From c4132a59bde6a11d211c7f7b80e7194d669175f0 Mon Sep 17 00:00:00 2001
From: Craig D Warner <cwarner@astro.ufl.edu>
Date: Tue, 27 Feb 2024 13:48:56 -0800
Subject: [PATCH 1/7] Modified archetypes using BVLS to add -1*all legendre
 terms to the basis vector (in tdata) so that NNLS can be used instead of
 BVLS.

Fixed bug when ValueError excepted in rebinning
---
 py/redrock/archetypes.py | 25 ++++++++++++++++++++-----
 py/redrock/fitz.py       |  9 +++++++--
 py/redrock/zfind.py      |  7 +++++--
 py/redrock/zscan.py      | 11 +++++++----
 4 files changed, 39 insertions(+), 13 deletions(-)

diff --git a/py/redrock/archetypes.py b/py/redrock/archetypes.py
index e6de246d..5774b0c8 100644
--- a/py/redrock/archetypes.py
+++ b/py/redrock/archetypes.py
@@ -63,6 +63,8 @@ def __init__(self, filename):
 
         # TODO: Allow Archetype files to specify their IGM model
         self.igm_model = 'Inoue14'
+        # TODO: Allow Archetype files to specify bvls or nnls or pca solver method
+        self._solver_method = 'bvls'
 
         return
 
@@ -202,6 +204,9 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
         nleg = legendre[list(legendre.keys())[0]].shape[0]
         iBest = np.argsort(zzchi2)[0:n_nearest]
         tdata = dict()
+        tot_nleg = nleg
+        if (self._solver_method == 'bvls'):
+            tot_nleg = nleg*2
         if (binned is not None):
             if (use_gpu):
                 binned = { hs:binned[hs][:,iBest].get() for hs in binned }
@@ -220,11 +225,14 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
             tdata[hs] = binned[hs][None,:,:]
             if (nleg > 0):
                 tdata[hs] = np.append(tdata[hs], legendre[hs].transpose()[None,:,:], axis=2)
+                if per_camera and self._solver_method == 'bvls':
+                    #Using BVLS - append negative copies of legendre terms as well to use NNLS with additional bases to approximate BVLS
+                    tdata[hs] = np.append(tdata[hs], -1*legendre[hs].transpose()[None,:,:], axis=2)
             nbasis = tdata[hs].shape[2]
             
         if per_camera:
             #Use CPU mode since small tdata
-            (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, nleg, 1, method='bvls', n_nbh=n_nearest, prior=prior, use_gpu=False, ncam=ncam)
+            (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, tot_nleg, 1, method=self._solver_method, n_nbh=n_nearest, prior=prior, use_gpu=False, ncam=ncam)
         else:
             #Use CPU mode for calc_zchi2 since small tdata
             (zzchi2, zzcoeff) = calc_zchi2_batch(spectra, tdata, weights, flux, wflux, 1, nbasis, use_gpu=False)
@@ -262,6 +270,10 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
         spectra = target.spectra
         nleg = target.nleg
         legendre = target.legendre(nleg=nleg, use_gpu=use_gpu) #Get previously calculated legendre
+        solve_method = self._solver_method
+        tot_nleg = nleg
+        if (solve_method == 'bvls'):
+            tot_nleg = nleg*2
 
         #Select np or cp for operations as arrtype
         if (use_gpu):
@@ -288,8 +300,8 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
         obs_wave = np.concatenate([dwave[key] for key in new_keys])
         
         nleg = legendre[list(legendre.keys())[0]].shape[0]
-        zzchi2 = np.zeros(self._narch, dtype=np.float64)
-        zzcoeff = np.zeros((self._narch,  1+ncam*(nleg)), dtype=np.float64)
+        #zzchi2 = np.zeros(self._narch, dtype=np.float64)
+        #zzcoeff = np.zeros((self._narch,  1+ncam*(nleg)), dtype=np.float64)
         
         #TODO: return best fit model as well
         #zzmodel = np.zeros((self._narch, obs_wave.size), dtype=np.float64)
@@ -324,14 +336,17 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
             #Create 3-d tdata with narch x nwave x nbasis where nbasis = 1+nleg
             if nleg > 0:
                 tdata[hs] = arrtype.append(binned[hs].transpose()[:,:,None], arrtype.tile(arrtype.asarray(legendre[hs]).transpose()[None,:,:], (self._narch, 1, 1)), axis=2)
+                if solve_method == 'bvls':
+                    #Using BVLS - append negative copies of legendre terms as well to use NNLS with additional bases to approximate BVLS
+                    tdata[hs] = arrtype.append(tdata[hs], arrtype.tile(-1*arrtype.asarray(legendre[hs]).transpose()[None,:,:], (self._narch, 1, 1)), axis=2)
             else:
                 tdata[hs] = binned[hs].transpose()[:,:,None]
             nbasis = tdata[hs].shape[2]
         if per_camera:
             if (use_gpu):
-                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, gpuweights, gpuflux, gpuwflux, nleg, self._narch, method=solve_method, n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
+                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, gpuweights, gpuflux, gpuwflux, tot_nleg, self._narch, method=solve_method, n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
             else:
-                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, nleg, self._narch, method=solve_method, n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
+                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, tot_nleg, self._narch, method=solve_method, n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
         else:
             if (use_gpu):
                 (zzchi2, zzcoeff) = calc_zchi2_batch(spectra, tdata, gpuweights, gpuflux, gpuwflux, self._narch, nbasis, use_gpu=use_gpu)
diff --git a/py/redrock/fitz.py b/py/redrock/fitz.py
index a0a9c7c7..970959cb 100644
--- a/py/redrock/fitz.py
+++ b/py/redrock/fitz.py
@@ -292,6 +292,8 @@ def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=
                 coeff = np.zeros(template.nbasis)
                 zwarn |= ZW.Z_FITLIMIT
                 zwarn |= ZW.BAD_MINFIT
+                #Set trans to None so as not to pass an empty dict to archetypes! CW 2/26/24
+                trans = None
             else:
                 #- Unknown problem; re-raise error
                 raise err
@@ -329,10 +331,13 @@ def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=
                     ncamera = len(list(dwave.keys())) # number of cameras, for e.g. DESI has three cameras
                 else:
                     ncamera = 1
+                tot_deg_legendre = deg_legendre
+                if archetype._solver_method == 'bvls':
+                    tot_deg_legendre = deg_legendre*2
                 if n_nearest is None:
-                    prior = prior_on_coeffs(1, deg_legendre, prior_sigma, ncamera)
+                    prior = prior_on_coeffs(1, tot_deg_legendre, prior_sigma, ncamera)
                 else:
-                    prior = prior_on_coeffs(n_nearest, deg_legendre, prior_sigma, ncamera)
+                    prior = prior_on_coeffs(n_nearest, tot_deg_legendre, prior_sigma, ncamera)
             else:
                 prior=None
             chi2min, coeff, fulltype = archetype.get_best_archetype(target,weights,flux,wflux,dwave,zbest, per_camera, n_nearest, trans=trans, use_gpu=use_gpu, prior=prior)
diff --git a/py/redrock/zfind.py b/py/redrock/zfind.py
index bb330828..04e1993f 100644
--- a/py/redrock/zfind.py
+++ b/py/redrock/zfind.py
@@ -598,10 +598,13 @@ def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=Non
         if archetypes is None or not per_camera:
             maxcoeff = np.max([t.template.nbasis for t in templates])
         else:
+            tot_deg_legendre = deg_legendre
+            if archetype._solver_method == 'bvls':
+                tot_deg_legendre = deg_legendre*2
             if n_nearest is not None:
-                maxcoeff = max(np.max([t.template.nbasis for t in templates]), ncamera*(deg_legendre)+n_nearest)
+                maxcoeff = max(np.max([t.template.nbasis for t in templates]), ncamera*(tot_deg_legendre)+n_nearest)
             else:
-                maxcoeff = max(np.max([t.template.nbasis for t in templates]), ncamera*(deg_legendre)+1)
+                maxcoeff = max(np.max([t.template.nbasis for t in templates]), ncamera*(tot_deg_legendre)+1)
 
         if allzfit['coeff'].ndim == 1:
             ntarg = allzfit['coeff'].shape
diff --git a/py/redrock/zscan.py b/py/redrock/zscan.py
index 119873b6..e4a66d7a 100644
--- a/py/redrock/zscan.py
+++ b/py/redrock/zscan.py
@@ -270,6 +270,7 @@ def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux
     spectra = target.spectra
 
     nbasis = n_nbh+nleg*ncam # n_nbh : for actual physical archetype(s), nleg: number of legendre polynomials, ncamera: number of cameras
+    #nleg should be doubled already here if using BVLS so nbasis should be correct
     ret_zcoeff= {'alpha':[], 'b':[], 'r':[], 'z':[]}
 
     #Setup dict of solver args to pass bounds to solver
@@ -277,10 +278,12 @@ def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux
     solver_args = dict()
     if (method == 'BVLS'):
         #only positive coefficients are allowed for the archetypes
-        bounds = np.zeros((2, nbasis))
-        bounds[0][n_nbh:]=-np.inf #constant and slope terms in archetype method (can be positive or negative)
-        bounds[1] = np.inf
-        solver_args['bounds'] = bounds
+        #bounds = np.zeros((2, nbasis))
+        #bounds[0][n_nbh:]=-np.inf #constant and slope terms in archetype method (can be positive or negative)
+        #bounds[1] = np.inf
+        #solver_args['bounds'] = bounds
+        #Update solver method
+        method = 'NNLS'
 
     #Use branching options because GPU is faster in batch in 3d
     #but due to timing weirdness in numpy, CPU is faster looping over

From a660106981b32727976610291d99d81aaa28933f Mon Sep 17 00:00:00 2001
From: Craig D Warner <cwarner@astro.ufl.edu>
Date: Mon, 11 Mar 2024 18:55:35 -0700
Subject: [PATCH 2/7] Modify prior array in prior_on_coeffs to include
 cross-terms for negative legendre terms.  Refactored a little to clean up.

For archetypes with legendre terms, NNLS is now actually used with
additional terms of -1*each legendre term so that we can use the faster
NNLS to get the same result as BVLS.
---
 py/redrock/archetypes.py |  9 ++++-----
 py/redrock/fitz.py       | 29 +++++++++++++++++++++--------
 py/redrock/zscan.py      | 10 ++++------
 3 files changed, 29 insertions(+), 19 deletions(-)

diff --git a/py/redrock/archetypes.py b/py/redrock/archetypes.py
index 5774b0c8..e8ccb673 100644
--- a/py/redrock/archetypes.py
+++ b/py/redrock/archetypes.py
@@ -299,9 +299,7 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
 
         obs_wave = np.concatenate([dwave[key] for key in new_keys])
         
-        nleg = legendre[list(legendre.keys())[0]].shape[0]
-        #zzchi2 = np.zeros(self._narch, dtype=np.float64)
-        #zzcoeff = np.zeros((self._narch,  1+ncam*(nleg)), dtype=np.float64)
+        #nleg = legendre[list(legendre.keys())[0]].shape[0]
         
         #TODO: return best fit model as well
         #zzmodel = np.zeros((self._narch, obs_wave.size), dtype=np.float64)
@@ -343,10 +341,11 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
                 tdata[hs] = binned[hs].transpose()[:,:,None]
             nbasis = tdata[hs].shape[2]
         if per_camera:
+            #Hard code NNLS as solve method for per_camera_coeff_with_least_square_batch
             if (use_gpu):
-                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, gpuweights, gpuflux, gpuwflux, tot_nleg, self._narch, method=solve_method, n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
+                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, gpuweights, gpuflux, gpuwflux, tot_nleg, self._narch, method='NNLS', n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
             else:
-                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, tot_nleg, self._narch, method=solve_method, n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
+                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, tot_nleg, self._narch, method='NNLS', n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
         else:
             if (use_gpu):
                 (zzchi2, zzcoeff) = calc_zchi2_batch(spectra, tdata, gpuweights, gpuflux, gpuwflux, self._narch, nbasis, use_gpu=use_gpu)
diff --git a/py/redrock/fitz.py b/py/redrock/fitz.py
index 970959cb..2af0093f 100644
--- a/py/redrock/fitz.py
+++ b/py/redrock/fitz.py
@@ -116,7 +116,7 @@ def legendre_calculate(nleg, dwave):
 
     return legendre
 
-def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera):
+def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera, method=None):
     
     """
     Args:
@@ -124,15 +124,30 @@ def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera):
         deg_legendre (int): number of Legendre polynomials 
         sigma (int): prior sigma to be used for archetype fitting
         ncamera (int): number of cameras for given instrument
+        method (str): archetype solver method - if bvls then prior will be
+            adjusted to account for negative legendre terms
     Returns:
         2d array to be added while solving for archetype fitting
 
     """
     
-    nbasis = n_nbh+deg_legendre*ncamera # 3 desi cameras
+    tot_legendre_terms = deg_legendre
+    if method == 'bvls':
+        #Double total legendre term count to reflect negative terms
+        tot_legendre_terms = deg_legendre*2
+    nbasis = n_nbh+tot_legendre_terms*ncamera # 3 desi cameras
     prior = np.zeros((nbasis, nbasis), dtype='float64');np.fill_diagonal(prior, 1/(sigma**2))
     for i in range(n_nbh):
         prior[i][i]=0. ## Do not add prior to the archetypes, added only to the Legendre polynomials
+
+    if method == 'bvls':
+        #Add cross terms to prior array
+        for i in range(ncamera):
+            for j in range(deg_legendre):
+                idx1 = n_nbh+i*tot_legendre_terms+j
+                idx2 = n_nbh+i*tot_legendre_terms+deg_legendre+j
+                prior[idx1][idx2] = -prior[idx1][idx1]
+                prior[idx2][idx1] = -prior[idx2][idx2]
     return prior
 
 
@@ -331,13 +346,11 @@ def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=
                     ncamera = len(list(dwave.keys())) # number of cameras, for e.g. DESI has three cameras
                 else:
                     ncamera = 1
-                tot_deg_legendre = deg_legendre
-                if archetype._solver_method == 'bvls':
-                    tot_deg_legendre = deg_legendre*2
                 if n_nearest is None:
-                    prior = prior_on_coeffs(1, tot_deg_legendre, prior_sigma, ncamera)
-                else:
-                    prior = prior_on_coeffs(n_nearest, tot_deg_legendre, prior_sigma, ncamera)
+                    n_nearest = 1
+                #Abstract the construction of prior including cross-terms for negative legendre terms
+                #to prior_on_coeffs method.
+                prior = prior_on_coeffs(n_nearest, deg_legendre, prior_sigma, ncamera, archetype._solver_method)
             else:
                 prior=None
             chi2min, coeff, fulltype = archetype.get_best_archetype(target,weights,flux,wflux,dwave,zbest, per_camera, n_nearest, trans=trans, use_gpu=use_gpu, prior=prior)
diff --git a/py/redrock/zscan.py b/py/redrock/zscan.py
index e4a66d7a..65949bc3 100644
--- a/py/redrock/zscan.py
+++ b/py/redrock/zscan.py
@@ -278,12 +278,10 @@ def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux
     solver_args = dict()
     if (method == 'BVLS'):
         #only positive coefficients are allowed for the archetypes
-        #bounds = np.zeros((2, nbasis))
-        #bounds[0][n_nbh:]=-np.inf #constant and slope terms in archetype method (can be positive or negative)
-        #bounds[1] = np.inf
-        #solver_args['bounds'] = bounds
-        #Update solver method
-        method = 'NNLS'
+        bounds = np.zeros((2, nbasis))
+        bounds[0][n_nbh:]=-np.inf #constant and slope terms in archetype method (can be positive or negative)
+        bounds[1] = np.inf
+        solver_args['bounds'] = bounds
 
     #Use branching options because GPU is faster in batch in 3d
     #but due to timing weirdness in numpy, CPU is faster looping over

From 7911c5bc6fc4db3982c9956e00074930596d2228 Mon Sep 17 00:00:00 2001
From: Craig D Warner <cwarner@astro.ufl.edu>
Date: Mon, 11 Mar 2024 19:26:32 -0700
Subject: [PATCH 3/7] Only use additional negative legendre terms and NNLS when
 in GPU mode because on CPU, the additional computation time for the larger
 array sizes dominates any time savings of NNLS vs BVLS

---
 py/redrock/archetypes.py | 21 +++++++++++----------
 py/redrock/fitz.py       | 15 +++++++++------
 2 files changed, 20 insertions(+), 16 deletions(-)

diff --git a/py/redrock/archetypes.py b/py/redrock/archetypes.py
index e8ccb673..ae135a14 100644
--- a/py/redrock/archetypes.py
+++ b/py/redrock/archetypes.py
@@ -205,7 +205,8 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
         iBest = np.argsort(zzchi2)[0:n_nearest]
         tdata = dict()
         tot_nleg = nleg
-        if (self._solver_method == 'bvls'):
+        if (self._solver_method == 'bvls' and use_gpu):
+            #Only use NNLS with additional legendre terms if BVLS AND gpu mode
             tot_nleg = nleg*2
         if (binned is not None):
             if (use_gpu):
@@ -225,8 +226,8 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
             tdata[hs] = binned[hs][None,:,:]
             if (nleg > 0):
                 tdata[hs] = np.append(tdata[hs], legendre[hs].transpose()[None,:,:], axis=2)
-                if per_camera and self._solver_method == 'bvls':
-                    #Using BVLS - append negative copies of legendre terms as well to use NNLS with additional bases to approximate BVLS
+                if per_camera and self._solver_method == 'bvls' and use_gpu:
+                    #Using BVLS and GPU - append negative copies of legendre terms as well to use NNLS with additional bases to approximate BVLS
                     tdata[hs] = np.append(tdata[hs], -1*legendre[hs].transpose()[None,:,:], axis=2)
             nbasis = tdata[hs].shape[2]
             
@@ -243,7 +244,7 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
         #print(z, zzchi2, zzcoeff, fsstype)
         return zzchi2[0], zzcoeff[0], self._rrtype+':::%s'%(fsstype)
 
-    def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nearest, trans=None, solve_method='bvls', prior=None, use_gpu=False):
+    def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nearest, trans=None, prior=None, use_gpu=False):
 
         """Get the best archetype for the given redshift and spectype.
 
@@ -257,7 +258,6 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
             per_camera (bool): True if fitting needs to be done in each camera
             n_nearest (int): number of nearest neighbours to be used in chi2 space (including best archetype)
             trans (dict): pass previously calcualated Lyman transmission instead of recalculating
-            solve_method (string): bvls or pca
             use_gpu (bool): use GPU or not
             prior (2d array): prior matrix on coefficients (1/sig**2)
             
@@ -270,9 +270,10 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
         spectra = target.spectra
         nleg = target.nleg
         legendre = target.legendre(nleg=nleg, use_gpu=use_gpu) #Get previously calculated legendre
-        solve_method = self._solver_method
+        solve_method = self._solver_method #get solve method from archetype class instead of passing as arg
         tot_nleg = nleg
-        if (solve_method == 'bvls'):
+        if (solve_method == 'bvls' and use_gpu):
+            #Only use NNLS with additional legendre terms if BVLS AND gpu mode
             tot_nleg = nleg*2
 
         #Select np or cp for operations as arrtype
@@ -334,18 +335,18 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
             #Create 3-d tdata with narch x nwave x nbasis where nbasis = 1+nleg
             if nleg > 0:
                 tdata[hs] = arrtype.append(binned[hs].transpose()[:,:,None], arrtype.tile(arrtype.asarray(legendre[hs]).transpose()[None,:,:], (self._narch, 1, 1)), axis=2)
-                if solve_method == 'bvls':
+                if solve_method == 'bvls' and use_gpu:
                     #Using BVLS - append negative copies of legendre terms as well to use NNLS with additional bases to approximate BVLS
                     tdata[hs] = arrtype.append(tdata[hs], arrtype.tile(-1*arrtype.asarray(legendre[hs]).transpose()[None,:,:], (self._narch, 1, 1)), axis=2)
             else:
                 tdata[hs] = binned[hs].transpose()[:,:,None]
             nbasis = tdata[hs].shape[2]
         if per_camera:
-            #Hard code NNLS as solve method for per_camera_coeff_with_least_square_batch
+            #Hard code NNLS as solve method for GPU mode only, use solve_method (BVLS) for CPU
             if (use_gpu):
                 (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, gpuweights, gpuflux, gpuwflux, tot_nleg, self._narch, method='NNLS', n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
             else:
-                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, tot_nleg, self._narch, method='NNLS', n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
+                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, tot_nleg, self._narch, method=solve_method, n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
         else:
             if (use_gpu):
                 (zzchi2, zzcoeff) = calc_zchi2_batch(spectra, tdata, gpuweights, gpuflux, gpuwflux, self._narch, nbasis, use_gpu=use_gpu)
diff --git a/py/redrock/fitz.py b/py/redrock/fitz.py
index 2af0093f..2330535a 100644
--- a/py/redrock/fitz.py
+++ b/py/redrock/fitz.py
@@ -116,7 +116,7 @@ def legendre_calculate(nleg, dwave):
 
     return legendre
 
-def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera, method=None):
+def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera, add_negative_legendre_terms=False):
     
     """
     Args:
@@ -124,15 +124,15 @@ def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera, method=None):
         deg_legendre (int): number of Legendre polynomials 
         sigma (int): prior sigma to be used for archetype fitting
         ncamera (int): number of cameras for given instrument
-        method (str): archetype solver method - if bvls then prior will be
-            adjusted to account for negative legendre terms
+        add_negative_legendre_terms (bool): whether to adjust prior
+            to account for negative legendre terms
     Returns:
         2d array to be added while solving for archetype fitting
 
     """
     
     tot_legendre_terms = deg_legendre
-    if method == 'bvls':
+    if add_negative_legendre_terms:
         #Double total legendre term count to reflect negative terms
         tot_legendre_terms = deg_legendre*2
     nbasis = n_nbh+tot_legendre_terms*ncamera # 3 desi cameras
@@ -140,7 +140,7 @@ def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera, method=None):
     for i in range(n_nbh):
         prior[i][i]=0. ## Do not add prior to the archetypes, added only to the Legendre polynomials
 
-    if method == 'bvls':
+    if add_negative_legendre_terms:
         #Add cross terms to prior array
         for i in range(ncamera):
             for j in range(deg_legendre):
@@ -350,7 +350,10 @@ def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=
                     n_nearest = 1
                 #Abstract the construction of prior including cross-terms for negative legendre terms
                 #to prior_on_coeffs method.
-                prior = prior_on_coeffs(n_nearest, deg_legendre, prior_sigma, ncamera, archetype._solver_method)
+                add_negative_legendre_terms = False
+                if archetype._solver_method == 'bvls' and use_gpu:
+                    add_negative_legendre_terms = True
+                prior = prior_on_coeffs(n_nearest, deg_legendre, prior_sigma, ncamera, add_negative_legendre_terms)
             else:
                 prior=None
             chi2min, coeff, fulltype = archetype.get_best_archetype(target,weights,flux,wflux,dwave,zbest, per_camera, n_nearest, trans=trans, use_gpu=use_gpu, prior=prior)

From 1ac311ad684e88e184d655b1aea38bf43f540359 Mon Sep 17 00:00:00 2001
From: Craig D Warner <cwarner@astro.ufl.edu>
Date: Sat, 20 Apr 2024 23:25:50 -0700
Subject: [PATCH 4/7] Moved as much logic as possible related to using NNLS
 with extra negative legendre terms on the GPU in lieu of BVLS into
 per_camera_coeff_with_least_square_batch.  Moved prior_on_coeffs into
 zscan.py, called from within per_camera_coeff_with_least_square_batch. Pass
 prior_sigma, a scalar, insted of the array from fitz to archetypes.

