def store_models(self, specs, ivar):
self.models = n.zeros( (specs.shape) )
for i in xrange(self.models.shape[0]):
minloc = n.unravel_index( self.zchi2arr[i].argmin(),
self.zchi2arr[i].shape )
pmat = n.zeros( (specs.shape[-1],self.npoly+1) )
this_temp = self.templates[minloc[:-1]]
pmat[:,0] = this_temp[(minloc[-1]*self.npixstep) +
self.pixoffset:(minloc[-1]*self.npixstep) +
self.pixoffset+specs.shape[-1]]
polyarr = poly_array(self.npoly, specs.shape[-1])
pmat[:,1:] = n.transpose(polyarr)
ninv = n.diag(ivar[i])
try: # Some eBOSS spectra have ivar[i] = 0 for all i
'''
f = n.linalg.solve( n.dot(n.dot(n.transpose(pmat),ninv),pmat),
n.dot( n.dot(n.transpose(pmat),ninv),specs[i]) )
'''
f = nnls( n.dot(n.dot(n.transpose(pmat),ninv),pmat),
n.dot( n.dot(n.transpose(pmat),ninv),specs[i]) );\
f = n.array(f)[0]
self.models[i] = n.dot(pmat, f)
except Exception as e:
self.models[i] = n.zeros(specs.shape[-1])
print "Exception: %r" % r
def create_model(self, fname, npoly, npixstep, minvector, zfindobj, flux,
ivar):
"""Return the best fit model for a given template at a
given redshift.
"""
try:
pixoffset = zfindobj.pixoffset
temps = read_ndArch(
join(environ['REDMONSTER_TEMPLATES_DIR'], fname))[0]
pmat = n.zeros((self.npixflux, npoly + 1))
this_temp = temps[minvector[:-1]]
pmat[:,0] = this_temp[(minvector[-1]*npixstep)+pixoffset:\
(minvector[-1]*npixstep)+pixoffset + \
self.npixflux]
polyarr = poly_array(npoly, self.npixflux)
pmat[:, 1:] = n.transpose(polyarr)
ninv = n.diag(ivar)
f = linalg.solve(n.dot(n.dot(n.transpose(pmat), ninv), pmat),
n.dot(n.dot(n.transpose(pmat), ninv), flux)) \
f = n.array(f)
if f[0] < 0:
try:
f = nnls(n.dot(n.dot(n.transpose(pmat), ninv), pmat),
n.dot(n.dot(n.transpose(pmat), ninv), flux))[0] \
f = n.array(f)
return n.dot(pmat, f), tuple(f)
except Exception as e:
print("Exception: %r" % e)
return n.zeros(self.npixflux), (0, )
else:
return n.dot(pmat, f), tuple(f)
except Exception as e:
print("Exception: %r" % e)
return n.zeros(self.npixflux), (0, )
def store_models(self, specs, ivar):
self.models = n.zeros((specs.shape))
for i in range(self.models.shape[0]):
minloc = n.unravel_index(self.zchi2arr[i].argmin(),
self.zchi2arr[i].shape)
pmat = n.zeros((specs.shape[-1], self.npoly + 1))
this_temp = self.templates[minloc[:-1]]
pmat[:,
0] = this_temp[(minloc[-1] * self.npixstep) +
self.pixoffset:(minloc[-1] * self.npixstep) +
self.pixoffset + specs.shape[-1]]
polyarr = poly_array(self.npoly, specs.shape[-1])
pmat[:, 1:] = n.transpose(polyarr)
ninv = n.diag(ivar[i])
try: # Some eBOSS spectra have ivar[i] = 0 for all i
'''
f = linalg.solve( n.dot(n.dot(n.transpose(pmat),ninv),pmat),
n.dot( n.dot(n.transpose(pmat),ninv),specs[i]) )
'''
f = nnls(n.dot(n.dot(n.transpose(pmat), ninv), pmat),
n.dot(n.dot(n.transpose(pmat), ninv), specs[i])) \
f = n.array(f)[0]
self.models[i] = n.dot(pmat, f)
except Exception as e:
self.models[i] = n.zeros(specs.shape[-1])
print("Exception: %r" % r)
def create_model(self, fname, npoly, npixstep, minvector, zfindobj,
flux, ivar):
"""Return the best fit model for a given template at a
given redshift.
