>.</p>
 </section>
 </section>
 
diff --git a/docs/templates.rst b/docs/templates.rst
index 2f88b8f..a29ac14 100644
--- a/docs/templates.rst
+++ b/docs/templates.rst
@@ -13,7 +13,7 @@ in the configuration file. The parameters are limited from the original fsps (`C
 such as the range in metallicity, initial mass function (IMF), and ionizing parameter. 
 
 It is in this step that gsf generates templates of different ages and metallicities. 
-The templates are saved in a file, "spec\_all.asdf" (irrespective to object IDs), 
+The templates are saved in a file, ``spec_all.asdf`` (irrespective to object IDs), 
 which can be shared for other objects for the purpose of saving time. 
 Redshifted templates (below), which are actually 
 used in the fitting process, are generated by starting off from this common file. 
@@ -34,7 +34,7 @@ If set to a negative value, then SSP is applied.
 
 Shifting templates to the input redshift (Execution flag = 1)
 -------------------------------------------------------------
-In this step (specifically, `maketemp` module), gsf shifts the rest-frame templates generated above (i.e. those in "spec\_all.asdf") 
+In this step (specifically, `maketemp` module), gsf shifts the rest-frame templates generated above (i.e. those in ``spec_all.asdf``) 
 to the input redshift and calculates fluxes of the input filters for one-to-one comparison. 
 Basically, there are three steps;
 
@@ -42,14 +42,14 @@ Basically, there are three steps;
 
 .. math::
 
-    \mathrm{wave_{obs}} = \mathrm{wave_{rest-frame}} (1 + z)
+    \mathrm{wave_{obs}} = \mathrm{wave_{rf}} (1 + z)
 
 
 - Scaling flux:
 
 .. math::
 
-    \mathrm{flux_{obs}} \propto \mathrm{flux_{rest-frame}} {4 \pi d_{L}^2 \over{(1+z)}}
+    \mathrm{flux_{obs}} \propto \mathrm{flux_{rf}} {4 \pi d_{L}^2 \over{(1+z)}}
 
 where :math:`d_L` is luminosity distance to the input redshift.
 
@@ -61,6 +61,6 @@ where :math:`d_L` is luminosity distance to the input redshift.
     \mathrm{flux_{atten}} = \mathrm{flux_{int}} \exp{(-\tau)}
 
 
-where gsf applies the Madau model for IGM attenuation at :math:`z>6`.
+where gsf applies `Dijkstra 2014 <https://ui.adsabs.harvard.edu/abs/2014PASA...31...40D/>`__ for IGM attenuation.
 
-gsf will store the files generated here in a directory specified with ``DIR_TEMP``.
+gsf will store the files generated here in the directory specified with ``DIR_TEMP``.

From a1bc2bf3ee650d9cf0ab824324dafa109db373ac Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Wed, 14 Dec 2022 20:16:38 -0800
Subject: [PATCH 13/14] minor

---
 gsf/gsf.py      | 74 +++++++++++++++++++++----------------------------
 gsf/plot_sfh.py |  2 +-
 2 files changed, 33 insertions(+), 43 deletions(-)

diff --git a/gsf/gsf.py b/gsf/gsf.py
index 3bd50cb..6a68a92 100644
--- a/gsf/gsf.py
+++ b/gsf/gsf.py
@@ -8,7 +8,7 @@
 
 # From gsf
 from .fitting import Mainbody
-from .maketmp_filt import maketemp #,maketemp_tau
+from .maketmp_filt import maketemp,maketemp_tau
 from .function_class import Func,Func_tau
 from .basic_func import Basic,Basic_tau
 
@@ -44,24 +44,25 @@ def run_gsf_template(inputs, fplt=0, tau_lim=0.001, idman=None, nthin=1, delwave
     #
     # 0. Make basic templates
     #
-    if fplt == 0:
+    if fplt == 0 or fplt == 1:
         #
         # 0. Make basic templates
         #
-        lammax = 40000 * (1.+MB.zgal) # AA
-        if MB.f_dust:
-            lammax = 2000000 * (1.+MB.zgal) # AA
+        if fplt==0:
+            lammax = 40000 * (1.+MB.zgal) # AA
+            if MB.f_dust:
+                lammax = 2000000 * (1.+MB.zgal) # AA
 
-        if MB.SFH_FORM == -99:
-            if MB.f_bpass == 1:
-                from .maketmp_z0 import make_tmp_z0_bpass
-                make_tmp_z0_bpass(MB, lammax=lammax)
+            if MB.SFH_FORM == -99:
+                if MB.f_bpass == 1:
+                    from .maketmp_z0 import make_tmp_z0_bpass
+                    make_tmp_z0_bpass(MB, lammax=lammax)
+                else:
+                    from .maketmp_z0 import make_tmp_z0
+                    make_tmp_z0(MB, lammax=lammax)
             else:
-                from .maketmp_z0 import make_tmp_z0
-                make_tmp_z0(MB, lammax=lammax)
-        else:
-            from .maketmp_z0_tau import make_tmp_z0
-            make_tmp_z0(MB, lammax=lammax)            
+                from .maketmp_z0_tau import make_tmp_z0
+                make_tmp_z0(MB, lammax=lammax)            
 