Bug fix on case where only some object types, not all, have an
archetype.
---
 py/redrock/archetypes.py | 34 +++++++++++-------------
 py/redrock/fitz.py       | 46 ++-------------------------------
 py/redrock/zfind.py      |  7 ++---
 py/redrock/zscan.py      | 56 +++++++++++++++++++++++++++++++++++++---
 4 files changed, 74 insertions(+), 69 deletions(-)

diff --git a/py/redrock/archetypes.py b/py/redrock/archetypes.py
index ae135a14..7c111109 100644
--- a/py/redrock/archetypes.py
+++ b/py/redrock/archetypes.py
@@ -167,7 +167,7 @@ def eval(self, subtype, dwave, coeff, wave, z):
 
         return flux
     
-    def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest, zzchi2, trans, per_camera, dedges=None, binned=None, use_gpu=False, prior=None, ncam=None):
+    def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest, zzchi2, trans, per_camera, dedges=None, binned=None, use_gpu=False, prior_sigma=None, ncam=None):
         
         """Nearest neighbour archetype approach; fitting with a combinating of nearest archetypes in chi2 space
         
@@ -185,7 +185,7 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
             dedges (dict): in GPU mode, use pre-computed dict of wavelength bin edges, already on GPU
             binned (dict): already computed dictionary of rebinned fluxes
             use_gpu (bool): use GPU or not
-            prior (2d array): prior matrix on coefficients (1/sig**2)
+            prior_sigma (float): prior to add in the final solution matrix: added as 1/(prior_sigma**2) only for per-camera mode
             ncam (int): Number of camera for given Instrument
         
         Returns:
@@ -204,10 +204,6 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
         nleg = legendre[list(legendre.keys())[0]].shape[0]
         iBest = np.argsort(zzchi2)[0:n_nearest]
         tdata = dict()
-        tot_nleg = nleg
-        if (self._solver_method == 'bvls' and use_gpu):
-            #Only use NNLS with additional legendre terms if BVLS AND gpu mode
-            tot_nleg = nleg*2
         if (binned is not None):
             if (use_gpu):
                 binned = { hs:binned[hs][:,iBest].get() for hs in binned }
@@ -233,7 +229,9 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
             
         if per_camera:
             #Use CPU mode since small tdata
-            (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, tot_nleg, 1, method=self._solver_method, n_nbh=n_nearest, prior=prior, use_gpu=False, ncam=ncam)
+            #CW - 4/19/24 - pass use_gpu and solver method to per_camera_coeff_with_least_square_batch
+            #it will decide there if narch == 1 to use CPU and it will calculate correct prior array
+            (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, nleg, 1, method=self._solver_method, n_nbh=n_nearest, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
         else:
             #Use CPU mode for calc_zchi2 since small tdata
             (zzchi2, zzcoeff) = calc_zchi2_batch(spectra, tdata, weights, flux, wflux, 1, nbasis, use_gpu=False)
@@ -244,7 +242,7 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
         #print(z, zzchi2, zzcoeff, fsstype)
         return zzchi2[0], zzcoeff[0], self._rrtype+':::%s'%(fsstype)
 
-    def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nearest, trans=None, prior=None, use_gpu=False):
+    def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nearest, trans=None, prior_sigma=None, use_gpu=False):
 
         """Get the best archetype for the given redshift and spectype.
 
@@ -259,7 +257,7 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
             n_nearest (int): number of nearest neighbours to be used in chi2 space (including best archetype)
             trans (dict): pass previously calcualated Lyman transmission instead of recalculating
             use_gpu (bool): use GPU or not
-            prior (2d array): prior matrix on coefficients (1/sig**2)
+            prior_sigma (float): prior to add in the final solution matrix: added as 1/(prior_sigma**2) only for per-camera mode
             
         Returns:
             chi2 (float): chi2 of best archetype
@@ -271,10 +269,6 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
         nleg = target.nleg
         legendre = target.legendre(nleg=nleg, use_gpu=use_gpu) #Get previously calculated legendre
         solve_method = self._solver_method #get solve method from archetype class instead of passing as arg
-        tot_nleg = nleg
-        if (solve_method == 'bvls' and use_gpu):
-            #Only use NNLS with additional legendre terms if BVLS AND gpu mode
-            tot_nleg = nleg*2
 
         #Select np or cp for operations as arrtype
         if (use_gpu):
@@ -324,9 +318,11 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
         # because prior was defined based on n_nearest argument..
         # this logic is needed because the first fitting is done with just one archetype 
         #and then nearest neighbour approach is implemented
-        if n_nearest is not None:
-            nnearest_prior = prior.copy() # prior corresponding to nearest_nbh method
-            prior = prior[n_nearest-1:,][:,n_nearest-1:] # removing first rows/columns corresponding to the nearest_archetypes, and keeping just one row for one archetype
+
+        ##CW 04/19/24 - no need to redefine prior since it is now not calculated until per_camera_coeff_with_least_square_batch
+        #if n_nearest is not None:
+        #    nnearest_prior = prior.copy() # prior corresponding to nearest_nbh method
+        #    prior = prior[n_nearest-1:,][:,n_nearest-1:] # removing first rows/columns corresponding to the nearest_archetypes, and keeping just one row for one archetype
 
         for hs, wave in dwave.items():
             if (trans[hs] is not None):
@@ -344,9 +340,9 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
         if per_camera:
             #Hard code NNLS as solve method for GPU mode only, use solve_method (BVLS) for CPU
             if (use_gpu):
-                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, gpuweights, gpuflux, gpuwflux, tot_nleg, self._narch, method='NNLS', n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
+                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, gpuweights, gpuflux, gpuwflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
             else:
-                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, tot_nleg, self._narch, method=solve_method, n_nbh=1, prior=prior, use_gpu=use_gpu, ncam=ncam)
+                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
         else:
             if (use_gpu):
                 (zzchi2, zzcoeff) = calc_zchi2_batch(spectra, tdata, gpuweights, gpuflux, gpuwflux, self._narch, nbasis, use_gpu=use_gpu)
@@ -354,7 +350,7 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
                 (zzchi2, zzcoeff) = calc_zchi2_batch(spectra, tdata, weights, flux, wflux, self._narch, nbasis, use_gpu=use_gpu)
 
         if n_nearest is not None:
-            best_chi2, best_coeff, best_fulltype = self.nearest_neighbour_model(target,weights,flux,wflux,dwave,z, n_nearest, zzchi2, trans, per_camera, dedges=dedges, binned=binned, use_gpu=use_gpu, prior=nnearest_prior, ncam=ncam)
+            best_chi2, best_coeff, best_fulltype = self.nearest_neighbour_model(target,weights,flux,wflux,dwave,z, n_nearest, zzchi2, trans, per_camera, dedges=dedges, binned=binned, use_gpu=use_gpu, prior_sigma=prior_sigma, ncam=ncam)
             #print(best_chi2, best_coeff, best_fulltype)
             return best_chi2, best_coeff, best_fulltype
         else:
diff --git a/py/redrock/fitz.py b/py/redrock/fitz.py
index a6bc8922..da1a9fb7 100644
--- a/py/redrock/fitz.py
+++ b/py/redrock/fitz.py
@@ -116,41 +116,6 @@ def legendre_calculate(nleg, dwave):
 
     return legendre
 
-def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera, add_negative_legendre_terms=False):
-    
-    """
-    Args:
-        n_nbh (int): number of dominant archetypes
-        deg_legendre (int): number of Legendre polynomials 
-        sigma (int): prior sigma to be used for archetype fitting
-        ncamera (int): number of cameras for given instrument
-        add_negative_legendre_terms (bool): whether to adjust prior
-            to account for negative legendre terms
-    Returns:
-        2d array to be added while solving for archetype fitting
-
-    """
-    
-    tot_legendre_terms = deg_legendre
-    if add_negative_legendre_terms:
-        #Double total legendre term count to reflect negative terms
-        tot_legendre_terms = deg_legendre*2
-    nbasis = n_nbh+tot_legendre_terms*ncamera # 3 desi cameras
-    prior = np.zeros((nbasis, nbasis), dtype='float64');np.fill_diagonal(prior, 1/(sigma**2))
-    for i in range(n_nbh):
-        prior[i][i]=0. ## Do not add prior to the archetypes, added only to the Legendre polynomials
-
-    if add_negative_legendre_terms:
-        #Add cross terms to prior array
-        for i in range(ncamera):
-            for j in range(deg_legendre):
-                idx1 = n_nbh+i*tot_legendre_terms+j
-                idx2 = n_nbh+i*tot_legendre_terms+deg_legendre+j
-                prior[idx1][idx2] = -prior[idx1][idx1]
-                prior[idx2][idx1] = -prior[idx2][idx2]
-    return prior
-
-
 def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=False, deg_legendre=None, zminfit_npoints=15, per_camera=False, n_nearest=None, prior_sigma=None):
     """Refines redshift measurement around up to nminima minima.
 
@@ -349,15 +314,8 @@ def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=
                     ncamera = 1
                 if n_nearest is None:
                     n_nearest = 1
-                #Abstract the construction of prior including cross-terms for negative legendre terms
-                #to prior_on_coeffs method.
-                add_negative_legendre_terms = False
-                if archetype._solver_method == 'bvls' and use_gpu:
-                    add_negative_legendre_terms = True
-                prior = prior_on_coeffs(n_nearest, deg_legendre, prior_sigma, ncamera, add_negative_legendre_terms)
-            else:
-                prior=None
-            chi2min, coeff, fulltype = archetype.get_best_archetype(target,weights,flux,wflux,dwave,zbest, per_camera, n_nearest, trans=trans, use_gpu=use_gpu, prior=prior)
+            #Pass prior_sigma as a scalar and move calculation of prior array to zscan.py in per_camera_coeff_with_least_square_batch
+            chi2min, coeff, fulltype = archetype.get_best_archetype(target,weights,flux,wflux,dwave,zbest, per_camera, n_nearest, trans=trans, use_gpu=use_gpu, prior_sigma=prior_sigma)
             del trans
 
             results.append(dict(z=zbest, zerr=zerr, zwarn=zwarn,
diff --git a/py/redrock/zfind.py b/py/redrock/zfind.py
index 04e1993f..41a06447 100644
--- a/py/redrock/zfind.py
+++ b/py/redrock/zfind.py
@@ -251,6 +251,10 @@ def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=Non
     if archetypes:
         archetypes = All_archetypes(archetypes_dir=archetypes).archetypes
         archetype_spectype = list(archetypes.keys()) # to account for the case if only one archetype is provided
+        tot_deg_legendre = deg_legendre
+        for key in archetypes:
+            if archetypes[key]._solver_method == 'bvls':
+                tot_deg_legendre = deg_legendre*2
     
     if not priors is None:
         priors = Priors(priors)
@@ -598,9 +602,6 @@ def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=Non
         if archetypes is None or not per_camera:
             maxcoeff = np.max([t.template.nbasis for t in templates])
         else:
-            tot_deg_legendre = deg_legendre
-            if archetype._solver_method == 'bvls':
-                tot_deg_legendre = deg_legendre*2
             if n_nearest is not None:
                 maxcoeff = max(np.max([t.template.nbasis for t in templates]), ncamera*(tot_deg_legendre)+n_nearest)
             else:
diff --git a/py/redrock/zscan.py b/py/redrock/zscan.py
index 07db5d6f..7405d68b 100644
--- a/py/redrock/zscan.py
+++ b/py/redrock/zscan.py
@@ -238,7 +238,41 @@ def calc_zchi2_one(spectra, weights, flux, wflux, tdata, solve_matrices_algorith
 
     return zchi2, zcoeff
 
-def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, nleg, narch, method=None, n_nbh=None, prior=None, use_gpu=False, ncam=None):
+def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera, add_negative_legendre_terms=False):
+
+    """
+    Args:
+        n_nbh (int): number of dominant archetypes
+        deg_legendre (int): number of Legendre polynomials
+        sigma (int): prior sigma to be used for archetype fitting
+        ncamera (int): number of cameras for given instrument
+        add_negative_legendre_terms (bool): whether to adjust prior
+            to account for negative legendre terms
+    Returns:
+        2d array to be added while solving for archetype fitting
+
+    """
+
+    tot_legendre_terms = deg_legendre
+    if add_negative_legendre_terms:
+        #Double total legendre term count to reflect negative terms
+        tot_legendre_terms = deg_legendre*2
+    nbasis = n_nbh+tot_legendre_terms*ncamera # 3 desi cameras
+    prior = np.zeros((nbasis, nbasis), dtype='float64');np.fill_diagonal(prior, 1/(sigma**2))
+    for i in range(n_nbh):
+        prior[i][i]=0. ## Do not add prior to the archetypes, added only to the Legendre polynomials
+
+    if add_negative_legendre_terms:
+        #Add cross terms to prior array
+        for i in range(ncamera):
+            for j in range(deg_legendre):
+                idx1 = n_nbh+i*tot_legendre_terms+j
+                idx2 = n_nbh+i*tot_legendre_terms+deg_legendre+j
+                prior[idx1][idx2] = -prior[idx1][idx1]
+                prior[idx2][idx1] = -prior[idx2][idx2]
+    return prior
+
+def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, nleg, narch, method=None, n_nbh=None, prior_sigma=None, use_gpu=False, ncam=None):
     
     """This function calculates coefficients for archetype mode in each camera using normal linear algebra matrix solver or BVLS (bounded value least square) method
 
@@ -268,7 +302,24 @@ def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux
     ### TODO - implement BVLS on GPU
     # number of cameras in DESI: b, r, z
     spectra = target.spectra
-
+    method = method.upper()
+    add_negative_legendre_terms = False
+    if (method == 'BVLS' and use_gpu):
+        add_negative_legendre_terms = True
+
+    #Calculate prior here
+    prior = prior_on_coeffs(n_nbh, nleg, prior_sigma, ncam, add_negative_legendre_terms)
+
+    if (method == 'BVLS' and use_gpu):
+        #Hard-code se NNLS with additional legendre terms if BVLS AND gpu mode
+        method = 'NNLS'
+        #tdata should already have the additional negative legendre terms
+        nleg *= 2
+        if narch == 1:
+            #Use CPU if only 1 narch (nearest neighbor)
+            use_gpu = False
+
+    #Calculate nbasis after accounting for legendre terms
     nbasis = n_nbh+nleg*ncam # n_nbh : for actual physical archetype(s), nleg: number of legendre polynomials, ncamera: number of cameras
     
     if nleg>0:
@@ -277,7 +328,6 @@ def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux
         ret_zcoeff= {'alpha':[]} # in case no Legendre polynomials are used at all, only archetypes.
 
     #Setup dict of solver args to pass bounds to solver
-    method = method.upper()
     solver_args = dict()
     if (method == 'BVLS'):
         #only positive coefficients are allowed for the archetypes

From d1b7b8f868101b68db70c48e0e313a717484e183 Mon Sep 17 00:00:00 2001
From: Craig D Warner <cwarner@astro.ufl.edu>
Date: Thu, 9 May 2024 15:36:37 -0700
Subject: [PATCH 5/7] Compress output COEFF when using BVLS-NNLS trick to
 remove legendre terms with 0 coefficients and make the coeffs negative for
 negative terms.

Refactored to remove the last bit of the BVLS-NNLS trick from
archetypes.py and zfind.py by passing binned instead of tdata to
per_camera_coeff_with_least_square_batch.
---
 py/redrock/archetypes.py | 51 +++++++++++++++--------------
 py/redrock/zfind.py      |  8 ++---
 py/redrock/zscan.py      | 71 ++++++++++++++++++++++++++++++++++++++--
 3 files changed, 97 insertions(+), 33 deletions(-)

diff --git a/py/redrock/archetypes.py b/py/redrock/archetypes.py
index 7c111109..51ca61ca 100644
--- a/py/redrock/archetypes.py
+++ b/py/redrock/archetypes.py
@@ -203,7 +203,6 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
 
         nleg = legendre[list(legendre.keys())[0]].shape[0]
         iBest = np.argsort(zzchi2)[0:n_nearest]
-        tdata = dict()
         if (binned is not None):
             if (use_gpu):
                 binned = { hs:binned[hs][:,iBest].get() for hs in binned }
@@ -218,22 +217,23 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
                     #Only multiply if trans[hs] is not None
                     #Both arrays are on CPU so no need to wrap with asarray
                     binned[hs] *= trans[hs][:,None]
-        for hs, w in dwave.items():
-            tdata[hs] = binned[hs][None,:,:]
-            if (nleg > 0):
-                tdata[hs] = np.append(tdata[hs], legendre[hs].transpose()[None,:,:], axis=2)
-                if per_camera and self._solver_method == 'bvls' and use_gpu:
-                    #Using BVLS and GPU - append negative copies of legendre terms as well to use NNLS with additional bases to approximate BVLS
-                    tdata[hs] = np.append(tdata[hs], -1*legendre[hs].transpose()[None,:,:], axis=2)
-            nbasis = tdata[hs].shape[2]
             
         if per_camera:
             #Use CPU mode since small tdata
             #CW - 4/19/24 - pass use_gpu and solver method to per_camera_coeff_with_least_square_batch
             #it will decide there if narch == 1 to use CPU and it will calculate correct prior array
-            (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, nleg, 1, method=self._solver_method, n_nbh=n_nearest, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
+            #CW - 5/2/24 - pass binned instead of tdata to put all BVLS/NNLS code into per_camera_coeff_with_least_square_batch
+            #(zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, 1, method=self._solver_method, n_nbh=n_nearest, prior_sigma=prior_sigma, use_gpu=use_gpu, bands=target.bands)
+            (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, 1, method=self._solver_method, n_nbh=n_nearest, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
         else:
             #Use CPU mode for calc_zchi2 since small tdata
+            #Calculate tdata here because binned is passed to per_camera_coeff_with_least_square_batch
+            tdata = dict()
+            for hs, w in dwave.items():
+                tdata[hs] = binned[hs][None,:,:]
+                if (nleg > 0):
+                    tdata[hs] = np.append(tdata[hs], legendre[hs].transpose()[None,:,:], axis=2)
+                nbasis = tdata[hs].shape[2]
             (zzchi2, zzcoeff) = calc_zchi2_batch(spectra, tdata, weights, flux, wflux, 1, nbasis, use_gpu=False)
 
         sstype = ['%s'%(self._subtype[k]) for k in iBest] # subtypes of best archetypes
@@ -311,9 +311,6 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
         #Rebin in batch
         binned = self.rebin_template_batch(z, dwave, trapz=True, dedges=dedges, use_gpu=use_gpu)
 
-        tdata = dict()
-        nbasis = 1
-        
         ## Prior must be redefined to remove nearest neighbour approach, 
         # because prior was defined based on n_nearest argument..
         # this logic is needed because the first fitting is done with just one archetype 
@@ -328,22 +325,26 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
             if (trans[hs] is not None):
                 #Only multiply if trans[hs] is not None
                 binned[hs] *= arrtype.asarray(trans[hs][:,None])
-            #Create 3-d tdata with narch x nwave x nbasis where nbasis = 1+nleg
-            if nleg > 0:
-                tdata[hs] = arrtype.append(binned[hs].transpose()[:,:,None], arrtype.tile(arrtype.asarray(legendre[hs]).transpose()[None,:,:], (self._narch, 1, 1)), axis=2)
-                if solve_method == 'bvls' and use_gpu:
-                    #Using BVLS - append negative copies of legendre terms as well to use NNLS with additional bases to approximate BVLS
-                    tdata[hs] = arrtype.append(tdata[hs], arrtype.tile(-1*arrtype.asarray(legendre[hs]).transpose()[None,:,:], (self._narch, 1, 1)), axis=2)
-            else:
-                tdata[hs] = binned[hs].transpose()[:,:,None]
-            nbasis = tdata[hs].shape[2]
+
         if per_camera:
-            #Hard code NNLS as solve method for GPU mode only, use solve_method (BVLS) for CPU
+            #Use per_camera_coeff_with_least_square_batch which has all logic associated with BVLS/NNLS solver methods
             if (use_gpu):
-                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, gpuweights, gpuflux, gpuwflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
+                #(zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, gpuweights, gpuflux, gpuwflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, bands=bands)
+                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, gpuweights, gpuflux, gpuwflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
             else:
-                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
+                #(zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, bands=bands)
+                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
         else:
+            #Calculate tdata here because binned is passed to per_camera_coeff_with_least_square_batch
+            tdata = dict()
+            nbasis = 1
+            for hs, wave in dwave.items():
+                #Create 3-d tdata with narch x nwave x nbasis where nbasis = 1+nleg
+                if nleg > 0:
+                    tdata[hs] = arrtype.append(binned[hs].transpose()[:,:,None], arrtype.tile(arrtype.asarray(legendre[hs]).transpose()[None,:,:], (self._narch, 1, 1)), axis=2)
+                else:
+                    tdata[hs] = binned[hs].transpose()[:,:,None]
+                nbasis = tdata[hs].shape[2]
             if (use_gpu):
                 (zzchi2, zzcoeff) = calc_zchi2_batch(spectra, tdata, gpuweights, gpuflux, gpuwflux, self._narch, nbasis, use_gpu=use_gpu)
             else:
diff --git a/py/redrock/zfind.py b/py/redrock/zfind.py
index 41a06447..bb330828 100644
--- a/py/redrock/zfind.py
+++ b/py/redrock/zfind.py
@@ -251,10 +251,6 @@ def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=Non
     if archetypes:
         archetypes = All_archetypes(archetypes_dir=archetypes).archetypes
         archetype_spectype = list(archetypes.keys()) # to account for the case if only one archetype is provided
-        tot_deg_legendre = deg_legendre
-        for key in archetypes:
-            if archetypes[key]._solver_method == 'bvls':
-                tot_deg_legendre = deg_legendre*2
     
     if not priors is None:
         priors = Priors(priors)
@@ -603,9 +599,9 @@ def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=Non
             maxcoeff = np.max([t.template.nbasis for t in templates])
         else:
             if n_nearest is not None:
-                maxcoeff = max(np.max([t.template.nbasis for t in templates]), ncamera*(tot_deg_legendre)+n_nearest)
+                maxcoeff = max(np.max([t.template.nbasis for t in templates]), ncamera*(deg_legendre)+n_nearest)
             else:
-                maxcoeff = max(np.max([t.template.nbasis for t in templates]), ncamera*(tot_deg_legendre)+1)
+                maxcoeff = max(np.max([t.template.nbasis for t in templates]), ncamera*(deg_legendre)+1)
 
         if allzfit['coeff'].ndim == 1:
             ntarg = allzfit['coeff'].shape
diff --git a/py/redrock/zscan.py b/py/redrock/zscan.py
index 7405d68b..dbccf33e 100644
--- a/py/redrock/zscan.py
+++ b/py/redrock/zscan.py
@@ -272,7 +272,7 @@ def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera, add_negative_legendre_t
                 prior[idx2][idx1] = -prior[idx2][idx2]
     return prior
 
-def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux, nleg, narch, method=None, n_nbh=None, prior_sigma=None, use_gpu=False, ncam=None):
+def per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, narch, method=None, n_nbh=None, prior_sigma=None, use_gpu=False, ncam=None):
     
     """This function calculates coefficients for archetype mode in each camera using normal linear algebra matrix solver or BVLS (bounded value least square) method
 
@@ -282,7 +282,7 @@ def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux
 
     Args:
         target (object): target object
-        tdata (dict): template data for model fit for ALL archetypes
+        binned (dict): template data for model fit for ALL archetypes
         weights (array): concatenated spectral weights (ivar).
         flux (array): concatenated flux values.
         wflux (array): concatenated weighted flux values.
@@ -302,6 +302,19 @@ def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux
     ### TODO - implement BVLS on GPU
     # number of cameras in DESI: b, r, z
     spectra = target.spectra
+#    ncam = len(bands)
+#    nbasis = n_nbh+nleg*ncam # n_nbh : for actual physical archetype(s), nleg: number of legendre polynomials, ncamera: number of cameras
+
+#    if bands is None or bands[0] is None:
+#        bands = ['b', 'z', 'r']
+#    ret_zcoeff = {}
+#    ret_zcoeff['alpha'] = []
+#    if nleg>0:
+#        for b in bands: # bands as save in targets object
+#            ret_zcoeff[b] = []
+
+#    new_bands = sorted(bands) # saves as correct order
+
     method = method.upper()
     add_negative_legendre_terms = False
     if (method == 'BVLS' and use_gpu):
@@ -309,6 +322,47 @@ def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux
 