"""
try:
pixoffset = zfindobj.pixoffset
temps = read_ndArch( join( environ['REDMONSTER_TEMPLATES_DIR'],
fname ) )[0]
pmat = n.zeros( (self.npixflux, npoly+1) )
this_temp = temps[minvector[:-1]]
pmat[:,0] = this_temp[(minvector[-1]*npixstep)+pixoffset:\
(minvector[-1]*npixstep)+pixoffset + \
self.npixflux]
polyarr = poly_array(npoly, self.npixflux)
pmat[:,1:] = n.transpose(polyarr)
ninv = n.diag(ivar)
f = linalg.solve( n.dot(n.dot(n.transpose(pmat),ninv),pmat),
n.dot( n.dot(n.transpose(pmat),ninv),flux) ); \
f = n.array(f)
if f[0] < 0:
try:
f = nnls( n.dot(n.dot(n.transpose(pmat),ninv),pmat),
n.dot( n.dot(n.transpose(pmat),ninv),flux) )[0]; \
f = n.array(f)
return n.dot(pmat,f), tuple(f)
except Exception as e:
print("Exception: %r" % e)
return n.zeros(self.npixflux), (0,)
else:
return n.dot(pmat,f), tuple(f)
except Exception as e:
print("Exception: %r" % e)
return n.zeros(self.npixflux), (0,)
def fit_linear_combo(self, loc):
# Find locations of best and second best z from chi2 surfaces
chi2vec = self.bestchi2vecs[loc]
minloc = chi2vec.argmin()
if (minloc > self.width) & (minloc < (chi2vec.shape[0] - self.width)):
minlessarg = chi2vec[:minloc - self.width].argmin()
minmorearg = chi2vec[minloc + self.width:].argmin() + (
minloc + self.width
) # Argmin gives index within the slice; a value of 2 from chi2vec[minloc:].argmin() means minloc+2 was the minimum
secondminloc = minlessarg if (
chi2vec[minlessarg] < chi2vec[minmorearg]) else minmorearg
elif (minloc < self.width):
secondminloc = chi2vec[minloc + self.width:].argmin() + (minloc +
width)
else:
secondminloc = chi2vec[:minloc - self.width].argmin()
# Read in template
temps = fits.open(
join(environ['REDMONSTER_DIR'], 'templates',
eval(self.fname[loc])))[0].data
flattemps = n.reshape(temps, (-1, temps.shape[-1]))
fitchi2s = n.zeros((flattemps.shape[0], flattemps.shape[0]))
data = self.flux[loc]
ivar = self.ivar[loc]
sn2_data = n.sum((data**2) * ivar)
polyarr = poly_array(self.npoly, data.shape[0])
# Start setting up matrices for fitting
pmat = n.zeros((data.shape[0], self.npoly + 2))
pmat[:, 2], pmat[:, 3], pmat[:, 4], pmat[:, 5] = polyarr[0], polyarr[
1], polyarr[2], polyarr[3]
ninv = n.diag(ivar)
for i in range(flattemps.shape[0]): #changed from flattemps.shape[0]
print(i)
#import pdb; pdb.set_trace()
pmat[:, 0] = flattemps[i, minloc:minloc + data.shape[0]]
for j in range(
flattemps.shape[0]): #changed from flattemps.shape[0]
pmat[:,
1] = flattemps[j,
secondminloc:secondminloc + data.shape[0]]
pmattrans = n.transpose(pmat)
a = n.dot(n.dot(pmattrans, ninv), pmat)
b = n.dot(n.dot(pmattrans, ninv), data)
f = n.linalg.solve(
a, b
) # Maybe this should use nnls instead of solve to preserve physicality?