     #
     # 1. Start making redshifted templates.
@@ -73,8 +74,7 @@ def run_gsf_template(inputs, fplt=0, tau_lim=0.001, idman=None, nthin=1, delwave
         if MB.SFH_FORM == -99:
             maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
         else:
-            maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
-            # maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+            maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
 
     # Read temp from asdf;
     # This has to happend after fplt==1 and before fplt>=2.
@@ -152,24 +152,25 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
     # Make templates based on input redsfift.
     #
     flag_suc = True
-    if fplt == 0:
+    if fplt == 0 or fplt == 1:
         #
         # 0. Make basic templates
         #
-        lammax = 40000 * (1.+MB.zgal) # AA
-        if MB.f_dust:
-            lammax = 2000000 * (1.+MB.zgal) # AA
+        if fplt==0:
+            lammax = 40000 * (1.+MB.zgal) # AA
+            if MB.f_dust:
+                lammax = 2000000 * (1.+MB.zgal) # AA
 
-        if MB.SFH_FORM == -99:
-            if MB.f_bpass == 1:
-                from .maketmp_z0 import make_tmp_z0_bpass
-                make_tmp_z0_bpass(MB, lammax=lammax)
+            if MB.SFH_FORM == -99:
+                if MB.f_bpass == 1:
+                    from .maketmp_z0 import make_tmp_z0_bpass
+                    make_tmp_z0_bpass(MB, lammax=lammax)
+                else:
+                    from .maketmp_z0 import make_tmp_z0
+                    make_tmp_z0(MB, lammax=lammax)
             else:
-                from .maketmp_z0 import make_tmp_z0
-                make_tmp_z0(MB, lammax=lammax)
-        else:
-            from .maketmp_z0_tau import make_tmp_z0
-            make_tmp_z0(MB, lammax=lammax)            
+                from .maketmp_z0_tau import make_tmp_z0
+                make_tmp_z0(MB, lammax=lammax)            
 
     if not flag_suc:
         sys.exit()
@@ -191,8 +192,7 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
         if MB.SFH_FORM == -99:
             flag_suc = maketemp(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
         else:
-            # flag_suc = maketemp_tau(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
-            flag_suc = maketemp(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
+            flag_suc = maketemp_tau(MB, tau_lim=MB.tau_lim, nthin=MB.nthin, delwave=MB.delwave)
 
         #
         # 2. Main fitting part.
@@ -240,7 +240,7 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
             if MB.SFH_FORM == -99:
                 flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
             else:
-                flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
+                flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
 
         if MB.SFH_FORM == -99:
             from .plot_sfh import plot_sfh
@@ -271,16 +271,6 @@ def run_gsf_all(parfile, fplt, cornerplot=True, f_Alog=True, idman=None, zman=No
     '''
 
     if fplt == 6:
-        # Use the final redshift;
-        from astropy.io import fits
-        hd_sum = fits.open(os.path.join(MB.DIR_OUT, 'summary_%s.fits'%MB.ID))[0].header
-        MB.zgal = hd_sum['ZMC']
-        
-        if MB.SFH_FORM == -99:
-            flag_suc = maketemp(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
-        else:
-            flag_suc = maketemp_tau(MB, tau_lim=tau_lim, nthin=nthin, delwave=delwave)
-
         if MB.SFH_FORM == -99:
             from .plot_sed import plot_corner_physparam_frame,plot_corner_physparam_summary
             plot_corner_physparam_summary(MB)
diff --git a/gsf/plot_sfh.py b/gsf/plot_sfh.py
index 026c23e..2532d6e 100644
--- a/gsf/plot_sfh.py
+++ b/gsf/plot_sfh.py
@@ -842,7 +842,7 @@ def sfr_tau(t0, tau0, Z=0.0, sfh=0, tt=np.arange(0,13,0.1), Mtot=1.,
     return tt, yy, yyms
 
 
-def plot_sfh_tau(MB, flim=0.01, lsfrl=-1, mmax=1000, Txmin=0.08, Txmax=4, lmmin=8.5, fil_path='./FILT/',
+def plot_sfh_tau(MB, f_comp=0, flim=0.01, lsfrl=-1, mmax=1000, Txmin=0.08, Txmax=4, lmmin=8.5, fil_path='./FILT/',
     dust_model=0, f_SFMS=False, f_symbol=True, verbose=False, f_silence=True, DIR_TMP=None,
     f_log_sfh=True, dpi=250, TMIN=0.0001, tau_lim=0.01, skip_zhist=True, tset_SFR_SED=0.1):
     '''

From f11ad663732703fd658cbf332361582bdf76efa7 Mon Sep 17 00:00:00 2001
From: Takahiro Morishita <takahiro@ipac.caltech.edu>
Date: Wed, 14 Dec 2022 20:17:46 -0800
Subject: [PATCH 14/14] minor

---
 gsf/function.py     | 13 +++++--------
 gsf/maketmp_filt.py | 40 ++++++++++++++++++++++++++++++++++------
 2 files changed, 39 insertions(+), 14 deletions(-)

diff --git a/gsf/function.py b/gsf/function.py
index d31c168..0bc30a6 100644
--- a/gsf/function.py
+++ b/gsf/function.py
@@ -489,16 +489,13 @@ def get_Fint(lmtmp, ftmp, lmin=1400, lmax=1500):
 
 
 def get_Fuv(lmtmp, ftmp, lmin=1400, lmax=1500):
-    '''
-    Purpose
-    -------
-    Get RF UV (or any wavelength) flux density.
+    '''Get RF UV (or any wavelength) flux density.
 