     #Calculate prior here
     prior = prior_on_coeffs(n_nbh, nleg, prior_sigma, ncam, add_negative_legendre_terms)
+    legendre = target.legendre(nleg=nleg, use_gpu=False) #Get previously calculated legendre
+
+#    if add_negative_legendre_terms:
+#        #Hard-code NNLS with additional legendre terms if BVLS AND gpu mode
+#        method = 'NNLS'
+#        #tdata should already have the additional negative legendre terms
+#        nleg *= 2
+#        if narch == 1:
+#            #Use CPU if only 1 narch (nearest neighbor)
+#            use_gpu = False
+
+#    #Calculate nbasis after accounting for legendre terms
+#    nbasis = n_nbh+nleg*ncam # n_nbh : for actual physical archetype(s), nleg: number of legendre polynomials, ncamera: number of cameras
+
+    #Calculate tdata arrays here instead of in archetypes
+    #Select np or cp for operations as arrtype
+    if (use_gpu):
+        import cupy as cp
+        arrtype = cp
+    else:
+        arrtype = np
+    tdata = dict()
+    for hs in binned: 
+        #Create 3-d tdata with narch x nwave x nbasis where nbasis = 1+nleg
+        if narch == 1:
+            tdata[hs] = binned[hs][None,:,:]
+            if nleg > 0:
+                tdata[hs] = np.append(tdata[hs], legendre[hs].transpose()[None,:,:], axis=2)
+                #if add_negative_legendre_terms:
+                if method == 'BVLS' and use_gpu:
+                    #Using BVLS and GPU - append negative copies of legendre terms as well to use NNLS with additional bases to approximate BVLS
+                    tdata[hs] = np.append(tdata[hs], -1*legendre[hs].transpose()[None,:,:], axis=2)
+        else:
+            if nleg > 0:
+                tdata[hs] = arrtype.append(binned[hs].transpose()[:,:,None], arrtype.tile(arrtype.asarray(legendre[hs]).transpose()[None,:,:], (narch, 1, 1)), axis=2)
+                #if add_negative_legendre_terms:
+                if method == 'BVLS' and use_gpu:
+                    #Using BVLS - append negative copies of legendre terms as well to use NNLS with additional bases to approximate BVLS
+                    tdata[hs] = arrtype.append(tdata[hs], arrtype.tile(-1*arrtype.asarray(legendre[hs]).transpose()[None,:,:], (narch, 1, 1)), axis=2)
+            else:
+                tdata[hs] = binned[hs].transpose()[:,:,None]
 
     if (method == 'BVLS' and use_gpu):
         #Hard-code se NNLS with additional legendre terms if BVLS AND gpu mode
@@ -364,6 +418,8 @@ def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux
                         tdata2[hs][:,k+nleg*i] = tdata[hs][j,:,k] # Legendre polynomials terms
                 tdata2[hs] = tdata2[hs][None,:,:]
             zzchi2[j], zzcoeff[j] = calc_zchi2_batch(spectra, tdata2, weights, flux, wflux, 1, nbasis, solve_matrices_algorithm=method, solver_args=solver_args, prior=prior, use_gpu=use_gpu)
+    if (add_negative_legendre_terms):
+        zzcoeff = remove_extra_legendre_terms(zzcoeff, n_nbh, ncam, nleg)
 
     # saving leading archetype coefficients in correct order
     ret_zcoeff['alpha'] = [zzcoeff[:,k] for k in range(n_nbh)] # archetype coefficient(s)
@@ -377,13 +433,24 @@ def per_camera_coeff_with_least_square_batch(target, tdata, weights, flux, wflux
         # In target spectra redrock saves values as 'b', 'z', 'r'.
         # So just re-ordering them here to 'b', 'r', 'z' for easier reading
         old_coeff = {band: split_coeff[i] for i, band in enumerate(['b', 'z', 'r'])}
+        #old_coeff = {band: split_coeff[i] for i, band in enumerate(bands)}
 
         for band in ['b', 'r', 'z']:# 3 cameras
+        #for band in new_bands:# 3 cameras
             ret_zcoeff[band] = old_coeff[band]
     coeff = np.concatenate(list(ret_zcoeff.values()), axis=1)
     #print(f'{time.time()-start} [sec] took for per camera BVLS method\n')
     return zzchi2, coeff
 
+def remove_extra_legendre_terms(zzcoeff, n_nbh, ncam, nleg):
+    nleg = nleg // 2
+    zzcoeff2 = np.zeros_like(zzcoeff, shape=(zzcoeff.shape[0], n_nbh+ncam*(nleg)))
+    zzcoeff2[:,:n_nbh] = zzcoeff[:,:n_nbh]
+    for j in range(ncam):
+        for l in range(nleg):
+            zzcoeff2[:,n_nbh+nleg*j+l] = zzcoeff[:,n_nbh+nleg*j*2+l] - zzcoeff[:,n_nbh+nleg*j*2+nleg+l]
+    return zzcoeff2
+
 def batch_dot_product_sparse(spectra, tdata, nz, use_gpu):
     """Calculate a batch dot product of the 3 sparse matrices in spectra
     with every template in tdata.  Sparse matrix libraries are used

From fd7661725bab695cf836851360f3614c298694dd Mon Sep 17 00:00:00 2001
From: Craig D Warner <cwarner@astro.ufl.edu>
Date: Thu, 9 May 2024 16:27:19 -0700
Subject: [PATCH 6/7] Refactor to bring more in-line with main where the
 conflicts mostly involve using a list of bands instead of ncam to be more
 general.

---
 py/redrock/archetypes.py | 14 ++++----
 py/redrock/zscan.py      | 73 +++++++++++++++-------------------------
 2 files changed, 35 insertions(+), 52 deletions(-)

diff --git a/py/redrock/archetypes.py b/py/redrock/archetypes.py
index 51ca61ca..e03330ec 100644
--- a/py/redrock/archetypes.py
+++ b/py/redrock/archetypes.py
@@ -199,6 +199,7 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
         #is multiplied by trans in get_best_archetype
         spectra = target.spectra
         nleg = target.nleg
+        bands = None
         legendre = target.legendre(nleg=nleg, use_gpu=False) #Get previously calculated legendre
 
         nleg = legendre[list(legendre.keys())[0]].shape[0]
@@ -223,8 +224,8 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
             #CW - 4/19/24 - pass use_gpu and solver method to per_camera_coeff_with_least_square_batch
             #it will decide there if narch == 1 to use CPU and it will calculate correct prior array
             #CW - 5/2/24 - pass binned instead of tdata to put all BVLS/NNLS code into per_camera_coeff_with_least_square_batch
-            #(zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, 1, method=self._solver_method, n_nbh=n_nearest, prior_sigma=prior_sigma, use_gpu=use_gpu, bands=target.bands)
-            (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, 1, method=self._solver_method, n_nbh=n_nearest, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
+            (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, 1, method=self._solver_method, n_nbh=n_nearest, prior_sigma=prior_sigma, use_gpu=use_gpu, bands=bands)
+            #(zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, 1, method=self._solver_method, n_nbh=n_nearest, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
         else:
             #Use CPU mode for calc_zchi2 since small tdata
             #Calculate tdata here because binned is passed to per_camera_coeff_with_least_square_batch
@@ -267,6 +268,7 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
         """
         spectra = target.spectra
         nleg = target.nleg
+        bands = None
         legendre = target.legendre(nleg=nleg, use_gpu=use_gpu) #Get previously calculated legendre
         solve_method = self._solver_method #get solve method from archetype class instead of passing as arg
 
@@ -329,11 +331,11 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
         if per_camera:
             #Use per_camera_coeff_with_least_square_batch which has all logic associated with BVLS/NNLS solver methods
             if (use_gpu):
-                #(zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, gpuweights, gpuflux, gpuwflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, bands=bands)
-                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, gpuweights, gpuflux, gpuwflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
+                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, gpuweights, gpuflux, gpuwflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, bands=bands)
+                #(zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, gpuweights, gpuflux, gpuwflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
             else:
-                #(zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, bands=bands)
-                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
+                (zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, bands=bands)
+                #(zzchi2, zzcoeff) = per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, self._narch, method=solve_method, n_nbh=1, prior_sigma=prior_sigma, use_gpu=use_gpu, ncam=ncam)
         else:
             #Calculate tdata here because binned is passed to per_camera_coeff_with_least_square_batch
             tdata = dict()
diff --git a/py/redrock/zscan.py b/py/redrock/zscan.py
index dbccf33e..d54fc941 100644
--- a/py/redrock/zscan.py
+++ b/py/redrock/zscan.py
@@ -272,7 +272,7 @@ def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera, add_negative_legendre_t
                 prior[idx2][idx1] = -prior[idx2][idx2]
     return prior
 
-def per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, narch, method=None, n_nbh=None, prior_sigma=None, use_gpu=False, ncam=None):
+def per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, narch, method=None, n_nbh=None, prior_sigma=None, use_gpu=False, bands=None):
     
     """This function calculates coefficients for archetype mode in each camera using normal linear algebra matrix solver or BVLS (bounded value least square) method
 
@@ -292,6 +292,7 @@ def per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflu
         n_nbh (int): number of nearest best archetypes
         prior (array): prior matrix added to the Legendre coefficients (1/sigma^2)
         use_gpu (bool): use GPU or not
+        bands (list): list of wavelength bands
         ncam (int): number of cameras for given instrument
     
     Returns:
@@ -302,18 +303,17 @@ def per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflu
     ### TODO - implement BVLS on GPU
     # number of cameras in DESI: b, r, z
     spectra = target.spectra
-#    ncam = len(bands)
-#    nbasis = n_nbh+nleg*ncam # n_nbh : for actual physical archetype(s), nleg: number of legendre polynomials, ncamera: number of cameras
+    if bands is None:
+        bands = ['b', 'z', 'r']
+    ncam = len(bands)
 
-#    if bands is None or bands[0] is None:
-#        bands = ['b', 'z', 'r']
-#    ret_zcoeff = {}
-#    ret_zcoeff['alpha'] = []
-#    if nleg>0:
-#        for b in bands: # bands as save in targets object
-#            ret_zcoeff[b] = []
+    ret_zcoeff = {}
+    ret_zcoeff['alpha'] = []
+    if nleg>0:
+        for b in bands: # bands as save in targets object
+            ret_zcoeff[b] = []
 
-#    new_bands = sorted(bands) # saves as correct order
+    new_bands = sorted(bands) # saves as correct order
 
     method = method.upper()
     add_negative_legendre_terms = False
@@ -324,17 +324,17 @@ def per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflu
     prior = prior_on_coeffs(n_nbh, nleg, prior_sigma, ncam, add_negative_legendre_terms)
     legendre = target.legendre(nleg=nleg, use_gpu=False) #Get previously calculated legendre
 
-#    if add_negative_legendre_terms:
-#        #Hard-code NNLS with additional legendre terms if BVLS AND gpu mode
-#        method = 'NNLS'
-#        #tdata should already have the additional negative legendre terms
-#        nleg *= 2
-#        if narch == 1:
-#            #Use CPU if only 1 narch (nearest neighbor)
-#            use_gpu = False
+    if add_negative_legendre_terms:
+        #Hard-code NNLS with additional legendre terms if BVLS AND gpu mode
+        method = 'NNLS'
+        #tdata should already have the additional negative legendre terms
+        nleg *= 2
+        if narch == 1:
+            #Use CPU if only 1 narch (nearest neighbor)
+            use_gpu = False
 
-#    #Calculate nbasis after accounting for legendre terms
-#    nbasis = n_nbh+nleg*ncam # n_nbh : for actual physical archetype(s), nleg: number of legendre polynomials, ncamera: number of cameras
+    #Calculate nbasis after accounting for legendre terms
+    nbasis = n_nbh+nleg*ncam # n_nbh : for actual physical archetype(s), nleg: number of legendre polynomials, ncamera: number of cameras
 
     #Calculate tdata arrays here instead of in archetypes
     #Select np or cp for operations as arrtype
@@ -350,37 +350,20 @@ def per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflu
             tdata[hs] = binned[hs][None,:,:]
             if nleg > 0:
                 tdata[hs] = np.append(tdata[hs], legendre[hs].transpose()[None,:,:], axis=2)
-                #if add_negative_legendre_terms:
-                if method == 'BVLS' and use_gpu:
+                if add_negative_legendre_terms:
+                #if method == 'BVLS' and use_gpu:
                     #Using BVLS and GPU - append negative copies of legendre terms as well to use NNLS with additional bases to approximate BVLS
                     tdata[hs] = np.append(tdata[hs], -1*legendre[hs].transpose()[None,:,:], axis=2)
         else:
             if nleg > 0:
                 tdata[hs] = arrtype.append(binned[hs].transpose()[:,:,None], arrtype.tile(arrtype.asarray(legendre[hs]).transpose()[None,:,:], (narch, 1, 1)), axis=2)
-                #if add_negative_legendre_terms:
-                if method == 'BVLS' and use_gpu:
+                if add_negative_legendre_terms:
+                #if method == 'BVLS' and use_gpu:
                     #Using BVLS - append negative copies of legendre terms as well to use NNLS with additional bases to approximate BVLS
                     tdata[hs] = arrtype.append(tdata[hs], arrtype.tile(-1*arrtype.asarray(legendre[hs]).transpose()[None,:,:], (narch, 1, 1)), axis=2)
             else:
                 tdata[hs] = binned[hs].transpose()[:,:,None]
 
-    if (method == 'BVLS' and use_gpu):
-        #Hard-code se NNLS with additional legendre terms if BVLS AND gpu mode
-        method = 'NNLS'
-        #tdata should already have the additional negative legendre terms
-        nleg *= 2
-        if narch == 1:
-            #Use CPU if only 1 narch (nearest neighbor)
-            use_gpu = False
-
-    #Calculate nbasis after accounting for legendre terms
-    nbasis = n_nbh+nleg*ncam # n_nbh : for actual physical archetype(s), nleg: number of legendre polynomials, ncamera: number of cameras
-    
-    if nleg>0:
-        ret_zcoeff= {'alpha':[], 'b':[], 'r':[], 'z':[]}
-    else:
-        ret_zcoeff= {'alpha':[]} # in case no Legendre polynomials are used at all, only archetypes.
-
     #Setup dict of solver args to pass bounds to solver
     solver_args = dict()
     if (method == 'BVLS'):
@@ -432,11 +415,9 @@ def per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflu
         split_coeff =  np.split(zzcoeff[:,n_nbh:], ncam, axis=1) # n_camera = 3
         # In target spectra redrock saves values as 'b', 'z', 'r'.
         # So just re-ordering them here to 'b', 'r', 'z' for easier reading
-        old_coeff = {band: split_coeff[i] for i, band in enumerate(['b', 'z', 'r'])}
-        #old_coeff = {band: split_coeff[i] for i, band in enumerate(bands)}
+        old_coeff = {band: split_coeff[i] for i, band in enumerate(bands)}
 
-        for band in ['b', 'r', 'z']:# 3 cameras
-        #for band in new_bands:# 3 cameras
+        for band in new_bands:# 3 cameras
             ret_zcoeff[band] = old_coeff[band]
     coeff = np.concatenate(list(ret_zcoeff.values()), axis=1)
     #print(f'{time.time()-start} [sec] took for per camera BVLS method\n')

From a4a953103fe4f7ba3fb70fbc4f4c716b325a6266 Mon Sep 17 00:00:00 2001
From: Craig D Warner <cwarner@astro.ufl.edu>
Date: Tue, 14 May 2024 11:48:53 -0700
Subject: [PATCH 7/7] Created a new branch to handle the merging of
 legendre_nnls and main because results were different and timing as well.

---
 doc/changes.rst                        |  48 ++++-
 py/redrock/_version.py                 |   2 +-
 py/redrock/archetypes.py               | 128 +++++++++---
 py/redrock/constants.py                |   1 +
 py/redrock/external/desi.py            | 244 ++++++++++++++++++-----
 py/redrock/fitz.py                     |  30 +--
 py/redrock/plotspec.py                 |   5 +-
 py/redrock/results.py                  |  58 +++---
 py/redrock/targets.py                  | 116 +++++++++--
 py/redrock/templates.py                | 261 +++++++++++++++++++------
 py/redrock/test/data/README            |  12 ++
 py/redrock/test/data/redrock-test.fits |  10 +
 py/redrock/test/data/rrdetails-test.h5 | Bin 0 -> 607162 bytes
 py/redrock/test/test_archetypes.py     |  43 ++++
 py/redrock/test/test_io.py             | 102 ++--------
 py/redrock/test/test_templates.py      | 190 ++++++++++++++++++
 py/redrock/test/test_utils.py          |  21 ++
 py/redrock/utils.py                    |  21 ++
 py/redrock/zfind.py                    |  45 +++--
 py/redrock/zscan.py                    |  43 ++--
 20 files changed, 1073 insertions(+), 307 deletions(-)
 create mode 100644 py/redrock/test/data/README
 create mode 100644 py/redrock/test/data/redrock-test.fits
 create mode 100644 py/redrock/test/data/rrdetails-test.h5
 create mode 100644 py/redrock/test/test_archetypes.py
 create mode 100644 py/redrock/test/test_templates.py

diff --git a/doc/changes.rst b/doc/changes.rst
index 0c93ded9..7f89e94d 100644
--- a/doc/changes.rst
+++ b/doc/changes.rst
@@ -2,19 +2,60 @@
 redrock Change Log
 ==================
 
-0.19.0 (unreleased)
+0.20.1 (unreleased)
+-------------------
+
+* No changes yet.
+
+0.20.0 (2024-05-03)
+-------------------
+
+Used for DESI Jura run.
+
+* Add rrdesi --model option to pre-generate model files (PR `#283`_).
+* Add load_templates_from_header (PR `#290`_).
+* Add units to fibermap output (PR `#292`_).
+* Set ZWARN/NODATA to z=0 GALAXY, no SUBTYPE, zero coefficients (PR `#294`_).
+* Add TEMFILnn keywords to header to support non-standard template names
+  (PR `#295`_).
+* Fix read_zscan; add make_fulltype, parse_fulltype to standardize
+  fulltype,spectype,subtype parsing (PR `#297`_).
+* Fix warning from zero-sized array (PR `#298`_).
+* Standardize wavelength -> [-1,1] mapping for Legendre poly (PR `#299`_).
+* Set max_velo_diff to 100 km/s for stars (PR `#300`_).
+* Ensure `FITMETHOD` holds 4-string `NONE` (addresses `#301`) (PR `#303`_).
+* Fix FITMETHOD bytes vs. str for details file output (PR `#304`_).
+* Remove unnecessary desispec dependency (PR `#306`_).
+
+.. _`#283`: https://github.com/desihub/redrock/pull/283
+.. _`#290`: https://github.com/desihub/redrock/pull/290
+.. _`#292`: https://github.com/desihub/redrock/pull/292
+.. _`#294`: https://github.com/desihub/redrock/pull/294
+.. _`#295`: https://github.com/desihub/redrock/pull/295
+.. _`#297`: https://github.com/desihub/redrock/pull/297
+.. _`#298`: https://github.com/desihub/redrock/pull/298
+.. _`#299`: https://github.com/desihub/redrock/pull/299
+.. _`#300`: https://github.com/desihub/redrock/pull/300
+.. _`#301`: https://github.com/desihub/redrock/issues/301
+.. _`#303`: https://github.com/desihub/redrock/pull/303
+.. _`#304`: https://github.com/desihub/redrock/pull/304
+.. _`#306`: https://github.com/desihub/redrock/pull/306
+
+0.19.0 (2024-04-19)
 -------------------
 
 * Write test files to temporary directory (PR `#263`_).
 * Check template dimension so code works on a single template (PR `#264`_). 
 * Versioned templates, NMF support, and updated IGM models (PR `#271`_).
+* Mask significantly negative flux (PR `#282`_).
+* Add ``results.read_zscan(..., select=targetids=...)`` option (PR `#289`_).
 * Archetype updates:
 
   * Added default --nminima for archetypes (PR `#265`_).
   * Adjust coeff size for Legendre for archetypes (PR `#266`_).
   * Docstring cleanup (PR `#267`_).
   * Fix --archetype_nnearest option (PR `#272`_).
-
+  * fix --archetype-legendre-degree=0 crash corner case (PR `#278`_).
 
 .. _`#263`: https://github.com/desihub/redrock/pull/263
 .. _`#264`: https://github.com/desihub/redrock/pull/264
@@ -23,6 +64,9 @@ redrock Change Log
 .. _`#267`: https://github.com/desihub/redrock/pull/267
 .. _`#271`: https://github.com/desihub/redrock/pull/271
 .. _`#272`: https://github.com/desihub/redrock/pull/272
+.. _`#278`: https://github.com/desihub/redrock/pull/278
+.. _`#282`: https://github.com/desihub/redrock/pull/282
+.. _`#289`: https://github.com/desihub/redrock/pull/289
 
 0.18.0 (2023-09-14)
 -------------------
diff --git a/py/redrock/_version.py b/py/redrock/_version.py
index a950f3e3..03e2d79a 100644
--- a/py/redrock/_version.py
+++ b/py/redrock/_version.py
@@ -1 +1 @@
-__version__ = '0.18.0.dev797'
+__version__ = '0.20.0.dev1043'
diff --git a/py/redrock/archetypes.py b/py/redrock/archetypes.py
index e03330ec..3b5dc161 100644
--- a/py/redrock/archetypes.py
+++ b/py/redrock/archetypes.py
@@ -7,10 +7,13 @@
 import os
 from glob import glob
 from astropy.io import fits
+from astropy.table import Table
 import numpy as np
 from scipy.interpolate import interp1d
 import scipy.special
 
+from .utils import reduced_wavelength
+
 from .zscan import calc_zchi2_one, calc_zchi2_batch
 
 from .rebin import trapz_rebin
@@ -19,6 +22,8 @@
 
 from .zscan import per_camera_coeff_with_least_square_batch
 
+from .templates import make_fulltype
+
 class Archetype():
     """Class to store all different archetypes from the same spectype.
 