fitchi2s[i, j] = sn2_data - n.dot(n.dot(f, a), f)
return fitchi2s
def fit_linear_combo(self, loc):
# Find locations of best and second best z from chi2 surfaces
chi2vec = self.bestchi2vecs[loc]
minloc = chi2vec.argmin()
if (minloc > self.width) & (minloc < (chi2vec.shape[0]-self.width)):
minlessarg = chi2vec[:minloc-self.width].argmin()
minmorearg = chi2vec[minloc+self.width:].argmin() + (minloc+self.width) # Argmin gives index within the slice; a value of 2 from chi2vec[minloc:].argmin() means minloc+2 was the minimum
secondminloc = minlessarg if (chi2vec[minlessarg] < chi2vec[minmorearg]) else minmorearg
elif (minloc < self.width):
secondminloc = chi2vec[minloc+self.width:].argmin() + (minloc+width)
else:
secondminloc = chi2vec[:minloc-self.width].argmin()
# Read in template
temps = fits.open(join(environ['REDMONSTER_DIR'],'templates',eval(self.fname[loc])))[0].data
flattemps = n.reshape(temps, (-1,temps.shape[-1]))
fitchi2s = n.zeros( (flattemps.shape[0],flattemps.shape[0]) )
data = self.flux[loc]
ivar = self.ivar[loc]
sn2_data = n.sum( (data**2)*ivar )
polyarr = poly_array(self.npoly,data.shape[0])
# Start setting up matrices for fitting
pmat = n.zeros( (data.shape[0],self.npoly+2) )
pmat[:,2], pmat[:,3], pmat[:,4], pmat[:,5] = polyarr[0],polyarr[1],polyarr[2],polyarr[3]
ninv = n.diag(ivar)
for i in xrange(flattemps.shape[0]): #changed from flattemps.shape[0]
print i
#import pdb; pdb.set_trace()
pmat[:,0] = flattemps[i,minloc:minloc+data.shape[0]]
for j in xrange(flattemps.shape[0]): #changed from flattemps.shape[0]
pmat[:,1] = flattemps[j,secondminloc:secondminloc+data.shape[0]]
pmattrans = n.transpose(pmat)
a = n.dot(n.dot(pmattrans,ninv),pmat)
b = n.dot(n.dot(pmattrans,ninv),data)
f = n.linalg.solve(a,b) # Maybe this should use nnls instead of solve to preserve physicality?
fitchi2s[i,j] = sn2_data - n.dot(n.dot(f,a),f)
return fitchi2s
def zchi2(self, specs, specloglam, ivar, npixstep=1, chi2file=False,
plate=None, mjd=None, fiberid=None):
self.chi2file = chi2file
self.npixstep = npixstep
self.zwarning = n.zeros(specs.shape[0])
flag_val_unplugged = int('0b10000000',2)
flag_val_neg_model = int('0b1000',2)
self.create_z_baseline(specloglam[0])
if (self.zmin != None) and (self.zmax != None) and \
(self.zmax > self.zmin):
zminpix, zmaxpix = self.conv_zbounds()
self.pixoffset = zminpix
num_z = int(n.floor( (zmaxpix - zminpix) / npixstep ))
zinds = zminpix + n.arange(num_z)*npixstep
self.zbase = self.zbase[zinds]
else:
zminpix = 0
# Number of pixels to be fitted in redshift
num_z = int(n.floor( (zself.origshape[-1] - specs.shape[-1]) /
npixstep ))
# Create arrays for use in routine
# Create chi2 array of shape (# of fibers, template_parameter_1,
# ..., template_parameter_N, # of redshifts)
zchi2arr = n.zeros((specs.shape[0], self.templates_flat.shape[0],
num_z))
temp_zwarning = n.zeros(zchi2arr.shape)
# Pad data and SSPs to a power of 2 for faster FFTs
data_pad = n.zeros(specs.shape[:-1] + (self.fftnaxis1,), dtype=float)
data_pad[...,:specs.shape[-1]] = specs
ivar_pad = n.zeros(ivar.shape[:-1] + (self.fftnaxis1,), dtype=float)
ivar_pad[...,:specs.shape[-1]] = ivar
# Pre-compute FFTs for use in convolutions
data_fft = n.fft.fft(data_pad * ivar_pad)
ivar_fft = n.fft.fft(ivar_pad)
if self.npoly>0 :
# Compute poly terms, noting that they will stay fixed with
# the data - assumes data is passed in as shape (nfibers, npix)
polyarr = poly_array(self.npoly, specs.shape[1])
pmat = n.zeros( (self.npoly+1, self.npoly+1, self.fftnaxis1),
dtype=float)