     Parameters
     ----------
-    lmtmp : 
+    lmtmp : float array
         Rest-frame wavelength, in AA.
-    ftmp : 
+    ftmp : float array
         Fnu
     
     Returns
@@ -548,12 +545,12 @@ def fnutonu(fnu, m0set=25.0, m0input=-48.6):
     return fnu_new
 
 
-def flamtonu(lam, flam, m0set=25.0):
+def flamtonu(lam, flam, m0set=25.0, m0=-48.6):
     '''
     Converts from Flam to Fnu, with mag zeropoint of m0set.
     
     '''
-    Ctmp = lam**2/c * 10**((48.6+m0set)/2.5) #/ delx_org
+    Ctmp = lam**2/c * 10**((m0set-m0)/2.5) #/ delx_org
     fnu  = flam * Ctmp
     return fnu
 
diff --git a/gsf/maketmp_filt.py b/gsf/maketmp_filt.py
index ec53240..5ea3261 100644
--- a/gsf/maketmp_filt.py
+++ b/gsf/maketmp_filt.py
@@ -594,13 +594,45 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
                     ms[:] = mshdu['ms_'+str(zz)][:] # [:] is necessary.
                     Ls[:] = mshdu['Ls_'+str(zz)][:]
 
+                    # Distance;
+                    DL = MB.cosmo.luminosity_distance(zbest).value * MB.Mpc_cm # Luminositydistance in cm
+                    DL10 = MB.Mpc_cm / 1e6 * 10 # 10pc in cm
+
                     for ss in range(Na):
                         wave = lm0
+                        wavetmp = wave*(1.+zbest)
                         if delwave>0:
                             fint = interpolate.interp1d(lm0_orig, spechdu['fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin], kind='nearest', fill_value="extrapolate")
-                            spec_mul[ss] = fint(lm0)
+                            spec_mul[ss,:] = fint(lm0)
                         else:
-                            spec_mul[ss] = spechdu['fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin] # Lsun/A
+                            spec_mul[ss,:] = spechdu['fspec_'+str(zz)+'_'+str(ss)+'_'+str(pp)][::nthin] # Lsun/A
+
+                        """
+                        ###################
+                        # Check xi_ion
+                        ###################
+                        h = 6.626e-34 # J s
+                        # 1 J = 1e7 erg
+                        h *= 1e7 # erg s
+                        nu = c / wave # A/s / A = 1/s
+                        # con_lyc = (wave[:-1]<912.0) & (wave[:-1]>228.0)
+                        # nph = spec_mul[ss,:] / (h * nu) # Lsun/A / (erg s * 1/s)
+                        # nph *= MB.Lsun # 1/A
+                        # delwave_array = np.diff(wave)
+                        # nph_Lyc = np.nansum(nph[:-1][con_lyc]*delwave_array[con_lyc])
+                        con_lyc = (wave[:]<912.0) & (wave[:]>228.0)
+                        nph = spec_mul[ss,:] / (h * nu) # Lsun/s/A / (erg s * 1/s) = Lsun/s / A / erg
+                        nph *= MB.Lsun # 1/s/A
+                        nph_Lyc = np.nansum(nph[:][con_lyc]) # 1/s
+
+                        # UV Flux density;
+                        fnu = flamtonu(wave, spec_mul[ss,:], m0set=MB.m0set) * MB.Lsun # erg/A
+                         #/ (4. * np.pi * DL10**2) # fnu, in erg/s/cm2/Hz.
+                        Fuv = get_Fuv(wave, fnu, lmin=1250, lmax=1650) # erg/A
+                        # MUV = -2.5 * np.log10(Fuv) + MB.m0set 
+                        Luv = Fuv * (1650-1250) # erg
+                        xi_ion = nph_Lyc / Luv # 1/s / (erg)
+                        """
 
                         ###################
                         # IGM attenuation.
@@ -611,10 +643,6 @@ def maketemp(MB, ebblim=1e10, lamliml=0., lamlimu=50000., ncolbb=10000,
                             MB.x_HI = x_HI
                             spec_mul[ss,:] = spec_av_tmp
 
-                        # Distance;
-                        DL = MB.cosmo.luminosity_distance(zbest).value * MB.Mpc_cm # Luminositydistance in cm
-                        wavetmp = wave*(1.+zbest)
-
                         # Flam to Fnu
                         spec_mul_nu[ss,:] = flamtonu(wave, spec_mul[ss,:], m0set=MB.m0set)
                         spec_mul_nu[ss,:] *= MB.Lsun / (4.*np.pi*DL**2/(1.+zbest))