@@ -31,6 +36,7 @@ class Archetype():
     def __init__(self, filename):
 
         # Load the file
+        self._filename = filename
         h = fits.open(os.path.expandvars(filename), memmap=False)
 
         hdr = h['ARCHETYPES'].header
@@ -40,7 +46,7 @@ def __init__(self, filename):
         self._rrtype = hdr['RRTYPE'].strip()
         self._subtype = np.array(np.char.strip(h['ARCHETYPES'].data['SUBTYPE'].astype(str)))
         self._subtype = np.char.add(np.char.add(self._subtype,'_'),np.arange(self._narch,dtype=int).astype(str))
-        self._full_type = np.char.add(self._rrtype+':::',self._subtype)
+        self._full_type = np.array([make_fulltype(self._rrtype, subtype) for subtype in self._subtype])
         self._version = hdr['VERSION']
 
         self.wave = np.asarray(hdr['CRVAL1'] + hdr['CDELT1']*np.arange(self.flux.shape[1]))
@@ -66,6 +72,8 @@ def __init__(self, filename):
         # TODO: Allow Archetype files to specify bvls or nnls or pca solver method
         self._solver_method = 'bvls'
 
+        self.method = 'ARCH'  # for API symmetry with Template.method
+
         return
 
     @property
@@ -84,6 +92,27 @@ def gpuflux(self):
             self._gpuflux = cp.asarray(self.flux)
         return self._gpuflux
 
+    @property
+    def version(self):
+        return self._version
+
+    @property
+    def filename(self):
+        return self._filename
+
+    #- template_type, sub_type, and full_type like Template object methods
+    @property
+    def template_type(self):
+        return self._rrtype
+
+    @property
+    def sub_type(self):
+        return self._subtype
+
+    @property
+    def full_type(self):
+        return self._full_type
+
     def rebin_template(self,index,z,dwave,trapz=True):
         """
         """
@@ -149,23 +178,46 @@ def rebin_template_batch(self,z,dwave,trapz=True,dedges=None,indx=None,use_gpu=F
             #return {hs:self._archetype['INTERP'](wave/(1.+z)) for hs, wave in dwave.items()}
         return result
 
-    def eval(self, subtype, dwave, coeff, wave, z):
-        """
+    def eval(self, subtype, coeff, wave, z, R=None, legcoeff=None):
+        """Return archetype for given subtype, coefficients, wavelengths, and redshift
+
+        Args:
+            subtype (str) : comma separated str of archetype subtype(s)
+            coeff : array of archetype coefficients
+            wave : wavelengths at which to evaluate template flux
+            z : redshift at which to evaluate template flux
+
+        Options:
+            R : array[nwave,nwave] resolution matrix to convolve with model
+            legcoeff : array of additional legendre coefficients
 
+        Returns:
+            archetype flux array
+
+        Notes:
+            No factors of (1+z) are applied to the resampled flux, i.e.
+            evaluating the same coeffs at different z does not conserve
+            integrated flux, but more directly maps model=templates.dot(coeff)
         """
 
-        deg_legendre = (coeff!=0.).size-1
-        index = np.arange(self._narch)[self._subtype==subtype][0]
+        subtypes = subtype.split(';')
+        model = np.zeros(len(wave))
+        for this_subtype, c in zip(subtypes, coeff[0:len(subtypes)]):
+            index = np.where(self._subtype == this_subtype)[0][0]
+            binned_archetype = trapz_rebin((1+z)*self.wave, self.flux[index], wave)
+            binned_archetype *= transmission_Lyman(z,wave,model=self.igm_model)
+
+            model += c*binned_archetype
 
-        w = np.concatenate([ w for w in dwave.values() ])
-        wave_min = w.min()
-        wave_max = w.max()
-        legendre = np.array([scipy.special.legendre(i)( (wave-wave_min)/(wave_max-wave_min)*2.-1. ) for i in range(deg_legendre)])
-        binned = trapz_rebin((1+z)*self.wave, self.flux[index], wave)*transmission_Lyman(z,wave,model=self.igm_model)
-        flux = np.append(binned[None,:],legendre, axis=0)
-        flux = flux.T.dot(coeff).T / (1+z)
+        if legcoeff is not None:
+            deg_legendre = len(legcoeff)
+            legendre = np.array([scipy.special.legendre(i)( reduced_wavelength(wave) ) for i in range(deg_legendre)])
+            model += legendre.T.dot(legcoeff).T
 
-        return flux
+        if R is not None:
+            model = R.dot(model)
+
+        return model
     
     def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest, zzchi2, trans, per_camera, dedges=None, binned=None, use_gpu=False, prior_sigma=None, ncam=None):
         
@@ -238,17 +290,17 @@ def nearest_neighbour_model(self, target,weights,flux,wflux,dwave,z, n_nearest,
             (zzchi2, zzcoeff) = calc_zchi2_batch(spectra, tdata, weights, flux, wflux, 1, nbasis, use_gpu=False)
 
         sstype = ['%s'%(self._subtype[k]) for k in iBest] # subtypes of best archetypes
-        fsstype = '_'.join(sstype)
+        fsstype = ';'.join(sstype)
         #print(sstype)
         #print(z, zzchi2, zzcoeff, fsstype)
-        return zzchi2[0], zzcoeff[0], self._rrtype+':::%s'%(fsstype)
+        return zzchi2[0], zzcoeff[0], make_fulltype(self._rrtype, fsstype)
 
     def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nearest, trans=None, prior_sigma=None, use_gpu=False):
 
         """Get the best archetype for the given redshift and spectype.
 
         Args:
-            spectra (list): list of Spectrum objects.
+            target (object): target object.
             weights (array): concatenated spectral weights (ivar).
             flux (array): concatenated flux values.
             wflux (array): concatenated weighted flux values.
@@ -257,8 +309,8 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
             per_camera (bool): True if fitting needs to be done in each camera
             n_nearest (int): number of nearest neighbours to be used in chi2 space (including best archetype)
             trans (dict): pass previously calcualated Lyman transmission instead of recalculating
-            use_gpu (bool): use GPU or not
             prior_sigma (float): prior to add in the final solution matrix: added as 1/(prior_sigma**2) only for per-camera mode
+            use_gpu (bool): use GPU or not
             
         Returns:
             chi2 (float): chi2 of best archetype
@@ -271,6 +323,7 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
         bands = None
         legendre = target.legendre(nleg=nleg, use_gpu=use_gpu) #Get previously calculated legendre
         solve_method = self._solver_method #get solve method from archetype class instead of passing as arg
+        bands = target.bands
 
         #Select np or cp for operations as arrtype
         if (use_gpu):
@@ -291,10 +344,9 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
         else:
             ncam = 1 # entire spectra
         
-        wkeys = list(dwave.keys())
-        new_keys = [wkeys[0], wkeys[2], wkeys[1]]
-
-        obs_wave = np.concatenate([dwave[key] for key in new_keys])
+        #wkeys = list(dwave.keys())
+        #new_keys = [wkeys[0], wkeys[2], wkeys[1]]
+        #obs_wave = np.concatenate([dwave[key] for key in new_keys])
         
         #nleg = legendre[list(legendre.keys())[0]].shape[0]
         
@@ -327,7 +379,6 @@ def get_best_archetype(self,target,weights,flux,wflux,dwave,z, per_camera, n_nea
             if (trans[hs] is not None):
                 #Only multiply if trans[hs] is not None
                 binned[hs] *= arrtype.asarray(trans[hs][:,None])
-
         if per_camera:
             #Use per_camera_coeff_with_least_square_batch which has all logic associated with BVLS/NNLS solver methods
             if (use_gpu):
@@ -387,6 +438,36 @@ def __init__(self, lstfilename=None, archetypes_dir=None, verbose=False):
 
         return
 
+def split_archetype_coeff(subtype, coeff, nbands, nleg=None):
+    """
+    Split coeff array into archetype + legendre terms
+
+    Args:
+        subtype (str): comma separated archetype subtypes
+        coeff (array): coefficients from redrock fit
+        nbands (int): number of spectrograph bands (e.g. 3 for DESI b/r/z)
+
+    Options
+        nleg (int): number of legendre terms per band
+
+    Returns (archcoeff, legcoeff) where archcoeff is array of archetype coefficients
+    for each subtype, and legcoeff is list of legendre coefficients per band.
+
+    If nleg is None, it will be derived from counting non-zero terms of coeff.
+    Expected length of non-zero coeffs is num_subtypes + nbands*nleg.
+    """
+    narchetypes = len(subtype.split(';'))
+    archcoeff = coeff[0:narchetypes]
+    all_legcoeff = coeff[narchetypes:]
+
+    if nleg is None:
+        # derive number of legendre coefficients used from non-zero terms
+        nleg = np.count_nonzero(all_legcoeff) // nbands
+
+    legcoeff = [all_legcoeff[i*nleg:(i+1)*nleg] for i in range(nbands)]
+
+    return archcoeff, legcoeff
+
 def find_archetypes(archetypes_dir=None):
     """Return list of rrarchetype-\*.fits archetype files
 
@@ -422,3 +503,6 @@ def find_archetypes(archetypes_dir=None):
             lstfilename = sorted([ f.replace(archetypes_dir_expand,archetypes_dir) for f in lstfilename])
 
     return lstfilename
+
+
+
diff --git a/py/redrock/constants.py b/py/redrock/constants.py
index 1b707267..f9be64d8 100644
--- a/py/redrock/constants.py
+++ b/py/redrock/constants.py
@@ -5,6 +5,7 @@
 Set constants used by the rest of the package.
 """
 max_velo_diff = 1000.0  # km/s
+max_velo_diff_star = 100.0  # km/s
 min_resolution_integral = 0.99
 
 min_deltachi2 = 9.
diff --git a/py/redrock/external/desi.py b/py/redrock/external/desi.py
index 0572350e..cb740b33 100644
--- a/py/redrock/external/desi.py
+++ b/py/redrock/external/desi.py
@@ -9,13 +9,14 @@
 import re
 import warnings
 import traceback
+import itertools
 
 import argparse
 
 import numpy as np
 
 from astropy.io import fits
-from astropy.table import Table
+from astropy.table import Table, vstack
 
 from desiutil.io import encode_table
 from desiutil.depend import add_dependencies, setdep
@@ -24,6 +25,8 @@
 from desispec.coaddition import coadd_fibermap
 from desispec.specscore import compute_coadd_tsnr_scores
 from desispec.maskbits import fibermask
+from desispec.io.util import get_tempfilename
+from desispec.io.fibermap import annotate_fibermap
 
 from ..utils import elapsed, get_mp, distribute_work, getGPUCountMPI
 
@@ -39,38 +42,36 @@
 
 from .._version import __version__
 
-from ..archetypes import All_archetypes
-
-
-def write_zbest(outfile, zbest, fibermap, exp_fibermap, tsnr2,
-        template_version, archetype_version,
-        spec_header=None):
-    """Write zbest and fibermap Tables to outfile
+from ..archetypes import All_archetypes, Archetype
 
+def _get_header(templates, archetypes=None, spec_header=None):
+    """Get standardized header with template and archetype versions
+    
     Args:
-        outfile (str): output path.
-        zbest (Table): best fit table.
-        fibermap (Table): the coadded fibermap from the original inputs.
-        tsnr2 (Table): table of input coadded TSNR2 values
-        exp_fibermap (Table): the per-exposure fibermap from the orig inputs.
-        template_version (str): template version used
-        archetype_version (str): archetype version used
+        templates : list or dict of template objects
 
     Options:
-        spec_header (dict-like): header of HDU 0 of input spectra
-
-    Modifies input tables.meta['EXTNAME']
+        archetypes : list or dict of Archetype objects
+        spec_header (dict-like): header of HDU 0 of input spectra/coadd
     """
+    if isinstance(templates, dict):
+        templates = templates.values()
+
+    if isinstance(archetypes, dict):
+        archetypes = archetypes.values()
+
     header = fits.Header()
     header['LONGSTRN'] = 'OGIP 1.0'
     header['RRVER'] = (__version__, 'Redrock version')
-    for i, fulltype in enumerate(template_version.keys()):
-        header['TEMNAM'+str(i).zfill(2)] = fulltype
-        header['TEMVER'+str(i).zfill(2)] = template_version[fulltype]
-    if not archetype_version is None:
-        for i, fulltype in enumerate(archetype_version.keys()):
-            header['ARCNAM'+str(i).zfill(2)] = fulltype
-            header['ARCVER'+str(i).zfill(2)] = archetype_version[fulltype]
+    for i, t in enumerate(templates):
+        header[f'TEMNAM{i:02d}'] = t.full_type
+        header[f'TEMVER{i:02d}'] = t.version
+        header[f'TEMFIL{i:02d}'] = os.path.basename(t.filename)
+    if archetypes is not None:
+        for i, atyp in enumerate(archetypes):
+            header[f'ARCNAM{i:02d}'] = atyp.template_type
+            header[f'ARCVER{i:02d}'] = atyp.version
+            header[f'ARCFIL{i:02d}'] = os.path.basename(atyp.filename)
 
     # record code versions and key environment variables
     add_dependencies(header)
@@ -85,6 +86,29 @@ def write_zbest(outfile, zbest, fibermap, exp_fibermap, tsnr2,
             if key in spec_header:
                 header[key] = spec_header[key]
 
+    return header
+
+def write_zbest(outfile, zbest, fibermap, exp_fibermap, tsnr2,
+        templates, archetypes=None,
+        spec_header=None):
+    """Write zbest and fibermap Tables to outfile
+
+    Args:
+        outfile (str): output path.
+        zbest (Table): best fit table.
+        fibermap (Table): the coadded fibermap from the original inputs.
+        tsnr2 (Table): table of input coadded TSNR2 values
+        exp_fibermap (Table): the per-exposure fibermap from the orig inputs.
+        templates : list or dict of template objects
+
+    Options:
+        archetypes : list or dict of Archetype objects
+        spec_header (dict-like): header of HDU 0 of input spectra
+
+    Modifies input tables.meta['EXTNAME']
+    """
+    header = _get_header(templates, archetypes, spec_header)
+
     zbest.meta['EXTNAME'] = 'REDSHIFTS'
     fibermap.meta['EXTNAME'] = 'FIBERMAP'
     exp_fibermap.meta['EXTNAME'] = 'EXP_FIBERMAP'
@@ -93,7 +117,8 @@ def write_zbest(outfile, zbest, fibermap, exp_fibermap, tsnr2,
     hx = fits.HDUList()
     hx.append(fits.PrimaryHDU(header=header))
     hx.append(fits.convenience.table_to_hdu(zbest))
-    hx.append(fits.convenience.table_to_hdu(fibermap))
+    # ADM annotate_fibermap adds units to fibermap HDUs.
+    hx.append(annotate_fibermap(fits.convenience.table_to_hdu(fibermap)))
     hx.append(fits.convenience.table_to_hdu(exp_fibermap))
     hx.append(fits.convenience.table_to_hdu(tsnr2))
 
@@ -108,6 +133,54 @@ def write_zbest(outfile, zbest, fibermap, exp_fibermap, tsnr2,
 
     return
 
+def write_bestmodel(outfile, zbest, modeldict, wavedict, templates,
+                    archetypes=None, spec_header=None):
+    """
+    Writes best fit redrock model in the outfile
+
+    Args:
+        outfile (str): output file name (fits)
+        zbest (Table): best fit redshift table
+        modeldict (dict): models[ntargets, nwave] keyed by camera band
+        wavedict (dict): wavelength dictionary keyed by camera band
+        templates : list or dict of template objects
+
+    Options:
+        archetypes : list or dict of Archetype objects
+        spec_header (dict-like): header of HDU 0 of input spectra
+
+    The output file format mirrors the structure of an input coadd, with B/R/Z_MODEL
+    HDUs instead of B/R/Z_FLUX HDUs.
+    """
+    header = _get_header(templates, archetypes, spec_header)
+    
+    zbest.meta['EXTNAME'] = 'REDSHIFTS'
+    hx = fits.HDUList()
+    hx.append(fits.PrimaryHDU(header=header))
+    hx.append(fits.convenience.table_to_hdu(zbest))
+
+    for band in wavedict.keys():
+        BAND = band.upper()
+        hdu = fits.ImageHDU(name=f'{BAND}_WAVELENGTH')
+        hdu.data = wavedict[band]
+        hdu.header["BUNIT"] = "Angstrom"
+        hx.append(hdu)
+
+        hdu = fits.ImageHDU(name=f'{BAND}_MODEL')
+        hdu.data = modeldict[band].astype('float32')
+        hx.append(hdu)
+
+    outfile = os.path.expandvars(outfile)
+    outdir = os.path.dirname(os.path.abspath(outfile))
+    if not os.path.exists(outdir):
+        os.makedirs(outdir)
+
+    tempfile = get_tempfilename(outfile)
+    hx.writeto(tempfile, overwrite=True)
+    os.rename(tempfile, outfile)
+
+    return
+
 
 class DistTargetsDESI(DistTargets):
     """Distributed targets for DESI.
@@ -400,8 +473,8 @@ def __init__(self, spectrafiles, coadd=True, targetids=None,
                         nspec = len(self._target_specs[sfile][t])
                         for s in range(nspec):
                             sindx = self._target_specs[sfile][t][s]
-                            speclist.append(Spectrum(self._wave[sfile][b],
-                                None, None, None, None))
+                            speclist.append(Spectrum(wave=self._wave[sfile][b],
+                                flux=None, ivar=None, R=None, band=b))
 
             self._my_data.append(Target(t, speclist, coadd=False))
 
@@ -575,7 +648,7 @@ def rrdesi(options=None, comm=None):
         required=False, help="If True, in archetype mode the fitting will be done for each camera/band")
     
     parser.add_argument("--archetype-legendre-prior", type=float, default=0.1,
-        required=False, help="sigma to add as prior in solving linear equation, 1/sig**2 will be added, default is 0.1")
+                        required=False, help="sigma to add as prior in solving linear equation, 1/sig**2 will be added, default is 0.1, Note: provide anything <=0 if you do not want to add any prior")
     
     parser.add_argument("--archetypes-no-legendre", default=False, action="store_true",
         required=False, help="Use this flag with archetypes if want to TURN OFF all archetype related default values")
@@ -588,6 +661,9 @@ def rrdesi(options=None, comm=None):
 
     parser.add_argument("-o", "--outfile", type=str, default=None,
         required=False, help="output FITS file with best redshift per target")
+    
+    parser.add_argument("--model", type=str, default=None,
+        required=False, help="output FITS file containing spectral model")
 
     parser.add_argument("--targetids", type=str, default=None,
         required=False, help="comma-separated list of target IDs")
@@ -657,7 +733,7 @@ def rrdesi(options=None, comm=None):
         args = parser.parse_args()
     else:
         args = parser.parse_args(options)
-
+    
     if args.ncpu is not None:
         print('WARNING: --ncpu is deprecated; use --mp instead')
         args.mp = args.ncpu
@@ -727,7 +803,7 @@ def rrdesi(options=None, comm=None):
                     comm.Abort()
                 else:
                     sys.exit(1)
-         
+
         if args.archetypes is not None:
             print('\n===== Archetype argument is provided, doing necessary checks=======\n')
             if os.path.exists(args.archetypes) and os.access(args.archetypes, os.R_OK):
@@ -754,6 +830,11 @@ def rrdesi(options=None, comm=None):
                 else:
                     sys.exit(1)
 
+    #- All ranks read the archetypes file(s)
+    archetypes = None
+    if args.archetypes is not None:
+        archetypes = All_archetypes(archetypes_dir=args.archetypes, verbose=(comm_rank==0)).archetypes
+
     targetids = None
     if args.targetids is not None:
         targetids = [ int(x) for x in args.targetids.split(",") ]
@@ -859,7 +940,7 @@ def rrdesi(options=None, comm=None):
                     ii |= (9792. <= s.wave) & (s.wave <= 9795.)
                     s.ivar[ii] = 0.0
 
-        # Get the dictionary of wavelength grids
+        # Get the dictionary of wavelength grids (with keys as wavehashes)
         dwave = targets.wavegrids()
         
         ncamera = len(list(dwave.keys())) # number of cameras for given instrument
@@ -872,9 +953,9 @@ def rrdesi(options=None, comm=None):
         else:
             if comm_rank == 0 and args.archetypes is not None:
                 print('Will be using default archetype values.') 
-                print('number of minimum redshift for which archetype redshift fitting will be done = %d'%(nminima))
+                print('Number of minimum redshift for which archetype redshift fitting will be done = %d'%(nminima))
 
-            if ncamera>1: 
+            if ncamera>=1: 
                 archetype_legendre_percamera = True
                 if comm_rank == 0 and args.archetypes is not None:
                     print('Number of cameras = %d, percamera fitting will be done'%(ncamera))
@@ -888,9 +969,16 @@ def rrdesi(options=None, comm=None):
             else:
                 if comm_rank == 0 and args.archetypes is not None:
                     print('No Legendre polynomial will be added to archetypes.')
-            archetype_legendre_prior = args.archetype_legendre_prior
+            if args.archetype_legendre_prior<=0:
+                archetype_legendre_prior = None
+                printstr = 'no'
+            else:
+                archetype_legendre_prior = args.archetype_legendre_prior
+                printstr = 'a'
             if comm_rank == 0 and args.archetypes is not None:
-                print('archetype_legendre_prior = %s has been provided, so a prior will be added while solving for the coefficients'%(archetype_legendre_prior))
+                if archetype_legendre_prior is None:
+                    print('A zero or negative prior is provided so setting archetype_legendre_prior = None')
+                print('archetype_legendre_prior = %s has been provided, so %s prior will be added while solving for the coefficients'%(archetype_legendre_prior, printstr))
             if args.archetype_nnearest is not None:
                 if comm_rank == 0 and args.archetypes is not None:
                     print('nearest neighbour = %d is provided, will do the final fitting for N-best nearest archetypes in chi2 space..'%(args.archetype_nnearest))
@@ -912,13 +1000,14 @@ def rrdesi(options=None, comm=None):
         # refinement.  This function only returns data on the rank 0 process.
 
         start = elapsed(None, "", comm=comm)
-        
+
         scandata, zfit = zfind(targets, dtemplates, mpprocs,
-            nminima=nminima, archetypes=args.archetypes,
+            nminima=nminima, archetypes=archetypes,
             priors=args.priors, chi2_scan=args.chi2_scan, use_gpu=use_gpu, zminfit_npoints=args.zminfit_npoints, per_camera=archetype_legendre_percamera, deg_legendre=args.archetype_legendre_degree, n_nearest=args.archetype_nnearest, prior_sigma=archetype_legendre_prior, ncamera=ncamera)
 
         stop = elapsed(start, "Computing redshifts", comm=comm)
 
+        zbest = None
         # Set some DESI-specific ZWARN bits from input fibermap
         if comm_rank == 0:
             fiberstatus = targets.fibermap['COADD_FIBERSTATUS']
@@ -941,6 +1030,13 @@ def rrdesi(options=None, comm=None):
 
             ii = np.isin(zfit['targetid'], targetids[badpos | broken | unassigned | bad])
             zfit['zwarn'][ii] |= ZWarningMask.NODATA
+            # ADM set NODATA cases to z=0, GALAXY with zero coefficients.
+            zfit['spectype'][ii] = 'GALAXY'
+            zfit['subtype'][ii] = ''
+            zfit['coeff'][ii] = 0.
+            # ADM enforce 4-string in case we've only populated PCA/NMF.
+            zfit['fitmethod'] = zfit['fitmethod'].astype('U4')
+            zfit['fitmethod'][ii] = 'NONE'
 
             ii = np.isin(zfit['targetid'], targetids[broken])
             zfit['zwarn'][ii] |= ZWarningMask.UNPLUGGED
@@ -951,6 +1047,18 @@ def rrdesi(options=None, comm=None):
             ii = np.isin(zfit['targetid'], targetids[badcoverage])
             zfit['zwarn'][ii] |= ZWarningMask.LITTLE_COVERAGE
 
+        # gather templates and archetypes for final I/O header metadata
+        templates = dict()
+        for dt in dtemplates:
+            spectype = dt.template.template_type
+            subtype = dt.template.sub_type
+            templates[(spectype,subtype)] = dt.template
+
+        archetypes = None
+        if not args.archetypes is None:
+            archetypes = All_archetypes(archetypes_dir=args.archetypes,
+                                        verbose=(comm_rank==0)).archetypes
+
         # Write the outputs
 
         if args.details is not None:
@@ -958,7 +1066,7 @@ def rrdesi(options=None, comm=None):
             if comm_rank == 0:
                 write_zscan(args.details, scandata, zfit, clobber=True)
             stop = elapsed(start, "Writing zscan data took", comm=comm)
-
+        
         if args.outfile:
             start = elapsed(None, "", comm=comm)
             if comm_rank == 0:
@@ -966,27 +1074,67 @@ def rrdesi(options=None, comm=None):
 
                 # Remove extra columns not needed for zbest
                 zbest.remove_columns(['zz', 'zzchi2', 'znum'])
-
-                # Change to upper case like DESI
+                
+                # Change to upper case like SDSS / DESI
                 for colname in zbest.colnames:
                     if colname.islower():
                         zbest.rename_column(colname, colname.upper())
 