bvec = n.zeros( (self.npoly+1, self.fftnaxis1), dtype=float)
# Pad to a power of 2 for faster FFTs
poly_pad = n.zeros((self.npoly, self.fftnaxis1), dtype=float)
poly_pad[...,:polyarr.shape[-1]] = polyarr
# Pre-compute FFTs for use in convolutions
poly_fft = n.zeros((ivar_pad.shape[0], self.npoly, self.fftnaxis1),dtype=complex)
for i in xrange(self.npoly):
poly_fft[:,i,:] = n.fft.fft(poly_pad[i] * ivar_pad)
# Compute z for all fibers
for i in xrange(specs.shape[0]): # Loop over fibers
start=time.time()
#print 'INFO Fitting fiber %s of %s for template %s' % \
# (i+1, specs.shape[0], self.fname)
# If flux is all zeros, flag as unplugged according to BOSS
# zwarning flags and don't bother with doing fit
if len(n.where(specs[i] != 0.)[0]) == 0:
self.zwarning[i] = int(self.zwarning[i]) | flag_val_unplugged
else: # Otherwise, go ahead and do fit
self.sn2_data.append (n.sum( (specs[i]**2)*ivar[i] ) )
if self.npoly>0 :
for ipos in xrange(self.npoly):
bvec[ipos+1] = n.sum( poly_pad[ipos] * data_pad[i] *
ivar_pad[i])
for ipos in xrange(self.npoly):
for jpos in xrange(self.npoly):
pmat[ipos+1,jpos+1] = n.sum( poly_pad[ipos] *
poly_pad[jpos] *ivar_pad[i]) # CAN GO FASTER HERE (BUT NOT LIMITING = 0.001475
f_null = n.linalg.solve(pmat[1:,1:,0],bvec[1:,0])
self.f_nulls.append( f_null )
self.chi2_null.append( self.sn2_data[i] -
n.dot(n.dot(f_null,pmat[1:,1:,0]),f_null))
else :
self.chi2_null.append( self.sn2_data[i] )
# print 'INFO Chi^2_null value is %s' % self.chi2_null[i]
# Loop over templates
# multiprocessing
func_args = []
for j in xrange(self.templates_flat.shape[0]):
if self.npoly>0 :
arguments = {"j":j,"poly_fft":poly_fft[i], "t_fft":self.t_fft[j], "t2_fft":self.t2_fft[j], "data_fft":data_fft[i], "ivar_fft":ivar_fft[i], "pmat_pol":pmat, "bvec_pol":bvec, "chi2_0":self.sn2_data[i], "chi2_null":self.chi2_null[i],"num_z":num_z, "npixstep":self.npixstep, "zminpix":zminpix,"flag_val_neg_model":flag_val_neg_model}
else :
arguments = {"j":j,"t_fft":self.t_fft[j], "t2_fft":self.t2_fft[j], "data_fft":data_fft[i], "ivar_fft":ivar_fft[i], "chi2_0":self.sn2_data[i], "num_z":num_z, "npixstep":self.npixstep, "zminpix":zminpix, "flag_val_neg_model":flag_val_neg_model}
func_args.append(arguments)
pool = multiprocessing.Pool(self.nproc)
if self.npoly>0 :
results = pool.map(_zchi2, func_args)
else :
results = pool.map(_zchi2_no_poly, func_args)
pool.close()
pool.join()
for result in results :
j = result[0]
zchi2arr[i,j] = result[1]
temp_zwarning[i,j] = result[2]
stop=time.time()
print "INFO fitted fiber %d/%d, chi2_null=%f, %d templates in %s, npoly=%d, using %d procs in %f sec"%(i+1, specs.shape[0],self.chi2_null[i],self.templates_flat.shape[0],self.fname,self.npoly,self.nproc,stop-start)
# Use only neg_model flag from best fit model/redshift and add
# it to self.zwarning
for i in xrange(self.zwarning.shape[0]):
minpos = ( n.where(zchi2arr[i] == n.min(zchi2arr[i]))[0][0],
n.where(zchi2arr[i] == n.min(zchi2arr[i]))[1][0] )
self.zwarning[i] = int(self.zwarning[i]) | \
int(temp_zwarning[i,minpos[0],minpos[1]])
zchi2arr = n.reshape(zchi2arr, (specs.shape[0],) + self.origshape[:-1] +
(num_z,) )
bestl = n.where(zchi2arr == n.min(zchi2arr))[-1][0]
thisz = ((10**(specloglam[0]))/self.tempwave[bestl+zminpix])-1
#return zchi2arr
self.zchi2arr = zchi2arr
#self.store_models(specs, ivar)
if self.chi2file is True:
if (plate is not None) & (mjd is not None) & (fiberid is not None):
write_chi2arr(plate, mjd, fiberid, self.zchi2arr)
else:
print 'WARNING Plate/mjd/fiberid not given - unable to write chi2 file!'