-                template_version = {t._template.full_type:t._template._version for t in dtemplates}
-                archetype_version = None
-                if not args.archetypes is None:
-                    archetypes = All_archetypes(archetypes_dir=args.archetypes,
-                                                verbose=(comm_rank==0)).archetypes
-                    archetype_version = {name:arch._version for name, arch in archetypes.items() }
+                # Allow 4 char for ARCH (vs. PCA/NMF) even if archetypes aren't used
+                zbest['FITMETHOD'] = zbest['FITMETHOD'].astype('U4')
 
                 write_zbest(args.outfile, zbest,
                         targets.fibermap, targets.exp_fibermap,
                         targets.tsnr2,
-                        template_version, archetype_version,
+                        templates, archetypes=archetypes,
                         spec_header=targets.header0)
+            
+            if comm is not None:
+                zbest = comm.bcast(zbest, root=0)
 
             stop = elapsed(start, f"Writing {args.outfile} took", comm=comm)
 
+        #- Evaluate models
+        if args.model is not None:
+
+            #- Evaluate best-fit models for local targets,
+            #- then collect into all_models on rank 0
+            local_models = targets.eval_models(zbest, templates, archetypes)
+            local_targetids = targets.local_target_ids()
+            if targets.comm is not None:
+                all_models = targets.comm.gather(local_models, root=0)
+                all_targetids = targets.comm.gather(local_targetids, root=0)
+            else:
+                all_models = [local_models,]
+                all_targetids = [local_targetids,]
+
+            stop = elapsed(start, f"Evaluating best fit models took", comm=comm)
+            if comm_rank == 0:
+                #- collapse list of lists of dictionaries -> single list of dictionaries
+                all_models = list(itertools.chain.from_iterable(all_models))
+
+                #- find sort orrder to match zbest
+                all_targetids = np.concatenate(all_targetids)
+                xsorted = np.argsort(all_targetids)
+                ypos = np.searchsorted(all_targetids[xsorted], zbest['TARGETID'])
+                sort_indices = xsorted[ypos]
+
+                #- double check sorting
+                all_targetids = np.array(all_targetids)[sort_indices]
+                np.testing.assert_array_equal(zbest['TARGETID'].data, all_targetids)
+
+                #- convert list[targets] of dict[bands] into dict[band] of sorted array[targets]
+                #- for DESI, the wavegrid keys = wavehashes = camera bands b/r/z
+                all_models_dict = dict()
+                wave_dict = targets.wavegrids()
+                for band in wave_dict:
+                    all_models_dict[band] = np.vstack([m[band] for m in all_models])[sort_indices]
+
+                write_bestmodel(args.model, zbest, all_models_dict, wave_dict,
+                                templates, archetypes, spec_header=targets.header0)
+
+            stop = elapsed(start, f"Writing {args.model} took", comm=comm)
+
     except Exception as err:
         exc_type, exc_value, exc_traceback = sys.exc_info()
         lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
diff --git a/py/redrock/fitz.py b/py/redrock/fitz.py
index da1a9fb7..6a10d497 100644
--- a/py/redrock/fitz.py
+++ b/py/redrock/fitz.py
@@ -9,13 +9,13 @@
 
 import numpy as np
 import scipy.constants
-import scipy.special
 
 from . import constants
 
 from .rebin import rebin_template
 
 from .zscan import calc_zchi2_one, spectral_data, calc_zchi2_batch
+from .zscan import calc_negOII_penalty
 
 from .zwarning import ZWarningMask as ZW
 
@@ -106,8 +106,6 @@ def minfit(x, y):
 
     return (x0, xerr, y0, zwarn)
 
-
-
 def legendre_calculate(nleg, dwave):
     wave = np.concatenate([ w for w in dwave.values() ])
     wmin = wave.min()
@@ -116,7 +114,8 @@ def legendre_calculate(nleg, dwave):
 
     return legendre
 
-def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=False, deg_legendre=None, zminfit_npoints=15, per_camera=False, n_nearest=None, prior_sigma=None):
+def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=False, deg_legendre=None,
+         zminfit_npoints=15, per_camera=False, n_nearest=None, prior_sigma=None):
     """Refines redshift measurement around up to nminima minima.
 
     TODO:
@@ -154,7 +153,7 @@ def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=
     dwave = { s.wavehash:s.wave for s in spectra }
 
     (weights, flux, wflux) = spectral_data(spectra)
-    
+
     if (use_gpu):
         #Get CuPy arrays of weights, flux, wflux
         #These are created on the first call of gpu_spectral_data() for a
@@ -175,15 +174,20 @@ def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=
     else:
         nz = zminfit_npoints
 
+    if template.template_type == 'STAR':
+        max_velo_diff = constants.max_velo_diff_star
+    else:
+        max_velo_diff = constants.max_velo_diff
+
     for imin in find_minima(zchi2):
         if len(results) == nminima:
             break
 
-        #- Skip this minimum if it is within constants.max_velo_diff km/s of a
+        #- Skip this minimum if it is within max_velo_diff km/s of a
         # previous one dv is in km/s
         zprev = np.array([tmp['z'] for tmp in results])
         dv = get_dv(z=redshifts[imin],zref=zprev)
-        if np.any(np.abs(dv) < constants.max_velo_diff):
+        if np.any(np.abs(dv) < max_velo_diff):
             continue
 
         #- Sample more finely around the minimum
@@ -232,6 +236,10 @@ def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=
                                                  solve_matrices_algorithm=template.solve_matrices_algorithm,
                                                  use_gpu=use_gpu)
 
+        #- Penalize chi2 for negative [OII] flux; ad-hoc
+        if hasattr(template, 'OIItemplate'):
+            zzchi2 += calc_negOII_penalty(template.OIItemplate, zzcoeff)
+
         #- fit parabola to 3 points around minimum
         i = min(max(np.argmin(zzchi2),1), len(zz)-2)
         zmin, sigma, chi2min, zwarn = minfit(zz[i-1:i+2], zzchi2[i-1:i+2])
@@ -296,16 +304,16 @@ def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=
             zwarn |= ZW.Z_FITLIMIT
 
         #- Skip this better defined minimum if it is within
-        #- constants.max_velo_diff km/s of a previous one
+        #- max_velo_diff km/s of a previous one
         zprev = np.array([tmp['z'] for tmp in results])
         dv = get_dv(z=zbest, zref=zprev)
-        if np.any(np.abs(dv) < constants.max_velo_diff):
+        if np.any(np.abs(dv) < max_velo_diff):
             continue
 
         if archetype is None:
             results.append(dict(z=zbest, zerr=zerr, zwarn=zwarn,
                 chi2=chi2min, zz=zz, zzchi2=zzchi2,
-                coeff=coeff))
+                coeff=coeff, fitmethod=template.method))
         else:
             if prior_sigma is not None:
                 if per_camera:
@@ -320,7 +328,7 @@ def fitz(zchi2, redshifts, target, template, nminima=3, archetype=None, use_gpu=
 
             results.append(dict(z=zbest, zerr=zerr, zwarn=zwarn,
                 chi2=chi2min, zz=zz, zzchi2=zzchi2,
-                coeff=coeff, fulltype=fulltype))
+                coeff=coeff, fulltype=fulltype, fitmethod=archetype.method))
 
     #- Sort results by chi2min; detailed fits may have changed order
     ii = np.argsort([tmp['chi2'] for tmp in results])
diff --git a/py/redrock/plotspec.py b/py/redrock/plotspec.py
index a65e72b0..58286df0 100644
--- a/py/redrock/plotspec.py
+++ b/py/redrock/plotspec.py
@@ -9,6 +9,7 @@
 
 from . import zwarning
 
+from .templates import make_fulltype
 
 class PlotSpec(object):
     def __init__(self, targets, templates, zscan, zfit, truth=None, archetypes=False):
@@ -104,9 +105,7 @@ def plot(self, keepzoom=False):
         zz = zfit[zfit['znum'] == self.znum][0]
         coeff = zz['coeff']
 
-        fulltype = zz['spectype']
-        if zz['subtype'] != '':
-            fulltype = fulltype+':::'+zz['subtype']
+        fulltype = make_fulltype(zz['spectype'], zz['subtype'])
         if self.archetypes:
             dwave = { s.wavehash:s.wave for s in target.spectra }
             tp = self.archetypes.archetypes[zz['spectype']]
diff --git a/py/redrock/results.py b/py/redrock/results.py
index b512ef26..2ce0ccd9 100644
--- a/py/redrock/results.py
+++ b/py/redrock/results.py
@@ -13,7 +13,7 @@
 from astropy.table import Table
 
 from .utils import encode_column
-
+from .templates import parse_fulltype
 
 def write_zscan(filename, zscan, zfit, clobber=False):
     """Writes redrock.zfind results to a file.
@@ -47,8 +47,10 @@ def write_zscan(filename, zscan, zfit, clobber=False):
     zfit = zfit.copy()
 
     #- convert unicode to byte strings
-    zfit.replace_column('spectype', np.char.encode(zfit['spectype'], 'ascii'))
-    zfit.replace_column('subtype', np.char.encode(zfit['subtype'], 'ascii'))
+    for colname in ('spectype', 'subtype', 'fitmethod'):
+        if colname in zfit.columns:
+            if zfit[colname].dtype.kind == 'U':
+                zfit.replace_column(colname, np.char.encode(zfit[colname], 'ascii'))
 
     zbest = zfit[zfit['znum'] == 0]
     zbest.remove_column('znum')
@@ -94,25 +96,23 @@ def read_zscan(filename, select_targetids=None):
         select_targetids (list): array of TARGETIDs to read
 
     Returns:
-        tuple: (zbest, results) where zbest is a Table with keys TARGETID, Z,
-            ZERR, ZWARN and results is a nested dictionary
-            results[targetid][templatetype] with keys:
-
-                - z: array of redshifts scanned
-                - zchi2: array of chi2 fit at each z
+        tuple: ``(zscan, zfit)`` where
+        ``zfit`` is a Table with the N best fits for each target
+        per spectype and subtype; and
+        ``zscan`` is a nested dictionary ``zscan[targetid][templatetype]``
+        with keys:
+
+                - redshifts: array of redshifts scanned
+                - zchi2: array of chi2 of fit vs. redshifts
                 - penalty: array of chi2 penalties for unphysical fits at each z
-                - zbest: best fit redshift (finer resolution fit around zchi2
-                    min)
-                - minchi2: chi2 at zbest
-                - zerr: uncertainty on zbest
-                - zwarn: 0=good, non-0 is a warning flag
-
+                - zcoeff: array of coefficients fit per redshifts
+                - zfit: table of N best-fit redshifts for just this target and templatetype
     """
     import h5py
     # zbest = Table.read(filename, format='hdf5', path='zbest')
     with h5py.File(os.path.expandvars(filename), mode='r') as fx:
         targetids = fx['targetids'][()]  # .value
-        spectypes = list(fx['zscan'].keys())
+        fulltypes = list(fx['zscan'].keys())
 
         if select_targetids is not None:
             indx = np.where(np.isin(targetids, select_targetids))[0]
@@ -122,29 +122,31 @@ def read_zscan(filename, select_targetids=None):
         zscan = dict()
         for targetid in targetids[indx]:
             zscan[targetid] = dict()
-            for spectype in spectypes:
-                zscan[targetid][spectype] = dict()
+            for fulltype in fulltypes:
+                zscan[targetid][fulltype] = dict()
 
-        for spectype in spectypes:
+        for fulltype in fulltypes:
+            spectype, subtype = parse_fulltype(fulltype)
             # blat[()] is obtuse syntax for what used to be clear blat.value
-            zchi2 = fx['/zscan/{}/zchi2'.format(spectype)][()]
-            penalty = fx['/zscan/{}/penalty'.format(spectype)][()]
-            zcoeff = fx['/zscan/{}/zcoeff'.format(spectype)][()]
-            redshifts = fx['/zscan/{}/redshifts'.format(spectype)][()]
+            zchi2 = fx['/zscan/{}/zchi2'.format(fulltype)][()]
+            penalty = fx['/zscan/{}/penalty'.format(fulltype)][()]
+            zcoeff = fx['/zscan/{}/zcoeff'.format(fulltype)][()]
+            redshifts = fx['/zscan/{}/redshifts'.format(fulltype)][()]
             for i, targetid in zip(indx, targetids[indx]):
-                zscan[targetid][spectype]['redshifts'] = redshifts
-                zscan[targetid][spectype]['zchi2'] = zchi2[i]
-                zscan[targetid][spectype]['penalty'] = penalty[i]
-                zscan[targetid][spectype]['zcoeff'] = zcoeff[i]
+                zscan[targetid][fulltype]['redshifts'] = redshifts
+                zscan[targetid][fulltype]['zchi2'] = zchi2[i]
+                zscan[targetid][fulltype]['penalty'] = penalty[i]
+                zscan[targetid][fulltype]['zcoeff'] = zcoeff[i]
                 thiszfit = fx['/zfit/{}/zfit'.format(targetid)][()]
                 ii = (thiszfit['spectype'].astype('U') == spectype)
+                ii &= (thiszfit['subtype'].astype('U') == subtype)
                 thiszfit = Table(thiszfit[ii])
                 thiszfit.remove_columns(['targetid', 'znum', 'deltachi2'])
                 thiszfit.replace_column('spectype',
                     encode_column(thiszfit['spectype']))
                 thiszfit.replace_column('subtype',
                     encode_column(thiszfit['subtype']))
-                zscan[targetid][spectype]['zfit'] = thiszfit
+                zscan[targetid][fulltype]['zfit'] = thiszfit
 
         zfit = [fx['zfit/{}/zfit'.format(tid)][()] for tid in targetids[indx]]
         zfit = Table(np.hstack(zfit))
diff --git a/py/redrock/targets.py b/py/redrock/targets.py
index 5b06a860..a08be80d 100644
--- a/py/redrock/targets.py
+++ b/py/redrock/targets.py
@@ -11,7 +11,7 @@
 import numpy as np
 import scipy.sparse
 
-from .utils import mp_array, distribute_work
+from .utils import mp_array, distribute_work, reduced_wavelength
 
 from . import constants
 
@@ -25,10 +25,11 @@ class Spectrum(object):
         R (scipy.sparse.dia_matrix): the resolution matrix in band diagonal
             format.
         Rcsr (scipy.sparse.csr_matrix): the resolution matrix in CSR format.
+        band (str): camera band name
 
     """
     # @profile
-    def __init__(self, wave, flux, ivar, R, Rcsr=None):
+    def __init__(self, wave, flux, ivar, R, Rcsr=None, band=None):
         if R is not None:
             w = np.asarray(R.sum(axis=1))[:,0]<constants.min_resolution_integral
             ivar[w] = 0.
@@ -39,10 +40,17 @@ def __init__(self, wave, flux, ivar, R, Rcsr=None):
         self.R = R
         self._Rcsr = Rcsr
         self._mpshared = False
-        if hasattr(R,'data'):
-            self.wavehash = hash((len(wave), wave[0], wave[1], wave[-2], wave[-1], R.data.shape[0]))
+
+        # "wavehash" tracks the different wavelength grids for different spectra
+        # if "band" is provided, use that, otherwise hash elements of the wave array
+        self.band = band
+        if band is not None:
+            self.wavehash = band
         else:
-            self.wavehash = hash((len(wave), wave[0], wave[1], wave[-2], wave[-1]))
+            if hasattr(R,'data'):
+                self.wavehash = hash((len(wave), wave[0], wave[1], wave[-2], wave[-1], R.data.shape[0]))
+            else:
+                self.wavehash = hash((len(wave), wave[0], wave[1], wave[-2], wave[-1]))
 
         #It is much more efficient to calculate edges once if rebinning
         #and copy to GPU and store here rather than doing this every time
@@ -75,6 +83,7 @@ def gpuedges(self):
 
     @property
     def Rcsr(self):
+        self.sharedmem_unpack()
         if self._Rcsr is None:
             self._Rcsr = self.R.tocsr()
         return self._Rcsr
@@ -168,6 +177,62 @@ def __init__(self, targetid, spectra, coadd=False, cosmics_nsig=0., meta=None):
         self._legendre = None
         self._gpulegendre = None
         self.nleg = 0
+        self.bands = []
+        for s in spectra:
+            self.bands.append(s.band)
+
+    def eval_model(self, bestfit, templates, archetypes=None, nleg=None):
+        """
+        Evaluate the best fit model for the spectra in this Target.
+
+        Args:
+            bestfit: dict-like with SPECTYPE, SUBTYPE, COEFF, and Z
+            templates: dict[(SPECTYPE,SUBTYPE)] of Template objects
+
+        Options:
+            archetypes: dict[(SPECTYPE,SUBTYPE)] of Archetype objects
+
+        Returns ``model`` dict of rendered best fit models keyed by wavehash
+
+        Note: modifies self by setting self.model to be returned value.
+
+        Example after calling target.eval_model::
+
+            spec = target.spectra[i]
+            wavelengths = spec.wave
+            fluxdata = spec.flux
+            wavehash = spec.wavehash
+            fluxmodel = target.model[wavehash]
+        """
+        spectype = bestfit['SPECTYPE']
+        subtype = bestfit['SUBTYPE']
+        coeff = bestfit['COEFF']
+        z = bestfit['Z']
+        fitmethod = bestfit['FITMETHOD']
+
+        self.model = dict()
+        if np.all(coeff == 0.0):
+            # entirely masked data has coeffs=0
+            for sp in self.spectra:
+                self.model[sp.wavehash] = np.zeros(len(sp.wave), dtype=np.float32)
+        elif fitmethod == 'ARCH':
+            #- Archetype fits have N nearest neighbor archetype coeffs
+            #- followed by ncamera*nlegendre coefficients
+            #- import here to avoid circular import
+            from .archetypes import split_archetype_coeff
+            archcoeff, legcoeff = split_archetype_coeff(subtype, coeff, len(self.bands))
+
+            ax = archetypes[spectype]
+            for i, sp in enumerate(self.spectra):
+                self.model[sp.wavehash] = ax.eval(subtype, archcoeff, sp.wave, z, R=sp.Rcsr, legcoeff=legcoeff[i])
+
+        else:
+            tx = templates[(spectype, subtype)]
+            for sp in self.spectra:
+                self.model[sp.wavehash] = tx.eval(coeff, sp.wave, z, sp.Rcsr)
+
+        return self.model
+
 
     def legendre(self, nleg, use_gpu=False):
         if (use_gpu and self.nleg == nleg):
@@ -180,10 +245,7 @@ def legendre(self, nleg, use_gpu=False):
         if (self._legendre is not None and self.nleg == nleg):
             return self._legendre
         dwave = { s.wavehash:s.wave for s in self.spectra }
-        wave = np.concatenate([ w for w in dwave.values() ])
-        wmin = wave.min()
-        wmax = wave.max()
-        self._legendre = { hs:np.array([scipy.special.legendre(i)( (w-wmin)/(wmax-wmin)*2.) for i in range(nleg)]) for hs, w in dwave.items() }
+        self._legendre = { hs:np.array([scipy.special.legendre(i)(reduced_wavelength(w)) for i in range(nleg)]) for hs, w in dwave.items() }
         self.nleg = nleg
         if (use_gpu):
             import cupy as cp
@@ -212,8 +274,14 @@ def compute_coadd(self, cache_Rcsr=False, cosmics_nsig=0.):
 
         This method REPLACES the list of individual spectra with coadds.
         """
+        # preserve order of wavehashes
+        wavehashes = list()
+        for sp in self.spectra:
+            if sp.wavehash not in wavehashes:
+                wavehashes.append(sp.wavehash)
+
         coadd = list()
-        for key in set([s.wavehash for s in self.spectra]):
+        for key in wavehashes:
             wave = None
             unweightedflux = None
             weightedflux = None
@@ -228,6 +296,7 @@ def compute_coadd(self, cache_Rcsr=False, cosmics_nsig=0.):
             for s in self.spectra:
                 if s.wavehash != key: continue
                 nspec += 1
+                current_band = s.band
                 if wave is None :
                     wave = s.wave
                     Rdiags = s.R.data * s.ivar
@@ -296,7 +365,7 @@ def compute_coadd(self, cache_Rcsr=False, cosmics_nsig=0.):
             else:
                 Rcsr = None
 
-            spc = Spectrum(wave, flux, weights, R, Rcsr)
+            spc = Spectrum(wave, flux, weights, R, Rcsr, band=current_band)
             coadd.append(spc)
 
         # swap the coadds into place.
@@ -370,6 +439,31 @@ def local(self):
         """
         return self._local_data()
 
+    def eval_models(self, bestfit, templates, archetypes=None):
+        """
+        Calls target.eval_model(bestfit, templates, archetypes) for each local target
+
+        Args:
+            bestfit: dict-like with SPECTYPE, SUBTYPE, COEFF, and Z
+            templates: dict[(SPECTYPE,SUBTYPE)] of Template objects
+
+        Options:
+            archetypes: dict[(SPECTYPE,SUBTYPE)] of Archetype objects
+
+        Returns ``models`` list of dictionaries, with one item per local target,
+            and dictionaries keyed by the wavehashes of the spectra for that target.
+
+        See Target.eval_model for more details.
+
+        Note: sets target.model for each local target
+        """
+        models = list()
+        for tgt in self.local():
+            i = np.where(bestfit['TARGETID'] == tgt.id)[0][0]
+            model = tgt.eval_model(bestfit[i], templates, archetypes)
+            models.append(model)
+
+        return models
 
     def wavegrids(self):
         """Return the global dictionary of wavelength grids for each wave hash.
diff --git a/py/redrock/templates.py b/py/redrock/templates.py
index 6112370e..cc6d855c 100644
--- a/py/redrock/templates.py
+++ b/py/redrock/templates.py
@@ -14,10 +14,13 @@
 
 import numpy as np
 from astropy.io import fits
+from astropy.table import Table
+from fitsio import FITS
 
 from .utils import native_endian, elapsed
 from .igm import transmission_Lyman
 from .rebin import rebin_template, trapz_rebin
+from .zscan import spectral_data
 
 valid_template_methods = ('PCA', 'NMF')
 
@@ -35,8 +38,10 @@ class Template(object):
     def __init__(self, filename=None, spectype=None, redshifts=None,
                  wave=None, flux=None, subtype=None, method='PCA',
                  igm_model='Inoue14',
-                 zscan_galaxy=None, zscan_qso=None, zscan_star=None):
+                 zscan_galaxy=None, zscan_qso=None, zscan_star=None,
+                 version=None):
 
+        self._filename = filename
         if filename is not None:
             fx = None
             if os.path.exists(filename):
@@ -104,13 +109,13 @@ def __init__(self, filename=None, spectype=None, redshifts=None,
                         "template type {}".format(self._rrtype))
                 zmin = self._redshifts[0]
                 zmax = self._redshifts[-1]
-                print("DEBUG: Using default redshift range {:.4f}-{:.4f} for "
-                    "{}".format(zmin, zmax, os.path.basename(filename)))
+                # print("DEBUG: Using default redshift range {:.4f}-{:.4f} for "
+                #     "{}".format(zmin, zmax, os.path.basename(filename)))
             else:
                 zmin = self._redshifts[0]
                 zmax = self._redshifts[-1]
-                print("DEBUG: Using redshift range {:.4f}-{:.4f} for "
-                    "{}".format(zmin, zmax, os.path.basename(filename)))
+                # print("DEBUG: Using redshift range {:.4f}-{:.4f} for "
+                #     "{}".format(zmin, zmax, os.path.basename(filename)))
 
             self._subtype = None
             if 'RRSUBTYP' in hdr:
@@ -143,6 +148,7 @@ def __init__(self, filename=None, spectype=None, redshifts=None,
             self._subtype = subtype
             self._igm_model = igm_model
             self._method = method
+            self._version = version
 
         self._nbasis = self.flux.shape[0]
         self._nwave = self.flux.shape[1]
@@ -158,9 +164,25 @@ def __init__(self, filename=None, spectype=None, redshifts=None,
             raise ValueError(f'Template method {self._method} unrecognized; '
                               f'should be one of {valid_template_methods}')
 