else:
#print 'INFO Not writing chi2'
pass
nfibers = hdu[0].header['NFIBERS']
wave = 10**(hdu[0].header['COEFF0'] + np.arange(npix)*hdu[0].header['COEFF1'])
pwave = 10**(3.0 + np.arange(10000)*0.0001)
count = np.zeros(10000)
chi2 = np.zeros(10000)
for i in xrange(1000):
print i
if hdu[1].data.ZWARNING[i] == 0:
if hdu[1].data.CLASS1[i] == 'ssp_galaxy_glob':
spec = hduplate[0].data[i]
ivar = hduplate[1].data[i]
thiswave = wave / (1+hdu[1].data.Z1[i])
pT = poly_array(npoly, npix)
ninv = np.diag(ivar)
a = linalg.solve( np.dot(np.dot(pT,ninv),np.transpose(pT)), np.dot(np.dot(pT,ninv), spec) )
pmod = np.dot(np.transpose(pT),a)
for j in xrange(w/2,npix-w/2):
pmodchi2 = np.sum(((spec[j-w/2:j+w/2+1]-pmod[j-w/2:j+w/2+1])**2)*ivar[j-w/2:j+w/2+1]) / float(w)
tmodchi2 = np.sum(((spec[j-w/2:j+w/2+1]-hdu[2].data[i,0][j-w/2:j+w/2+1])**2)*ivar[j-w/2:j+w/2+1]) / float(w)
ind = np.abs(thiswave[j] - pwave).argmin()
chi2[ind] += (pmodchi2 - tmodchi2)
count[ind] += 1
y = chi2/count
for i,val in enumerate(y):
if np.isnan(val): y[i] = 0
p.plot(pwave, y)
p.axis([pwave[0], pwave[-1], min(y)*2, max(y)*1.2])
def zchi2(self,
specs,
specloglam,
ivar,
npixstep=1,
chi2file=False,
plate=None,
mjd=None,
fiberid=None):
self.chi2file = chi2file
self.npixstep = npixstep
self.zwarning = n.zeros(specs.shape[0])
flag_val_unplugged = int('0b10000000', 2)
flag_val_neg_model = int('0b1000', 2)
self.create_z_baseline(specloglam[0])
if (self.zmin != None) and (self.zmax != None) and \
(self.zmax > self.zmin):
zminpix, zmaxpix = self.conv_zbounds()
self.pixoffset = zminpix
num_z = int(n.floor((zmaxpix - zminpix) / npixstep))
zinds = zminpix + n.arange(num_z) * npixstep
self.zbase = self.zbase[zinds]
else:
zminpix = 0
# Number of pixels to be fitted in redshift
num_z = int(
n.floor((zself.origshape[-1] - specs.shape[-1]) / npixstep))
# Create arrays for use in routine
# Create chi2 array of shape (# of fibers, template_parameter_1,
# ..., template_parameter_N, # of redshifts)
zchi2arr = n.zeros(
(specs.shape[0], self.templates_flat.shape[0], num_z))
temp_zwarning = n.zeros(zchi2arr.shape)
# Pad data and SSPs to a power of 2 for faster FFTs
data_pad = n.zeros(specs.shape[:-1] + (self.fftnaxis1, ), dtype=float)
data_pad[..., :specs.shape[-1]] = specs
ivar_pad = n.zeros(ivar.shape[:-1] + (self.fftnaxis1, ), dtype=float)
ivar_pad[..., :specs.shape[-1]] = ivar
# Pre-compute FFTs for use in convolutions
data_fft = n.fft.fft(data_pad * ivar_pad)
ivar_fft = n.fft.fft(ivar_pad)
if self.npoly > 0:
# Compute poly terms, noting that they will stay fixed with
# the data - assumes data is passed in as shape (nfibers, npix)
polyarr = poly_array(self.npoly, specs.shape[1])