-        print(f'INFO: {self.full_type} templates using {self._method} for fitting')
-        print(f'INFO: {self.full_type} templates using {self._igm_model} IGM model')
+        # Special case for GALAXY templates and PCA fitting, which can include
+        # negative templates or coefficients: Cache a template that covers just
+        # [OII].
+        if self._rrtype == 'GALAXY' and self._method == "PCA":
+            isOII = (3724 <= self.wave) & (self.wave <= 3733)
+            self.OIItemplate = self.flux[:,isOII]
 
+        if filename is not None:
+            print(f'INFO: {os.path.basename(filename)} {self}')
+        else:
+            print(f'INFO: {self}')
+
+    def __str__(self):
+        zmin = round(self._redshifts[0], 4)
+        zmax = round(self._redshifts[-1], 4)
+        return f'{self._rrtype} {self._subtype} {self._method} IGM={self._igm_model} z={zmin}-{zmax}'
+
+    def __repr__(self):
+        return str(self)
 
     @property
     def nbasis(self):
@@ -180,12 +202,17 @@ def sub_type(self):
 
     @property
     def full_type(self):
-        """Return formatted type:subtype string.
+        """Return formatted fulltype = spectype:::subtype string.
         """
-        if self._subtype != '':
-            return '{}:::{}'.format(self._rrtype, self._subtype)
-        else:
-            return self._rrtype
+        return make_fulltype(self._rrtype, self._subtype)
+
+    @property
+    def version(self):
+        return self._version
+
+    @property
+    def filename(self):
+        return self._filename
 
     @property
     def redshifts(self):
@@ -203,12 +230,19 @@ def solve_matrices_algorithm(self):
         """
         return self._method
 
+    @property
+    def method(self):
+        """
+        Alias for self.solve_matrices_algorithm, i.e. PCA or NMF
+        """
+        return self._method
+
     @property
     def igm_model(self):
         return self._igm_model
 
 
-    def eval(self, coeff, wave, z):
+    def eval(self, coeff, wave, z, R=None):
         """Return template for given coefficients, wavelengths, and redshift
 
         Args:
@@ -216,18 +250,50 @@ def eval(self, coeff, wave, z):
             wave : wavelengths at which to evaluate template flux
             z : redshift at which to evaluate template flux
 
+        Options:
+            R : array[nwave,nwave] resolution matrix to convolve with model
+
         Returns:
             template flux array
 
         Notes:
-            A single factor of (1+z)^-1 is applied to the resampled flux
-            to conserve integrated flux after redshifting.
-
+            No factors of (1+z) are applied to the resampled flux, i.e.
+            evaluating the same coeffs at different z does not conserve
+            integrated flux, but more directly maps model=templates.dot(coeff)
         """
-        assert len(coeff) == self.nbasis
-        flux = self.flux.T.dot(coeff).T / (1+z)
-        return trapz_rebin(self.wave*(1+z), flux, wave)
+        coeff = coeff[0:self.nbasis]
+        flux = self.flux.T.dot(coeff).T
+        model = trapz_rebin(self.wave*(1+z), flux, wave)
+        if R is not None:
+            model = R.dot(model)
 
+        return model
+
+def parse_fulltype(fulltype):
+    """Parse template fulltype into (spectype, subtype)
+    """
+    fulltype = fulltype.strip()
+    if ':::' in fulltype:
+        spectype, subtype = fulltype.split(':::')
+    else:
+        spectype = fulltype
+        subtype = ''
+
+    return spectype, subtype
+
+def make_fulltype(spectype, subtype):
+    """combine (spectype, subtype) into fulltype
+    """
+    spectype = spectype.strip()
+    if subtype is not None:
+        subtype = subtype.strip()
+
+    if subtype is not None and subtype != '':
+        fulltype = f'{spectype}:::{subtype}'
+    else:
+        fulltype = spectype
+
+    return fulltype
 
 def find_templates(template_path=None):
     """Return list of Redrock template files
@@ -254,13 +320,14 @@ def find_templates(template_path=None):
         print(f'DEBUG: Reading templates from {template_path}')
 
     if os.path.isdir(template_path):
-        default_templates_file = f'{template_path}/default_templates.txt'
+        default_templates_file = f'{template_path}/templates-default.txt'
         template_dir = template_path
+
     elif template_path.endswith('.txt'):
         if not os.path.exists(template_path):
             raise ValueError(f'Missing {template_path=}')
         default_templates_file = template_path
-        template_dir = os.path.dirname(template_path)
+        template_dir = os.path.dirname(os.path.abspath(template_path))
     elif template_path.endswith( ('.fits', '.fits.gz', '.fits.fz') ):
         #- single template file, return that as a list
         return [template_path,]
@@ -293,18 +360,84 @@ def find_templates(template_path=None):
     return template_files
 
 
-def load_templates(template_path=None):
+def header2templatefiles(hdr, template_dir=None):
+    """Derive template filenames from header keywords
+
+    Args:
+        hdr: dict-like with keys TEMNAMnn+TEMVERnn and/or TEMFILnn
+
+    Options:
+        template_dir (str): full path to redrock-templates directory
+
+    Returns list of template filenames, including directory path
+    """
+    filenames = list()
+    for i in range(100):
+        filekey = f'TEMFIL{i:02d}'
+        typekey = f'TEMNAM{i:02d}'
+        verkey = f'TEMVER{i:02d}'
+        if filekey in hdr:
+            filenames.append( hdr[filekey] )
+        elif (typekey in hdr) and (verkey in hdr):
+            version = hdr[verkey].strip()
+            spectype, subtype = parse_fulltype(hdr[typekey])
+
+            #- special case unversioned STAR templates
+            if version=='unknown':
+                if spectype == 'STAR':
+                    version = '0.1'
+                else:
+                    raise ValueError(f'unknown version for {spectype=}')
+
+            #- blank subtype -> None for filename purposes
+            if subtype == '':
+                subtype = None
+
+            filenames.append(f'rrtemplate-{spectype}-{subtype}-v{version}.fits')
+
+    #- preprend template directory
+    if template_dir is None:
+        template_dir = os.environ['RR_TEMPLATE_DIR']
+
+    filenames = [os.path.join(template_dir, fn) for fn in filenames]
+
+    return filenames
+
+
+def load_templates_from_header(hdr, template_dir=None):
+    """Return templates matching header keywords
+
+    Args:
+        hdr: dict-like with keys TEMNAMnn+TEMVERnn and/or TEMFILnn
+
+    Options:
+        template_dir (str): full path to redrock-templates directory
+
+    Returns list of Template objects
+    """
+    template_filenames = header2templatefiles(hdr, template_dir=template_dir)
+    return load_templates(template_filenames)
+
+def load_templates(template_path=None, zscan_galaxy=None, zscan_star=None, zscan_qso=None,
+                   asdict=False):
     """
-    Return list of Template objects
+    Return list or dict of Template objects
+
+    Options:
+        template_path: list of template files, directory, or text file with list of templates
+        zscan_galaxy (str): zmin,zmax,dz redshift range for galaxies
+        zscan_star (str): zmin,zmax,dz redshift range for stars
+        zscan_qso (str): zmin,zmax,dz redshift range for QSOs
+        asdict (bool): return dict keyed by (spectype, subtype) instead of list
 
     `template_path` is list of template file paths, or path to provide to
     find_templates, i.e. a path to a directory with templates, a path to
     a text file containing a list of templates, a path to a single template
     file, or None to use $RR_TEMPLATE_DIR instead.
 
-    Returns: list of Template objects
+    Returns: list or dict of Template objects
 
-    Note: this always returns a list, even if template_path is a path to a
+    Note: this always returns a list/dict, even if template_path is a path to a
     single template file.
     """
     if isinstance(template_path, (str, type(None))):
@@ -314,9 +447,18 @@ def load_templates(template_path=None):
 
     templates = list()
     for filename in template_path:
-        templates.append(Template(filename))
+        tx = Template(filename, zscan_galaxy=zscan_galaxy,
+                      zscan_star=zscan_star, zscan_qso=zscan_qso)
+        templates.append(tx)
 
-    return templates
+    if asdict:
+        templates_dict = dict()
+        for tx in templates:
+            templates_dict[(tx.template_type, tx.sub_type)] = tx
+
+        return templates_dict
+    else:
+        return templates
 
 
 class DistTemplatePiece(object):
@@ -583,30 +725,22 @@ def load_dist_templates(dwave, templates=None, comm=None, mp_procs=1,
             after distributed rebinning so each process has the full
             redshift range for the template.
 
-    Returns:
-        list: a list of DistTemplate objects.
-
+    Returns dist_templates: a list of DistTemplate objects, sampled at redshifts
     """
     timer = elapsed(None, "", comm=comm)
 
-    template_files = None
-    if (comm is None) or (comm.rank == 0):
-        template_files = find_templates(templates)
-
-    if comm is not None:
-        template_files = comm.bcast(template_files, root=0)
-
-    template_data = list()
+    template_dict = None
     if (comm is None) or (comm.rank == 0):
-        for t in template_files:
-            template_data.append(Template(filename=t, zscan_galaxy=zscan_galaxy,
-                                          zscan_star=zscan_star, zscan_qso=zscan_qso))
+        template_dict = load_templates(templates, asdict=True,
+                                       zscan_galaxy=zscan_galaxy,
+                                       zscan_star=zscan_star,
+                                       zscan_qso=zscan_qso)
 
     if comm is not None:
-        template_data = comm.bcast(template_data, root=0)
+        template_dict = comm.bcast(template_dict, root=0)
 
     timer = elapsed(timer, "Read and broadcast of {} templates"\
-        .format(len(template_files)), comm=comm)
+        .format(len(template_dict)), comm=comm)
 
     if (use_gpu):
         import cupy as cp
@@ -618,7 +752,7 @@ def load_dist_templates(dwave, templates=None, comm=None, mp_procs=1,
     # Compute the interpolated templates in a distributed way with every
     # process generating a slice of the redshift range.
     dtemplates = list()
-    for t in template_data:
+    for t in template_dict.values():
         if redistribute:
             dtemplate = ReDistTemplate(t, dwave, mp_procs=mp_procs, comm=comm, use_gpu=use_gpu, gpu_mode=gpu_mode)
         else:
@@ -656,23 +790,30 @@ def eval_model(data, wave, R=None, templates=None):
         a dictionary of model fluxes, one for each camera.
     """
     if templates is None:
-        templates = dict()
-        templatefn = find_templates()
-        for fn in templatefn:
-            tx = Template(fn)
-            templates[(tx.template_type, tx.sub_type)] = tx
+        templates = load_templates(asdict=True)
+
+    #- if wave is a dictionary of wavelength arrays, recursively call
+    #- eval_model to return dictionary of models
     if isinstance(wave, dict):
-        Rdict = R if R is not None else {x: None for x in wave}
-        return {x: eval_model(data, wave[x], R=Rdict[x], templates=templates)
-                for x in wave}
-    out = np.zeros((len(data), len(wave)), dtype='f4')
+        if R is None:
+            Rdict = {x: None for x in wave}
+        else:
+            if not isinstance(R, dict) or set(R.keys()) != set(wave.keys()):
+                raise ValueError('R must be dict with same keys as wave')
+            Rdict = R
+
+        out = dict()
+        for key in wave:
+            out[key] = eval_model(data, wave[key], R=Rdict[key], templates=templates)
+
+        return out
+
+    models = np.zeros((len(data), len(wave)), dtype='f4')
     for i in range(len(data)):
         tx = templates[(data['SPECTYPE'][i], data['SUBTYPE'][i])]
         coeff = data['COEFF'][i][0:tx.nbasis]
-        model = tx.flux.T.dot(coeff).T
-        mx = trapz_rebin(tx.wave*(1+data['Z'][i]), model, wave)
-        if R is None:
-            out[i] = mx
-        else:
-            out[i] = R[i].dot(mx)
-    return out
+        z = data['Z'][i]
+        models[i] = tx.eval(coeff, wave, z, R=R[i])
+
+    return models
+
diff --git a/py/redrock/test/data/README b/py/redrock/test/data/README
new file mode 100644
index 00000000..7c5ef372
--- /dev/null
+++ b/py/redrock/test/data/README
@@ -0,0 +1,12 @@
+Test data generated with a templates.txt file:
+```
+/global/common/software/desi/perlmutter/desiconda/20230111-2.1.0/code/redrock-templates/main/rrtemplate-GALAXY-None-v2.6.fits
+/global/common/software/desi/perlmutter/desiconda/20230111-2.1.0/code/redrock-templates/main/rrtemplate-QSO-HIZ-v1.1.fits
+```
+
+and the command:
+```
+rrdesi -n 3 -i $DESI_ROOT/spectro/redux/fuji/tiles/cumulative/100/20210505/coadd-0-100-thru20210505.fits \
+  -t templates.txt -o redrock-test.fits -d rrdetails-test.h5
+```
+
diff --git a/py/redrock/test/data/redrock-test.fits b/py/redrock/test/data/redrock-test.fits
new file mode 100644
index 00000000..c0469832
--- /dev/null
+++ b/py/redrock/test/data/redrock-test.fits
@@ -0,0 +1,10 @@
+SIMPLE  =                    T / conforms to FITS standard                      BITPIX  =                    8 / array data type                                NAXIS   =                    0 / number of array dimensions                     EXTEND  =                    T                                                  LONGSTRN= 'OGIP 1.0'                                                            RRVER   = '0.19.0.dev943'      / Redrock version                                TEMNAM00= 'GALAXY  '                                                            TEMVER00= '2.6     '                                                            TEMFIL00= 'rrtemplate-GALAXY-None-v2.6.fits'                                    TEMNAM01= 'QSO:::HIZ'                                                           TEMVER01= '1.1     '                                                            TEMFIL01= 'rrtemplate-QSO-HIZ-v1.1.fits'                                        DEPNAM00= 'python  '                                                            DEPVER00= '3.10.8  '                                                            DEPNAM01= 'numpy   '                                                            DEPVER01= '1.22.4  '                                                            DEPNAM02= 'scipy   '                                                            DEPVER02= '1.8.1   '                                                            DEPNAM03= 'astropy '                                                            DEPVER03= '5.2.1   '                                                            DEPNAM04= 'yaml    '                                                            DEPVER04= '6.0     '                                                            DEPNAM05= 'fitsio  '                                                            DEPVER05= '1.1.8   '                                                            DEPNAM06= 'h5py    '                                                            DEPVER06= '3.7.0   '                                                            DEPNAM07= 'desiutil'                                                            DEPVER07= '3.4.3.dev1103'                                                       DEPNAM08= 'desispec'                                                            DEPVER08= '0.61.0.dev8145'                                                      DEPNAM09= 'desitarget'                                                          DEPVER09= '2.7.0.dev5481'                                                       DEPNAM10= 'speclite'                                                            DEPVER10= ''                                                                    DEPNAM11= 'redrock '                                                            DEPVER11= '0.19.0.dev943'                                                       DEPNAM12= 'DESI_ROOT'                                                           DEPVER12= '/global/cfs/cdirs/desi'                                              DEPNAM13= 'DESI_SPECTRO_DATA'                                                   DEPVER13= '/global/cfs/cdirs/desi/spectro/data'                                 DEPNAM14= 'DESI_SPECTRO_REDUX'                                                  DEPVER14= '/global/cfs/cdirs/desi/spectro/redux'                                DEPNAM15= 'SPECPROD'                                                            DEPVER15= 'sjbailey'                                                            DEPNAM16= 'DESI_SPECTRO_CALIB'                                                  DEPVER16= '/global/cfs/cdirs/desi/spectro/desi_spectro_calib/trunk'             DEPNAM17= 'DESI_BASIS_TEMPLATES'                                                DEPVER17= '/global/cfs/cdirs/desi/spectro/templates/basis_templates/v3.2'       DEPNAM18= 'DESI_TARGET'                                                         DEPVER18= '/global/cfs/cdirs/desi/target'                                       DEPNAM19= 'DESIMODEL'                                                           DEPVER19= '/global/common/software/desi/perlmutter/desiconda/20230111-2.1.0/co&'CONTINUE  'de/desimodel/main'                                                   DEPNAM20= 'RR_TEMPLATE_DIR'                                                     DEPVER20= '/global/common/software/desi/perlmutter/desiconda/20230111-2.1.0/co&'CONTINUE  'de/redrock-templates/main'                                           SPGRP   = 'cumulative'                                                          SPGRPVAL=             20210505                                                  TILEID  =                  100                                                  SPECTRO =                    0                                                  PETAL   =                    0                                                  NIGHT   =             20210505                                                  END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / array data type                                NAXIS   =                    2 / number of array dimensions                     NAXIS1  =                  170 / length of dimension 1                          NAXIS2  =                    3 / length of dimension 2                          PCOUNT  =                    0 / number of group parameters                     GCOUNT  =                    1 / number of groups                               TFIELDS =                   11 / number of table fields                         TTYPE1  = 'TARGETID'                                                            TFORM1  = 'K       '                                                            TTYPE2  = 'Z       '                                                            TFORM2  = 'D       '                                                            TTYPE3  = 'ZERR    '                                                            TFORM3  = 'D       '                                                            TTYPE4  = 'ZWARN   '                                                            TFORM4  = 'K       '                                                            TTYPE5  = 'CHI2    '                                                            TFORM5  = 'D       '                                                            TTYPE6  = 'COEFF   '                                                            TFORM6  = '10D     '                                                            TDIM6   = '(10)    '                                                            TTYPE7  = 'NPIXELS '                                                            TFORM7  = 'K       '                                                            TTYPE8  = 'SPECTYPE'                                                            TFORM8  = '6A      '                                                            TTYPE9  = 'SUBTYPE '                                                            TFORM9  = '20A     '                                                            TTYPE10 = 'NCOEFF  '                                                            TFORM10 = 'K       '                                                            TTYPE11 = 'DELTACHI2'                                                           TFORM11 = 'D       '                                                            EXTNAME = 'REDSHIFTS'                                                           END                                                                                                                                                                                                                                                                                                                             ŒÎ\pÀß?õ@¤($xÂ?ð­tO€
@¼Ïm~€ÀA³”w(ã¹ÀldŠB„ùÀ†½ŒÇجÀpù$`߀@ƒÁvÓŸaÀt§6i	#,@;1OÄÀ˜ÀuуK gº@Sü8ù¿áôÀ17aöXøGALAXY
+@,;/„ŒÎ\qé?ÄÎ{w~?[pï¬-x@¾`oÃà@ŒÓcÌ¡;Û@„y_>Ú+’@Hÿ¿<c%„ÀNK'á‹ÀÄÍ›<@ûH‘ÂÀQZ(áq{ÀE?’xŸ¿Ñ«¢þPê@DE2áýöGALAXY
+@rù€™éÀŒÎ\qÄ¿Fä8È|>òtÃϧpÊ@ƵFÆ/w@¤2fs~K@œšú–xºÈ@rÕà¨×ÑÀ€±º[`ÙÀCíÃݬ°@QßAÀ>mÀuØ8½yÀ!#±"++?ï¶M{éj~Àdf	�jÊ
+ðGALAXY
+@½)"qü!XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / array data type                                NAXIS   =                    2 / number of array dimensions                     NAXIS1  =                  387 / length of dimension 1                          NAXIS2  =                    3 / length of dimension 2                          PCOUNT  =                    0 / number of group parameters                     GCOUNT  =                    1 / number of groups                               TFIELDS =                   81 / number of table fields                         TTYPE1  = 'TARGETID'           / Unique DESI target ID                          TFORM1  = 'K       '                                                            TTYPE2  = 'PETAL_LOC'          / Petal location [0-9]                           TFORM2  = 'I       '                                                            TTYPE3  = 'DEVICE_LOC'         / Device location on focal plane [0-523]         TFORM3  = 'J       '                                                            TTYPE4  = 'LOCATION'           / FP location PETAL_LOC*1000 + DEVICE_LOC        TFORM4  = 'K       '                                                            TTYPE5  = 'FIBER   '           / Fiber ID on the CCDs [0-4999]                  TFORM5  = 'J       '                                                            TTYPE6  = 'COADD_FIBERSTATUS'  / Bitwise-AND of input FIBERSTATUS               TFORM6  = 'J       '                                                            TTYPE7  = 'TARGET_RA'          / Barycentric right ascension in ICRS            TFORM7  = 'D       '                                                            TUNIT7  = 'deg     '           / TARGET_RA units                                TTYPE8  = 'TARGET_DEC'         / Barycentric declination in ICRS                TFORM8  = 'D       '                                                            TUNIT8  = 'deg     '           / TARGET_DEC units                               TTYPE9  = 'PMRA    '           / Proper motion in the +RA direction             TFORM9  = 'E       '                                                            TUNIT9  = 'mas yr^-1'          / PMRA units                                     TTYPE10 = 'PMDEC   '           / Proper motion in the +Dec direction            TFORM10 = 'E       '                                                            TUNIT10 = 'mas yr^-1'          / PMDEC units                                    TTYPE11 = 'REF_EPOCH'          / Reference epoch for Gaia/Tycho astrometry      TFORM11 = 'E       '                                                            TUNIT11 = 'yr      '           / REF_EPOCH units                                TTYPE12 = 'LAMBDA_REF'         / Wavelength at which fiber was centered         TFORM12 = 'E       '                                                            TUNIT12 = 'Angstrom'           / LAMBDA_REF units                               TTYPE13 = 'FA_TARGET'          / Fiberassign internal target bitmask            TFORM13 = 'K       '                                                            TTYPE14 = 'FA_TYPE '           / Fiberassign internal target type               TFORM14 = 'B       '                                                            TTYPE15 = 'OBJTYPE '           / Object type: TGT, SKY, NON, BAD                TFORM15 = '3A      '                                                            TTYPE16 = 'FIBERASSIGN_X'      / Expected CS5 X location on focal plane         TFORM16 = 'E       '                                                            TUNIT16 = 'mm      '           / FIBERASSIGN_X units                            TTYPE17 = 'FIBERASSIGN_Y'      / Expected CS5 Y location on focal plane         TFORM17 = 'E       '                                                            TUNIT17 = 'mm      '           / FIBERASSIGN_Y units                            TTYPE18 = 'PRIORITY'           / Target current priority                        TFORM18 = 'J       '                                                            TTYPE19 = 'SUBPRIORITY'        / Assignment subpriority [0-1)                   TFORM19 = 'D       '                                                            TTYPE20 = 'OBSCONDITIONS'      / Bitmask of allowed observing conditions        TFORM20 = 'J       '                                                            TTYPE21 = 'RELEASE '           / Imaging surveys release ID                     TFORM21 = 'I       '                                                            TTYPE22 = 'BRICKNAME'          / Brick name from tractor input                  TFORM22 = '8A      '                                                            TTYPE23 = 'BRICKID '           / Brick ID from tractor input                    TFORM23 = 'J       '                                                            TTYPE24 = 'BRICK_OBJID'        / Imaging Surveys OBJID on that brick            TFORM24 = 'J       '                                                            TTYPE25 = 'MORPHTYPE'          / Imaging Surveys morphological type             TFORM25 = '4A      '                                                            TTYPE26 = 'EBV     '           / Galactic extinction E(B-V) reddening           TFORM26 = 'E       '                                                            TUNIT26 = 'mag     '           / EBV units                                      TTYPE27 = 'FLUX_G  '           / Flux in the Legacy Survey g-band (AB)          TFORM27 = 'E       '                                                            TUNIT27 = 'nanomaggy'          / FLUX_G units                                   TTYPE28 = 'FLUX_R  '           / Flux in the Legacy Survey r-band (AB)          TFORM28 = 'E       '                                                            TUNIT28 = 'nanomaggy'          / FLUX_R units                                   TTYPE29 = 'FLUX_Z  '           / Flux in the Legacy Survey z-band (AB)          TFORM29 = 'E       '                                                            TUNIT29 = 'nanomaggy'          / FLUX_Z units                                   TTYPE30 = 'FLUX_W1 '           / WISE flux in W1 (AB)                           TFORM30 = 'E       '                                                            TUNIT30 = 'nanomaggy'          / FLUX_W1 units                                  TTYPE31 = 'FLUX_W2 '           / WISE flux in W2 (AB)                           TFORM31 = 'E       '                                                            TUNIT31 = 'nanomaggy'          / FLUX_W2 units                                  TTYPE32 = 'FLUX_IVAR_G'        / Inverse variance of FLUX_G (AB)                TFORM32 = 'E       '                                                            TUNIT32 = 'nanomaggy^-2'       / FLUX_IVAR_G units                              TTYPE33 = 'FLUX_IVAR_R'        / Inverse variance of FLUX_R (AB)                TFORM33 = 'E       '                                                            TUNIT33 = 'nanomaggy^-2'       / FLUX_IVAR_R units                              TTYPE34 = 'FLUX_IVAR_Z'        / Inverse variance of FLUX_Z (AB)                TFORM34 = 'E       '                                                            TUNIT34 = 'nanomaggy^-2'       / FLUX_IVAR_Z units                              TTYPE35 = 'FLUX_IVAR_W1'       / Inverse variance of FLUX_W1 (AB)               TFORM35 = 'E       '                                                            TUNIT35 = 'nanomaggy^-2'       / FLUX_IVAR_W1 units                             TTYPE36 = 'FLUX_IVAR_W2'       / Inverse variance of FLUX_W2 (AB)               TFORM36 = 'E       '                                                            TUNIT36 = 'nanomaggy^-2'       / FLUX_IVAR_W2 units                             TTYPE37 = 'FIBERFLUX_G'        / Predicted g-band flux in 1.5" fiber            TFORM37 = 'E       '                                                            TUNIT37 = 'nanomaggy'          / FIBERFLUX_G units                              TTYPE38 = 'FIBERFLUX_R'        / Predicted r-band flux in 1.5" fiber            TFORM38 = 'E       '                                                            TUNIT38 = 'nanomaggy'          / FIBERFLUX_R units                              TTYPE39 = 'FIBERFLUX_Z'        / Predicted z-band flux in 1.5" fiber            TFORM39 = 'E       '                                                            TUNIT39 = 'nanomaggy'          / FIBERFLUX_Z units                              TTYPE40 = 'FIBERTOTFLUX_G'     / g-band flux in 1.5" fiber from all sources     TFORM40 = 'E       '                                                            TUNIT40 = 'nanomaggy'          / FIBERTOTFLUX_G units                           TTYPE41 = 'FIBERTOTFLUX_R'     / r-band flux in 1.5" fiber from all sources     TFORM41 = 'E       '                                                            TUNIT41 = 'nanomaggy'          / FIBERTOTFLUX_R units                           TTYPE42 = 'FIBERTOTFLUX_Z'     / z-band flux in 1.5" fiber from all sources     TFORM42 = 'E       '                                                            TUNIT42 = 'nanomaggy'          / FIBERTOTFLUX_Z units                           TTYPE43 = 'MASKBITS'           / Bitmask for photometry issues                  TFORM43 = 'I       '                                                            TTYPE44 = 'SERSIC  '           / Power-law index for the Sersic profile model   TFORM44 = 'E       '                                                            TTYPE45 = 'SHAPE_R '           / Half-light radius of galaxy model (>0)         TFORM45 = 'E       '                                                            TUNIT45 = 'arcsec  '           / SHAPE_R units                                  TTYPE46 = 'SHAPE_E1'           / Ellipticity component 1 of galaxy model        TFORM46 = 'E       '                                                            TTYPE47 = 'SHAPE_E2'           / Ellipticity component 2 of galaxy model        TFORM47 = 'E       '                                                            TTYPE48 = 'REF_ID  '           / Astrometric cat refID (Gaia SOURCE_ID)         TFORM48 = 'K       '                                                            TTYPE49 = 'REF_CAT '           / Reference catalog source for star              TFORM49 = '2A      '                                                            TTYPE50 = 'GAIA_PHOT_G_MEAN_MAG' / Gaia G band magnitude                        TFORM50 = 'E       '                                                            TUNIT50 = 'mag     '           / GAIA_PHOT_G_MEAN_MAG units                     TTYPE51 = 'GAIA_PHOT_BP_MEAN_MAG' / Gaia BP band magnitude                      TFORM51 = 'E       '                                                            TUNIT51 = 'mag     '           / GAIA_PHOT_BP_MEAN_MAG units                    TTYPE52 = 'GAIA_PHOT_RP_MEAN_MAG' / Gaia RP band magnitude                      TFORM52 = 'E       '                                                            TUNIT52 = 'mag     '           / GAIA_PHOT_RP_MEAN_MAG units                    TTYPE53 = 'PARALLAX'           / Reference catalog parallax                     TFORM53 = 'E       '                                                            TUNIT53 = 'mas     '           / PARALLAX units                                 TTYPE54 = 'PHOTSYS '           / N for BASS/MzLS, S for DECam                   TFORM54 = '1A      '                                                            TTYPE55 = 'PRIORITY_INIT'      / Target initial priority                        TFORM55 = 'K       '                                                            TTYPE56 = 'NUMOBS_INIT'        / Initial number of observations for target      TFORM56 = 'K       '                                                            TTYPE57 = 'SV3_DESI_TARGET'    / DESI target selection bitmask for SV3          TFORM57 = 'K       '                                                            TTYPE58 = 'SV3_BGS_TARGET'     / BGS target selection bitmask for SV3           TFORM58 = 'K       '                                                            TTYPE59 = 'SV3_MWS_TARGET'     / MWS target selection bitmask for SV3           TFORM59 = 'K       '                                                            TTYPE60 = 'SV3_SCND_TARGET'    / Secondary target selection bitmask for SV3     TFORM60 = 'K       '                                                            TTYPE61 = 'DESI_TARGET'        / DESI target selection bitmask                  TFORM61 = 'K       '                                                            TTYPE62 = 'BGS_TARGET'         / BGS target selection bitmask                   TFORM62 = 'K       '                                                            TTYPE63 = 'MWS_TARGET'         / Milky Way Survey targeting bits                TFORM63 = 'K       '                                                            TTYPE64 = 'PLATE_RA'           / Right Ascension to be used by PlateMaker       TFORM64 = 'D       '                                                            TUNIT64 = 'deg     '           / PLATE_RA units                                 TTYPE65 = 'PLATE_DEC'          / Declination to be used by PlateMaker           TFORM65 = 'D       '                                                            TUNIT65 = 'deg     '           / PLATE_DEC units                                TTYPE66 = 'TILEID  '           / Unique DESI tile ID                            TFORM66 = 'J       '                                                            TTYPE67 = 'COADD_NUMEXP'       / Number of exposures in coadd                   TFORM67 = 'I       '                                                            TTYPE68 = 'COADD_EXPTIME'      / Summed exposure time for coadd                 TFORM68 = 'E       '                                                            TUNIT68 = 's       '           / COADD_EXPTIME units                            TTYPE69 = 'COADD_NUMNIGHT'     / Number of nights in coadd                      TFORM69 = 'I       '                                                            TTYPE70 = 'COADD_NUMTILE'      / Number of tiles in coadd                       TFORM70 = 'I       '                                                            TTYPE71 = 'MEAN_DELTA_X'       / Mean (over exposures) fiber difference in X    TFORM71 = 'E       '                                                            TUNIT71 = 'mm      '           / MEAN_DELTA_X units                             TTYPE72 = 'RMS_DELTA_X'        / RMS (over exposures) fiber difference in X     TFORM72 = 'E       '                                                            TUNIT72 = 'mm      '           / RMS_DELTA_X units                              TTYPE73 = 'MEAN_DELTA_Y'       / Mean (over exposures) fiber difference in Y    TFORM73 = 'E       '                                                            TUNIT73 = 'mm      '           / MEAN_DELTA_Y units                             TTYPE74 = 'RMS_DELTA_Y'        / RMS (over exposures) difference in Y           TFORM74 = 'E       '                                                            TUNIT74 = 'mm      '           / RMS_DELTA_Y units                              TTYPE75 = 'MEAN_FIBER_RA'      / Mean (over exposures) RA of fiber position     TFORM75 = 'D       '                                                            TUNIT75 = 'deg     '           / MEAN_FIBER_RA units                            TTYPE76 = 'STD_FIBER_RA'       / Standard deviation (over exposures) of RA      TFORM76 = 'E       '                                                            TUNIT76 = 'deg     '           / STD_FIBER_RA units                             TTYPE77 = 'MEAN_FIBER_DEC'     / Mean (over exposures) DEC of fiber position    TFORM77 = 'D       '                                                            TUNIT77 = 'deg     '           / MEAN_FIBER_DEC units                           TTYPE78 = 'STD_FIBER_DEC'      / Standard deviation (over exposures) of DEC     TFORM78 = 'E       '                                                            TUNIT78 = 'deg     '           / STD_FIBER_DEC units                            TTYPE79 = 'MEAN_PSF_TO_FIBER_SPECFLUX' / Mean of PSF_TO_FIBER_SPECFLUX          TFORM79 = 'E       '                                                            TTYPE80 = 'MEAN_FIBER_X'       / Mean (over exposures) fiber CS5 X location     TFORM80 = 'E       '                                                            TUNIT80 = 'mm      '           / MEAN_FIBER_X units                             TTYPE81 = 'MEAN_FIBER_Y'       / Mean (over exposures) fiber CS5 Y location     TFORM81 = 'E       '                                                            TUNIT81 = 'mm      '           / MEAN_FIBER_Y units                             EXTNAME = 'FIBERMAP'                                                            CHECKSUM= 'GbQFJZQ9GaQEGYQ9'   / HDU checksum updated 2024-04-23T15:43:28       DATASUM = '190268092'          / data unit checksum updated 2024-04-23T15:43:28 END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            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;Ä›¦;ÛÐ~:ƒo:ƒo@g²B%®E>9"j@NLñ…�Ð8,±¶?IûçAÑÑìÇ*°XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / array data type                                NAXIS   =                    2 / number of array dimensions                     NAXIS1  =                  162 / length of dimension 1                          NAXIS2  =                    6 / length of dimension 2                          PCOUNT  =                    0 / number of group parameters                     GCOUNT  =                    1 / number of groups                               TFIELDS =                   26 / number of table fields                         TTYPE1  = 'TARGETID'                                                            TFORM1  = 'K       '                                                            TTYPE2  = 'PRIORITY'                                                            TFORM2  = 'J       '                                                            TTYPE3  = 'SUBPRIORITY'                                                         TFORM3  = 'D       '                                                            TTYPE4  = 'NIGHT   '                                                            TFORM4  = 'J       '                                                            TTYPE5  = 'EXPID   '                                                            TFORM5  = 'J       '                                                            TTYPE6  = 'MJD     '                                                            TFORM6  = 'D       '                                                            TTYPE7  = 'TILEID  '                                                            TFORM7  = 'J       '                                                            TTYPE8  = 'EXPTIME '                                                            TFORM8  = 'D       '                                                            TTYPE9  = 'PETAL_LOC'                                                           TFORM9  = 'I       '                                                            TTYPE10 = 'DEVICE_LOC'                                                          TFORM10 = 'J       '                                                            TTYPE11 = 'LOCATION'                                                            TFORM11 = 'K       '                                                            TTYPE12 = 'FIBER   '                                                            TFORM12 = 'J       '                                                            TTYPE13 = 'FIBERSTATUS'                                                         TFORM13 = 'J       '                                                            TTYPE14 = 'FIBERASSIGN_X'                                                       TFORM14 = 'E       '                                                            TTYPE15 = 'FIBERASSIGN_Y'                                                       TFORM15 = 'E       '                                                            TTYPE16 = 'LAMBDA_REF'                                                          TFORM16 = 'E       '                                                            TTYPE17 = 'PLATE_RA'                                                            TFORM17 = 'D       '                                                            TTYPE18 = 'PLATE_DEC'                                                           TFORM18 = 'D       '                                                            TTYPE19 = 'NUM_ITER'                                                            TFORM19 = 'K       '                                                            TTYPE20 = 'FIBER_X '                                                            TFORM20 = 'D       '                                                            TTYPE21 = 'FIBER_Y '                                                            TFORM21 = 'D       '                                                            TTYPE22 = 'DELTA_X '                                                            TFORM22 = 'D       '                                                            TTYPE23 = 'DELTA_Y '                                                            TFORM23 = 'D       '                                                            TTYPE24 = 'FIBER_RA'                                                            TFORM24 = 'D       '                                                            TTYPE25 = 'FIBER_DEC'                                                           TFORM25 = 'D       '                                                            TTYPE26 = 'PSF_TO_FIBER_SPECFLUX'                                               TFORM26 = 'D       '                                                            EXTNAME = 'EXP_FIBERMAP'                                                        END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ŒÎ\pÀßÿÿÿÿ?ïý@/þu
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ÈKE¨À@gª7>jöÐ@NKÛ€‰4�@LrMÒñ©üÀqýó¶E¢?`bMÒñ©ü?h“t¼j~ú@gª7a„8Š@NKÛ¶§?èŒvp¦×.ŒÎ\qÄ?ÆZ ÖýÏ€
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UA@ìù‡çÔ?ˆd@\ï¬q²–üüAÑúwÇ-PE¨À@g²A¾-æ@NLñ<Xöí@:?;dZ¬ÀpåKƧïž?h“t¼j~ú?PbMÒñ©ü@g²Añú¾B@NLñž-j?é?|í‘hqXTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / array data type                                NAXIS   =                    2 / number of array dimensions                     NAXIS1  =                  136 / length of dimension 1                          NAXIS2  =                    3 / length of dimension 2                          PCOUNT  =                    0 / number of group parameters                     GCOUNT  =                    1 / number of groups                               TFIELDS =                   33 / number of table fields                         TTYPE1  = 'TARGETID'                                                            TFORM1  = 'K       '                                                            TTYPE2  = 'TSNR2_GPBDARK_B'                                                     TFORM2  = 'E       '                                                            TTYPE3  = 'TSNR2_ELG_B'                                                         TFORM3  = 'E       '                                                            TTYPE4  = 'TSNR2_GPBBRIGHT_B'                                                   TFORM4  = 'E       '                                                            TTYPE5  = 'TSNR2_LYA_B'                                                         TFORM5  = 'E       '                                                            TTYPE6  = 'TSNR2_BGS_B'                                                         TFORM6  = 'E       '                                                            TTYPE7  = 'TSNR2_GPBBACKUP_B'                                                   TFORM7  = 'E       '                                                            TTYPE8  = 'TSNR2_QSO_B'                                                         TFORM8  = 'E       '                                                            TTYPE9  = 'TSNR2_LRG_B'                                                         TFORM9  = 'E       '                                                            TTYPE10 = 'TSNR2_GPBDARK_R'                                                     TFORM10 = 'E       '                                                            TTYPE11 = 'TSNR2_ELG_R'                                                         TFORM11 = 'E       '                                                            TTYPE12 = 'TSNR2_GPBBRIGHT_R'                                                   TFORM12 = 'E       '                                                            TTYPE13 = 'TSNR2_LYA_R'                                                         TFORM13 = 'E       '                                                            TTYPE14 = 'TSNR2_BGS_R'                                                         TFORM14 = 'E       '                                                            TTYPE15 = 'TSNR2_GPBBACKUP_R'                                                   TFORM15 = 'E       '                                                            TTYPE16 = 'TSNR2_QSO_R'                                                         TFORM16 = 'E       '                                                            TTYPE17 = 'TSNR2_LRG_R'                                                         TFORM17 = 'E       '                                                            TTYPE18 = 'TSNR2_GPBDARK_Z'                                                     TFORM18 = 'E       '                                                            TTYPE19 = 'TSNR2_ELG_Z'                                                         TFORM19 = 'E       '                                                            TTYPE20 = 'TSNR2_GPBBRIGHT_Z'                                                   TFORM20 = 'E       '                                                            TTYPE21 = 'TSNR2_LYA_Z'                                                         TFORM21 = 'E       '                                                            TTYPE22 = 'TSNR2_BGS_Z'                                                         TFORM22 = 'E       '                                                            TTYPE23 = 'TSNR2_GPBBACKUP_Z'                                                   TFORM23 = 'E       '                                                            TTYPE24 = 'TSNR2_QSO_Z'                                                         TFORM24 = 'E       '                                                            TTYPE25 = 'TSNR2_LRG_Z'                                                         TFORM25 = 'E       '                                                            TTYPE26 = 'TSNR2_GPBDARK'                                                       TFORM26 = 'E       '                                                            TTYPE27 = 'TSNR2_ELG'                                                           TFORM27 = 'E       '                                                            TTYPE28 = 'TSNR2_GPBBRIGHT'                                                     TFORM28 = 'E       '                                                            TTYPE29 = 'TSNR2_LYA'                                                           TFORM29 = 'E       '                                                            TTYPE30 = 'TSNR2_BGS'                                                           TFORM30 = 'E       '                                                            TTYPE31 = 'TSNR2_GPBBACKUP'                                                     TFORM31 = 'E       '                                                            TTYPE32 = 'TSNR2_QSO'                                                           TFORM32 = 'E       '                                                            TTYPE33 = 'TSNR2_LRG'                                                           TFORM33 = 'E       '                                                            EXTNAME = 'TSNR2   '                                                            END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ŒÎ\pÀßA¤In<'9J@‚«Ý@æ;Bu"B/L>Š=]=àïE»@’ô=CÑéO<¼™Cð)*EG?¸H?@ÖXÌ6d¹’Ap{54;ÔD@.;6º*š@O·@è9E
+ŸA�€CÓóþ@æV™D£Ê1EI>Ö@ž‘uA`ü>ŒÎ\qéA¬öY<0
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6½"@S(6@ëh·E
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diff --git a/py/redrock/test/data/rrdetails-test.h5 b/py/redrock/test/data/rrdetails-test.h5
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diff --git a/py/redrock/test/test_archetypes.py b/py/redrock/test/test_archetypes.py
new file mode 100644
index 00000000..9fb8e8f6
--- /dev/null
+++ b/py/redrock/test/test_archetypes.py
@@ -0,0 +1,43 @@
+"""
+Test redrock.archetypes
+
+TODO: expand tests
+"""
+
+import unittest
+
+class TestArchetypes(unittest.TestCase):
+
+    def test_split_archetype_coeff(self):
+        """test archetypes.split_archetype_coeff"""
+        from redrock.archetypes import split_archetype_coeff
+
+        # for archetypes, the coeff array is N archetype coefficients,
+        # followed by nbands * nleg Legendre coefficients
+
+        #- 1 archetype, 3 bands, 1 legendre per band
+        ac, lc = split_archetype_coeff('ELG_12', [1,2,3,4], nbands=3)
+        self.assertEqual(ac, [1,])
+        self.assertEqual(lc, [[2,], [3,], [4,]])
+
+        #- 1 archetype, 3 bands, 2 legendre per band
+        ac, lc = split_archetype_coeff('ELG_12', [1,2,3,4,5,6,7], nbands=3)
+        self.assertEqual(ac, [1,])
+        self.assertEqual(lc, [[2,3], [4,5], [6,7]])
+
+        #- 1 archetype, 3 bands, 2 legendre per band, with trailing zeros
+        ac, lc = split_archetype_coeff('ELG_12', [1,2,3,4,5,6,7,0,0,0], nbands=3)
+        self.assertEqual(ac, [1,])
+        self.assertEqual(lc, [[2,3], [4,5], [6,7]])
+
+        #- 2 archetypes, 3 bands, 2 legendre per band, with trailing zeros
+        ac, lc = split_archetype_coeff('ELG_12;LRG_5', [1,0,2,3,4,5,6,7,0,0,0], nbands=3)
+        self.assertEqual(ac, [1,0])
+        self.assertEqual(lc, [[2,3], [4,5], [6,7]])
+
+        #- no legendre terms
+        ac, lc = split_archetype_coeff('ELG_12;LRG_5', [1,0,], nbands=3)
+        self.assertEqual(ac, [1,0])
+        self.assertEqual(lc, [list(), list(), list()])
+
+
diff --git a/py/redrock/test/test_io.py b/py/redrock/test/test_io.py
index 04c15ae3..d12668d8 100644
--- a/py/redrock/test/test_io.py
+++ b/py/redrock/test/test_io.py
@@ -8,12 +8,11 @@
 
 from .. import utils as rrutils
 from ..results import read_zscan, write_zscan
-from ..templates import DistTemplate, find_templates, load_templates, load_dist_templates
 from ..zfind import zfind
+from ..templates import DistTemplate
 
 from . import util
 
-
 class TestIO(unittest.TestCase):
 
     #- Create unique test filename in a subdirectory
@@ -22,42 +21,12 @@ def setUpClass(cls):
         cls.testDir = tempfile.mkdtemp()        
         cls.testfile = os.path.join(cls.testDir, 'test-{uuid}.h5'.format(uuid=uuid1()))
 
-        # create dummy template files in a separate dir
-        cls.testTemplateDir = tempfile.mkdtemp()
-        cls.default_templates = (
-                'rrtemplate-galaxy-v2.fits',
-                'rrtemplate-qso-v1.fits',
-                )
-        cls.alternate_templates = (
-                'rrtemplate-galaxy-v1.fits',
-                'rrtemplate-blat-v1.fits',
-                )
-
-        for filename in cls.default_templates + cls.alternate_templates:
-            with open(f'{cls.testTemplateDir}/{filename}', 'w'):
-                pass
-
-        with open(cls.testTemplateDir + '/default_templates.txt', 'w') as fx:
-            fx.write('# Header comment\n')
-            fx.write('\n')
-            for filename in cls.default_templates:
-                fx.write(f'{filename}\n')
-
-        with open(cls.testTemplateDir + '/alternate_templates.txt', 'w') as fx:
-            fx.write('# Header comment\n')
-            fx.write('\n')
-            for filename in cls.alternate_templates:
-                fx.write(f'{filename}\n')
-
     #- Cleanup test files if they exist
     @classmethod
     def tearDownClass(cls):
         if os.path.exists(cls.testfile):
             os.remove(cls.testfile)
 
-        if os.path.isdir(cls.testDir):
-            rmtree(cls.testDir)
-
     def setUp(self):
         #- remove testfile if leftover from a previous test
         if os.path.exists(self.testfile):
@@ -73,60 +42,6 @@ def test_endian(self):
         else:
             self.assertTrue(x2 is rrutils.native_endian(x2))
 
-    ### @unittest.skipIf('RR_TEMPLATE_DIR' not in os.environ, '$RR_TEMPLATE_DIR not set')
-    def test_find_templates(self):
-        templates = find_templates()
-        self.assertTrue(len(templates) > 0)
-        template_dir = os.path.dirname(templates[0])
-        templates = find_templates(template_dir)
-        self.assertTrue(len(templates) > 0)
-
-        template_dir = os.path.dirname(templates[0])
-        templates = find_templates(template_dir)
-        self.assertTrue(len(templates) > 0)
-
-        templates = find_templates(self.testTemplateDir)
-        self.assertEqual(len(templates), len(self.default_templates))
-        for filename in templates:
-            self.assertIn(os.path.basename(filename), self.default_templates)
-
-        templates = find_templates(self.testTemplateDir+'/alternate_templates.txt')
-        self.assertEqual(len(templates), len(self.alternate_templates))
-        for filename in templates:
-            self.assertIn(os.path.basename(filename), self.alternate_templates)
-
-        with self.assertRaises(ValueError):
-            templates = find_templates(self.testTemplateDir+'/blat.txt')
-
-        with self.assertRaises(ValueError):
-            templates = find_templates('blat.txt')
-
-        with self.assertRaises(ValueError):
-            templates = find_templates('blat.foo')
-
-
-    def test_load_templates(self):
-        templates = load_templates()
-        self.assertTrue(len(templates) > 0)
-
-        template_files = find_templates()
-        templates = load_templates(template_files[0:2])
-        self.assertEqual(len(templates), 2)
-
-
-    ### @unittest.skipIf('RR_TEMPLATE_DIR' not in os.environ, '$RR_TEMPLATE_DIR not set')
-    def test_load_dist_templates(self):
-        dtarg = util.fake_targets()
-        dwave = dtarg.wavegrids()
-        for dtp in load_dist_templates(dwave):
-            self.assertIn('wave', dtp.template.__dict__)
-            self.assertIn('flux', dtp.template.__dict__)
-            wave = dtp.template.wave
-            flux = dtp.template.flux
-            self.assertEqual(wave.shape[0], flux.shape[1])
-            self.assertEqual(wave.ndim, 1)
-            self.assertEqual(flux.ndim, 2)
-
     def test_zscan_io(self):
         dtarg = util.fake_targets()
 