pmat = n.zeros((self.npoly + 1, self.npoly + 1, self.fftnaxis1),
dtype=float)
bvec = n.zeros((self.npoly + 1, self.fftnaxis1), dtype=float)
# Pad to a power of 2 for faster FFTs
poly_pad = n.zeros((self.npoly, self.fftnaxis1), dtype=float)
poly_pad[..., :polyarr.shape[-1]] = polyarr
# Pre-compute FFTs for use in convolutions
poly_fft = n.zeros((ivar_pad.shape[0], self.npoly, self.fftnaxis1),
dtype=complex)
for i in range(self.npoly):
poly_fft[:, i, :] = n.fft.fft(poly_pad[i] * ivar_pad)
# Compute z for all fibers
for i in range(specs.shape[0]): # Loop over fibers
start = time.time()
#print 'INFO Fitting fiber %s of %s for template %s' % \
# (i+1, specs.shape[0], self.fname)
# If flux is all zeros, flag as unplugged according to BOSS
# zwarning flags and don't bother with doing fit
if len(n.where(specs[i] != 0.)[0]) == 0:
self.zwarning[i] = int(self.zwarning[i]) | flag_val_unplugged
else: # Otherwise, go ahead and do fit
self.sn2_data.append(n.sum((specs[i]**2) * ivar[i]))
if self.npoly > 0:
for ipos in range(self.npoly):
bvec[ipos + 1] = n.sum(poly_pad[ipos] * data_pad[i] *
ivar_pad[i])
for ipos in range(self.npoly):
for jpos in range(self.npoly):
pmat[ipos + 1, jpos + 1] = n.sum(
poly_pad[ipos] * poly_pad[jpos] * ivar_pad[i]
) # CAN GO FASTER HERE (BUT NOT LIMITING = 0.001475
f_null = linalg.solve(pmat[1:, 1:, 0], bvec[1:, 0])
self.f_nulls.append(f_null)
self.chi2_null.append(
self.sn2_data[i] -
n.dot(n.dot(f_null, pmat[1:, 1:, 0]), f_null))
else:
self.chi2_null.append(self.sn2_data[i])
# print 'INFO Chi^2_null value is %s' % self.chi2_null[i]
# Loop over templates
# multiprocessing
func_args = []
for j in range(self.templates_flat.shape[0]):
if self.npoly > 0:
arguments = {
"j": j,
"poly_fft": poly_fft[i],
"t_fft": self.t_fft[j],
"t2_fft": self.t2_fft[j],
"data_fft": data_fft[i],
"ivar_fft": ivar_fft[i],
"pmat_pol": pmat,
"bvec_pol": bvec,
"chi2_0": self.sn2_data[i],
"chi2_null": self.chi2_null[i],
"num_z": num_z,
"npixstep": self.npixstep,
"zminpix": zminpix,
"flag_val_neg_model": flag_val_neg_model
}
else:
arguments = {
"j": j,
"t_fft": self.t_fft[j],
"t2_fft": self.t2_fft[j],
"data_fft": data_fft[i],
"ivar_fft": ivar_fft[i],
"chi2_0": self.sn2_data[i],
"num_z": num_z,
"npixstep": self.npixstep,
"zminpix": zminpix,
"flag_val_neg_model": flag_val_neg_model
}
func_args.append(arguments)
results = None
if self.nproc > 1:
pool = multiprocessing.Pool(self.nproc)
if self.npoly > 0:
results = pool.map(_zchi2, func_args)
else:
results = pool.map(_zchi2_no_poly, func_args)
pool.close()
pool.join()
else:
if self.npoly > 0:
results = [_zchi2(x) for x in func_args]
else:
results = [_zchi2_no_poly(x) for x in func_args]
for result in results:
j = result[0]
zchi2arr[i, j] = result[1]
temp_zwarning[i, j] = result[2]
stop = time.time()
print(
"INFO fitted spectrum %d/%d, chi2_null=%f, %d templates in %s, npoly=%d, using %d procs in %f sec"
% (i + 1, specs.shape[0], self.chi2_null[i],
self.templates_flat.shape[0], self.fname, self.npoly,
self.nproc, stop - start))
# Use only neg_model flag from best fit model/redshift and add
# it to self.zwarning
for i in range(self.zwarning.shape[0]):
minpos = (n.where(zchi2arr[i] == n.min(zchi2arr[i]))[0][0],
n.where(zchi2arr[i] == n.min(zchi2arr[i]))[1][0])
self.zwarning[i] = int(self.zwarning[i]) | \
int(temp_zwarning[i,minpos[0],minpos[1]])
zchi2arr = n.reshape(zchi2arr, (specs.shape[0], ) +
self.origshape[:-1] + (num_z, ))
bestl = n.where(zchi2arr == n.min(zchi2arr))[-1][0]
thisz = ((10**(specloglam[0])) / self.tempwave[bestl + zminpix]) - 1
#return zchi2arr
self.zchi2arr = zchi2arr
#self.store_models(specs, ivar)
if self.chi2file is True:
if (plate is not None) & (mjd is not None) & (fiberid is not None):
write_chi2arr(plate, mjd, fiberid, self.zchi2arr)
else:
print(
'WARNING Plate/mjd/fiberid not given - unable to write chi2 file!'
)
else:
print('INFO Not writing chi2')
ztemp.zchi2(specs.flux, specs.loglambda, specs.ivar, npixstep=1)
zfit_temp = zfitter.ZFitter(ztemp.zchi2arr, ztemp.zbase)
zfit_temp.z_refine()
#temp_flags = misc.comb_flags(specs, ztemp, zfit_temp)
#zpick = zpicker.ZPicker(specs, ztemp, zfit_temp)
# Solve for parameters, create model
import pdb
pdb.set_trace()
minloc = n.unravel_index(ztemp.zchi2arr.argmin(), ztemp.zchi2arr.shape)
pmat = n.zeros((specs.flux.shape[-1], ztemp.npoly + 1))
this_temp = ztemp.templates[minloc[1:-1]]
pmat[:, 0] = this_temp[(minloc[-1] * ztemp.npixstep) +
ztemp.pixoffset:(minloc[-1] * ztemp.npixstep) +
ztemp.pixoffset + specs.flux.shape[-1]]
polyarr = poly_array(ztemp.npoly, specs.flux.shape[-1])
pmat[:, 1:] = n.transpose(polyarr)
ninv = n.diag(specs.ivar[0])
f = n.linalg.solve(n.dot(n.dot(n.transpose(pmat), ninv), pmat),
n.dot(n.dot(n.transpose(pmat), ninv), specs.flux[0]))
model = n.dot(pmat, f)
# Make plot
p.plot(10**specs.loglambda, specs.flux[0], 'r', label='Data')
p.plot(10**specs.loglambda, model, 'k', label='Model', hold=True)
p.title('Plate %s Fiber %s, z=%.4f' % (plate, fiberid[0], zfit_temp.z[0][0]),
size=18)
p.xlabel(r'Wavelength ($\AA$)', size=16)
p.ylabel(r'Flux ($10^{-17}$ erg s$^{-1}$cm$^{-2}$$\AA^{-1}$)', size=16)
p.legend()