@@ -139,6 +54,8 @@ def test_zscan_io(self):
 
         zscan1, zfit1 = zfind(dtarg, [ dtemp ])
 
+        zfit1.remove_column('fitmethod')
+
         write_zscan(self.testfile, zscan1, zfit1)
         write_zscan(self.testfile, zscan1, zfit1, clobber=True)
         zscan2, zfit2 = read_zscan(self.testfile)
@@ -154,4 +71,17 @@ def test_zscan_io(self):
                     d2 = zscan2[targetid][spectype][key]
                     self.assertTrue(np.all(d1==d2), 'data mismatch {}/{}/{}'.format(targetid, spectype, key))
 
+    def test_read_zscan(self):
+        """Additional read_zscan testing with pre-generated data"""
+        import importlib
+        zscanfile = importlib.resources.files('redrock.test').joinpath('data/rrdetails-test.h5')
+        zscan, zfit = read_zscan(zscanfile)
+
+        for targetid in np.unique(zfit['targetid']):
+            for fulltype in zscan[targetid].keys():
+                zz = zscan[targetid][fulltype]
+                self.assertEqual(len(zz['redshifts']), len(zz['zchi2']))
+                self.assertGreater(len(zz['zfit']), 0, f'Empty zfit Table for {targetid=} {fulltype=}')
+
+
 
diff --git a/py/redrock/test/test_templates.py b/py/redrock/test/test_templates.py
new file mode 100644
index 00000000..b92f6029
--- /dev/null
+++ b/py/redrock/test/test_templates.py
@@ -0,0 +1,190 @@
+from __future__ import division, print_function
+
+import os, tempfile
+from shutil import rmtree
+import unittest
+from uuid import uuid1
+import numpy as np
+
+from .. import utils as rrutils
+from ..templates import DistTemplate, find_templates, load_templates, load_dist_templates
+
+from . import util as testutil
+
+class TestTemplates(unittest.TestCase):
+
+    #- Create unique test filename in a subdirectory
+    @classmethod
+    def setUpClass(cls):
+        cls.testDir = tempfile.mkdtemp()        
+        cls.testfile = os.path.join(cls.testDir, 'test-{uuid}.h5'.format(uuid=uuid1()))
+        cls.starting_cwd = os.getcwd()
+
+        # create dummy template files in a separate dir
+        cls.testTemplateDir = tempfile.mkdtemp()
+        cls.default_templates = (
+                'rrtemplate-galaxy-v2.fits',
+                'rrtemplate-qso-v1.fits',
+                )
+        cls.alternate_templates = (
+                'rrtemplate-galaxy-v1.fits',
+                'rrtemplate-blat-v1.fits',
+                )
+
+        for filename in cls.default_templates + cls.alternate_templates:
+            with open(f'{cls.testTemplateDir}/{filename}', 'w'):
+                pass
+
+        with open(cls.testTemplateDir + '/templates-default.txt', 'w') as fx:
+            fx.write('# Header comment\n')
+            fx.write('\n')
+            for filename in cls.default_templates:
+                fx.write(f'{filename}\n')
+
+        with open(cls.testTemplateDir + '/templates-alternate.txt', 'w') as fx:
+            fx.write('# Header comment\n')
+            fx.write('\n')
+            for filename in cls.alternate_templates:
+                fx.write(f'{filename}\n')
+
+    #- Cleanup test files if they exist
+    @classmethod
+    def tearDownClass(cls):
+        if os.path.exists(cls.testfile):
+            os.remove(cls.testfile)
+
+        if os.path.isdir(cls.testDir):
+            rmtree(cls.testDir)
+
+    def setUp(self):
+        #- remove testfile if leftover from a previous test
+        if os.path.exists(self.testfile):
+            os.remove(self.testfile)
+
+        #- make sure we are in starting directory every time
+        os.chdir(self.starting_cwd)
+
+    ### @unittest.skipIf('RR_TEMPLATE_DIR' not in os.environ, '$RR_TEMPLATE_DIR not set')
+    def test_find_templates(self):
+        templates = find_templates()
+        self.assertTrue(len(templates) > 0)
+        template_dir = os.path.dirname(templates[0])
+        templates = find_templates(template_dir)
+        self.assertTrue(len(templates) > 0)
+
+        template_dir = os.path.dirname(templates[0])
+        templates = find_templates(template_dir)
+        self.assertTrue(len(templates) > 0)
+
+        templates = find_templates(self.testTemplateDir)
+        self.assertEqual(len(templates), len(self.default_templates))
+        for filename in templates:
+            self.assertIn(os.path.basename(filename), self.default_templates)
+
+        templates = find_templates(self.testTemplateDir+'/templates-alternate.txt')
+
+        # works without a path prefix
+        os.chdir(self.testTemplateDir)
+        templates = find_templates('templates-alternate.txt')
+        os.chdir(self.starting_cwd)
+
+        self.assertEqual(len(templates), len(self.alternate_templates))
+        for filename in templates:
+            self.assertIn(os.path.basename(filename), self.alternate_templates)
+
+        with self.assertRaises(ValueError):
+            templates = find_templates(self.testTemplateDir+'/blat.txt')
+
+        with self.assertRaises(ValueError):
+            templates = find_templates('blat.txt')
+
+        with self.assertRaises(ValueError):
+            templates = find_templates('blat.foo')
+
+    def test_load_templates(self):
+        templates = load_templates()
+        self.assertTrue(len(templates) > 0)
+
+        template_files = find_templates()
+        templates = load_templates(template_files[0:2])
+        self.assertEqual(len(templates), 2)
+
+        self.assertEqual(templates[0].filename, template_files[0])
+        self.assertEqual(templates[1].filename, template_files[1])
+
+
+    ### @unittest.skipIf('RR_TEMPLATE_DIR' not in os.environ, '$RR_TEMPLATE_DIR not set')
+    def test_load_dist_templates(self):
+        dtarg = testutil.fake_targets()
+        dwave = dtarg.wavegrids()
+        for dtp in load_dist_templates(dwave):
+            self.assertIn('wave', dtp.template.__dict__)
+            self.assertIn('flux', dtp.template.__dict__)
+            wave = dtp.template.wave
+            flux = dtp.template.flux
+            self.assertEqual(wave.shape[0], flux.shape[1])
+            self.assertEqual(wave.ndim, 1)
+            self.assertEqual(flux.ndim, 2)
+
+    def test_header2templatefiles(self):
+        """Test templates.header2templatefiles"""
+        from ..templates import header2templatefiles
+
+        #- Derive filenames from TEMNAM/TEMVER, with whitespace
+        #- STAR version unknown defaults to 0.1
+        hdr = dict(
+                TEMNAM00='GALAXY  ',  TEMVER00='2.6     ',
+                TEMNAM01='QSO:::HIZ', TEMVER01='1.0     ',
+                TEMNAM02='STAR:::M',  TEMVER02='unknown ',
+            )
+        filenames = header2templatefiles(hdr, template_dir=self.testTemplateDir)
+        self.assertTrue(filenames[0].endswith('rrtemplate-GALAXY-None-v2.6.fits'), f'Wrong {filenames[0]}')
+        self.assertTrue(filenames[1].endswith('rrtemplate-QSO-HIZ-v1.0.fits'),     f'Wrong {filenames[1]}')
+        self.assertTrue(filenames[2].endswith('rrtemplate-STAR-M-v0.1.fits'),      f'Wrong {filenames[2]}')
+
+        #- Derive filenames from TEMNAM/TEMVER, no whitespace
+        #- star version used if not "unknown"
+        hdr = dict(
+                TEMNAM00='GALAXY',    TEMVER00='2.6',
+                TEMNAM01='QSO:::HIZ', TEMVER01='1.0',
+                TEMNAM02='STAR:::M',  TEMVER02='3.14',
+            )
+        filenames = header2templatefiles(hdr, template_dir=self.testTemplateDir)
+        self.assertTrue(filenames[0].endswith('rrtemplate-GALAXY-None-v2.6.fits'), f'Wrong {filenames[0]}')
+        self.assertTrue(filenames[1].endswith('rrtemplate-QSO-HIZ-v1.0.fits'),     f'Wrong {filenames[1]}')
+        self.assertTrue(filenames[2].endswith('rrtemplate-STAR-M-v3.14.fits'),     f'Wrong {filenames[2]}')
+
+        #- TEMFIL overrides TEMNAM/TEMVER if it exists
+        hdr = dict(
+                TEMNAM00='GALAXY',    TEMVER00='2.6',  TEMFIL00='blat.fits',
+                TEMNAM01='QSO:::HIZ', TEMVER01='1.0',  TEMFIL01='foo.fits',
+                TEMNAM02='STAR:::M',  TEMVER02='3.14', TEMFIL02='bar.fits'
+            )
+        filenames = header2templatefiles(hdr, template_dir=self.testTemplateDir)
+        self.assertTrue(filenames[0].endswith('blat.fits'), f'Wrong {filenames[0]}')
+        self.assertTrue(filenames[1].endswith('foo.fits'),  f'Wrong {filenames[1]}')
+        self.assertTrue(filenames[2].endswith('bar.fits'),  f'Wrong {filenames[2]}')
+
+        #- Testing future possibilities for GALAXY suptypes
+        #- Also test dict entries "out of order"
+        hdr = dict(
+                TEMNAM00='GALAXY:::BGS',    TEMVER00='3.0',
+                TEMNAM02='GALAXY:::LRG',    TEMVER02='3.0',
+                TEMNAM01='GALAXY:::ELG',    TEMVER01='3.0',
+            )
+        filenames = header2templatefiles(hdr, template_dir=self.testTemplateDir)
+        self.assertTrue(filenames[0].endswith('rrtemplate-GALAXY-BGS-v3.0.fits'), f'Wrong {filenames[0]}')
+        self.assertTrue(filenames[1].endswith('rrtemplate-GALAXY-ELG-v3.0.fits'), f'Wrong {filenames[1]}')
+        self.assertTrue(filenames[2].endswith('rrtemplate-GALAXY-LRG-v3.0.fits'), f'Wrong {filenames[2]}')
+
+    def test_fulltype_parsing(self):
+        from ..templates import parse_fulltype, make_fulltype
+        self.assertEqual(parse_fulltype('GALAXY'), ('GALAXY', ''))
+        self.assertEqual(parse_fulltype('QSO:::HIZ'), ('QSO', 'HIZ'))
+
+        self.assertEqual(make_fulltype('GALAXY', ''), 'GALAXY')
+        self.assertEqual(make_fulltype('GALAXY', None), 'GALAXY')
+        self.assertEqual(make_fulltype('QSO', 'HIZ'), 'QSO:::HIZ')
+
+
+
diff --git a/py/redrock/test/test_utils.py b/py/redrock/test/test_utils.py
index 5039b00d..dc15bbd7 100644
--- a/py/redrock/test/test_utils.py
+++ b/py/redrock/test/test_utils.py
@@ -38,6 +38,27 @@ def test_distribute_work(self):
         dist = utils.distribute_work(nproc, ids, capacities=capacities)
         self.assertEqual(list(map(len, dist)), [1, 3])
 
+    def test_reduced_wavelength(self):
+        x = utils.reduced_wavelength(np.arange(10))
+        self.assertEqual(x[0], -1.0)
+        self.assertEqual(x[-1], 1.0)
+        x = utils.reduced_wavelength(np.linspace(3600, 5800, 10))
+        self.assertEqual(x[0], -1.0)
+        self.assertEqual(x[-1], 1.0)
+
+        #- even out-of-order non-linear ok
+        x = utils.reduced_wavelength(np.random.uniform(-5, 20, size=100))
+        self.assertEqual(np.min(x), -1.0)
+        self.assertEqual(np.max(x), 1.0)
+
+        #- also works on cupy if installed,
+        #- and answer remains on GPU as a cupy array
+        if cp_available:
+            x = utils.reduced_wavelength(cp.linspace(3600, 5800, 10))
+            self.assertEqual(x[0], -1.0)
+            self.assertEqual(x[-1], 1.0)
+            self.assertTrue(isinstance(x, cp.ndarray))
+
 def test_suite():
     """Allows testing of only this module with the command::
 
diff --git a/py/redrock/utils.py b/py/redrock/utils.py
index 253981a3..9ad6898c 100644
--- a/py/redrock/utils.py
+++ b/py/redrock/utils.py
@@ -163,9 +163,17 @@ def mp_array(original):
     Returns;
         ndarray: the wrapped data.
 
+    If the input array has size=0, the original is returned without
+    wrapping it in a size=0 multiprocessing.RawArray (which generates
+    warnings)
     """
     import multiprocessing as mp
 
+    # A zero-length array generates the warning documented in
+    # https://github.com/desihub/redrock/issues/250; capture it.
+    if original.size == 0:
+        return original
+    
     typecode = original.dtype.char
     shape = original.shape
 
@@ -248,3 +256,16 @@ def getGPUCountMPI(comm):
     if comm.rank == 0:
         print("WARNING:  Found {:d} GPUs".format(len(pci_id_list)))
     return len(pci_id_list)
+
+def reduced_wavelength(wave):
+    """function to calculate reduced wavelength arrya for 
+    legendre polynomial in archetype mode, 
+    legendre polynomials are orthogonal in [-1,1]
+    Args:
+        wave (array): wavelength for which reduced wavelengths to be estimated
+    Return:
+        reduced wavelength in [-1,1] range
+    """
+    wavemax = wave.max()
+    wavemin = wave.min()
+    return 2*(wave - wavemin) / (wavemax - wavemin) - 1
diff --git a/py/redrock/zfind.py b/py/redrock/zfind.py
index bb330828..913186b1 100644
--- a/py/redrock/zfind.py
+++ b/py/redrock/zfind.py
@@ -21,6 +21,8 @@
 
 from .targets import distribute_targets
 
+from .templates import parse_fulltype
+
 from .archetypes import All_archetypes
 
 from .priors import Priors
@@ -208,7 +210,8 @@ def sort_zfit_dict(zfit):
     sort_dict_by_cols(zfit, ('__badfit__', 'chi2'), sort_first_column_first=True)
     zfit.pop('__badfit__')
 
-def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=None, chi2_scan=None, use_gpu=False, zminfit_npoints=15, per_camera=None, deg_legendre=None, n_nearest=None, prior_sigma=None, ncamera=None):
+def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=None, chi2_scan=None, use_gpu=False,
+          zminfit_npoints=15, per_camera=None, deg_legendre=None, n_nearest=None, prior_sigma=None, ncamera=None):
     """Compute all redshift fits for the local set of targets and collect.
 
     Given targets and templates distributed across a set of MPI processes,
@@ -228,7 +231,7 @@ def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=Non
             multiprocessing processes to use.
         nminima (int, optional): number of chi^2 minima to consider.
             Passed to fitz().
-        archetypes (str, optional): file or directory containing archetypes
+        archetypes (list optional): list of Archetype objects
             to use for final fitz choice of best chi2 vs. z minimum.
         priors (str, optional): file containing redshift priors
         chi2_scan (str, optional): file containing already computed chi2 scan
@@ -249,7 +252,6 @@ def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=Non
     """
 
     if archetypes:
-        archetypes = All_archetypes(archetypes_dir=archetypes).archetypes
         archetype_spectype = list(archetypes.keys()) # to account for the case if only one archetype is provided
     
     if not priors is None:
@@ -320,9 +322,8 @@ def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=Non
     #creating list of template spectype to make use in case archetypes would be used
     all_spectype = []
     for spec in templates:
-        temp_spectype = spec.template._rrtype.split(':::')
-        if temp_spectype not in all_spectype:
-            all_spectype.append(temp_spectype)
+        if spec.template.template_type not in all_spectype:
+            all_spectype.append(spec.template.template_type)
 
     for t in np.array(list(templates))[sort]:
         ft = t.template.full_type
@@ -423,23 +424,27 @@ def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=Non
                     continue
                 tmp = allresults[tid][fulltype]['zfit']
 
-                #- TODO: reconsider fragile parsing of fulltype
                 if archetypes is None:
-                    if fulltype.count(':::') > 0:
-                        spectype, subtype = fulltype.split(':::')
-                    else:
-                        spectype, subtype = (fulltype, '')
-
+                    spectype, subtype = parse_fulltype(fulltype)
                 else:
                     if 'fulltype' in tmp.keys():#to take care of case when archetype is applied only for one template
-                        spectype = [ el[0].split(':::')[0] for el in tmp['fulltype'] ] #el is a list with one element (corresponding to each minima)
-                        subtype = [ el[0].split(':::')[1] for el in tmp['fulltype'] ]
+                        spectype = list()
+                        subtype = list()
+                        #l el is a list with one element (corresponding to each minimum)
+                        for el in tmp['fulltype']:
+                            this_spectype, this_subtype = parse_fulltype(el[0])
+                            spectype.append(this_spectype)
+                            subtype.append(this_subtype)
                         del tmp['fulltype'] #it's a dictionary
                     else:
-                        if fulltype.count(':::') > 0:
-                            spectype, subtype = fulltype.split(':::')
-                        else:
-                            spectype, subtype = (fulltype, '')
+                        spectype, subtype = parse_fulltype(fulltype)
+
+                # set max_velo_diff differently for STARs, but watch out
+                # for archtypes which have spectype as list instead of scalar
+                if (np.isscalar(spectype) and spectype.upper() == 'STAR') or spectype[0].upper() == 'STAR':
+                    max_velo_diff = constants.max_velo_diff_star
+                else:
+                    max_velo_diff = constants.max_velo_diff
 
                 #Have to create arrays of correct length since using dict of
                 #np arrays instead of astropy Table
@@ -527,8 +532,8 @@ def zfind(targets, templates, mp_procs=1, nminima=3, archetypes=None, priors=Non
             tzfit['znum'] = np.arange(l)
 
             #- calc deltachi2 and set ZW.SMALL_DELTA_CHI2 flag
-            deltachi2, setzwarn = calc_deltachi2(
-                    tzfit['chi2'], tzfit['z'], tzfit['zwarn'])
+            deltachi2, setzwarn = calc_deltachi2(tzfit['chi2'], tzfit['z'], tzfit['zwarn'],
+                                                 dvlimit=max_velo_diff)
             tzfit['deltachi2'] = deltachi2
             tzfit['zwarn'][setzwarn] |= ZW.SMALL_DELTA_CHI2
 
diff --git a/py/redrock/zscan.py b/py/redrock/zscan.py
index d54fc941..10a0c8d5 100644
--- a/py/redrock/zscan.py
+++ b/py/redrock/zscan.py
@@ -273,7 +273,6 @@ def prior_on_coeffs(n_nbh, deg_legendre, sigma, ncamera, add_negative_legendre_t
     return prior
 
 def per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflux, nleg, narch, method=None, n_nbh=None, prior_sigma=None, use_gpu=False, bands=None):
-    
     """This function calculates coefficients for archetype mode in each camera using normal linear algebra matrix solver or BVLS (bounded value least square) method
 
     BVLS described in : https://www.stat.berkeley.edu/~stark/Preprints/bvls.pdf
@@ -293,7 +292,6 @@ def per_camera_coeff_with_least_square_batch(target, binned, weights, flux, wflu
         prior (array): prior matrix added to the Legendre coefficients (1/sigma^2)
         use_gpu (bool): use GPU or not
         bands (list): list of wavelength bands
-        ncam (int): number of cameras for given instrument
     
     Returns:
         coefficients and chi2
@@ -708,7 +706,8 @@ def calc_batch_dot_product_3d3d_gpu(a, b, transpose_a=False, fullprecision=True)
 ###    are very obviously analagous though and should be highly
 ###    maintainable.  The main difference is the extra loop on the CPU version
 
-def calc_zchi2_batch(spectra, tdata, weights, flux, wflux, nz, nbasis, solve_matrices_algorithm=None, solver_args=None, use_gpu=False, fullprecision=True, prior=None):
+def calc_zchi2_batch(spectra, tdata, weights, flux, wflux, nz, nbasis, solve_matrices_algorithm=None,
+                     solver_args=None, use_gpu=False, fullprecision=True, prior=None):
     
     """Calculate a batch of chi2.
     For many redshifts and a set of spectral data, compute the chi2 for
@@ -864,6 +863,24 @@ def calc_zchi2_batch(spectra, tdata, weights, flux, wflux, nz, nbasis, solve_mat
             zchi2[i] = np.dot( (flux - model)**2, weights )
     return (zchi2, zcoeff)
 
+def calc_negOII_penalty(OIItemplate, coeff):
+    """return penalty term for model having negative [OII] flux
+
+    Args:
+        OIItemplate[nwave, nbasis]: portion of template around [OII] doublet
+        coeff[nz, nbasis]: coefficients from full template fit to data
+
+    Returns:
+        penalty[nz]: penalty per redshift bin
+    """
+    #- evaluate template and sum over [OII] doublet
+    OIIflux = np.sum(coeff @ OIItemplate, axis=1)
+
+    #- Linear penalty with negative [OII]; no mathematical basis but
+    #- pragmatically works ok
+    penalty = -OIIflux * (OIIflux < 0)
+
+    return penalty
 
 ###!!! NOTE - this is the main method for the v3 algorithm
 ###    In this version, everything is done in batch on the GPU but the
@@ -912,12 +929,6 @@ def calc_zchi2(target_ids, target_data, dtemplate, progress=None, use_gpu=False)
     zchi2penalty = np.zeros( (ntargets, nz) )
     zcoeff = np.zeros( (ntargets, nz, nbasis) )
 
-    # Redshifts near [OII]; used only for galaxy templates
-    if dtemplate.template.template_type == 'GALAXY':
-        isOII = (3724 <= dtemplate.template.wave) & \
-            (dtemplate.template.wave <= 3733)
-        OIItemplate = dtemplate.template.flux[:,isOII].T
-
     ## Redshifted templates are now already in format needed - dict of 3d
     # arrays (CUPY or numpy).
     tdata = dtemplate.local.data
@@ -946,12 +957,14 @@ def calc_zchi2(target_ids, target_data, dtemplate, progress=None, use_gpu=False)
         # for all templates for all three spectra for this target
 
         # For coarse z scan, use fullprecision = False to maximize speed
-        (zchi2[j,:], zcoeff[j,:,:]) = calc_zchi2_batch(target_data[j].spectra, tdata, weights, flux, wflux, nz, nbasis, dtemplate.template.solve_matrices_algorithm, use_gpu=use_gpu, fullprecision=False)
-
-        #- Penalize chi2 for negative [OII] flux; ad-hoc
-        if dtemplate.template.template_type == 'GALAXY':
-            OIIflux = np.sum(zcoeff[j] @ OIItemplate.T, axis=1)
-            zchi2penalty[j][OIIflux < 0] = -OIIflux[OIIflux < 0]
+        (zchi2[j,:], zcoeff[j,:,:]) = calc_zchi2_batch(
+            target_data[j].spectra, tdata, weights, flux, wflux, nz, nbasis,
+            dtemplate.template.solve_matrices_algorithm, use_gpu=use_gpu,
+            fullprecision=False)
+
+        #- Galaxy templates penalize chi2 for negative [OII] flux; ad-hoc
+        if hasattr(dtemplate.template, 'OIItemplate'):
+            zchi2penalty[j] = calc_negOII_penalty(dtemplate.template.OIItemplate, zcoeff[j])
 
         if dtemplate.comm is None and progress is not None:
             progress.put(1)