def z_refine(self, threshold=23.3, width=15):
# Default threshold of 23.3 is delta (chi_r)**2 = .005, and
# width is 1000 km/s
self.threshold = threshold
self.width = width
for ifiber in range(self.zchi2.shape[0]):
self.minvector.append((ifiber, ) + n.unravel_index(
self.zchi2[ifiber].argmin(), self.zchi2[ifiber].shape))
bestzvec = n.zeros(self.zchi2.shape[-1])
for iz in range(self.zchi2.shape[-1]):
bestzvec[iz] = n.min(self.zchi2[ifiber, ..., iz])
posinvec = n.where(bestzvec == n.min(bestzvec))[0][0]
# Flag and skip interpolation fit if best chi2 is at edge of z-range
if (posinvec == 0) or (posinvec == bestzvec.shape[0] - 1):
self.flag_z_fitlimit(ifiber)
self.z[ifiber] = -1.
self.z_err[ifiber] = -1.
else:
# Find best redshift using global chi2 minimum
xp = n.linspace(self.zbase[posinvec - 1],
self.zbase[posinvec + 1], 1000)
f = quadfit(self.zbase[posinvec - 1:posinvec + 2],
bestzvec[posinvec - 1:posinvec + 2])
fit = quad_for_fit(xp, f[0], f[1], f[2])
#p.plot(xp, fit, color='red', hold=False)
#p.plot(self.zbase[posinvec-1:posinvec+2],
#bestzvec[posinvec-1:posinvec+2], 'ko', hold=True)
self.z[ifiber, 0] = xp[n.where(fit == n.min(fit))[0][0]]
self.z_err[ifiber, 0] = self.estimate_z_err(xp, fit)
# Find second- and third-, and fourth-best redshifts
zspline = gs.GridSpline(bestzvec)
zminlocs = n.round(zspline.get_min())
zminvals = zspline.get_val(zminlocs)
if len(zminvals) == 1:
self.z[ifiber, 1] = -1.
self.z_err[ifiber, 1] = -1.
self.z[ifiber, 2] = -1
self.z_err[ifiber, 2] = -1
self.z[ifiber, 3] = -1
self.z_err[ifiber, 3] = -1
self.z[ifiber, 4] = -1
self.z_err[ifiber, 4] = -1
elif len(zminvals) == 2:
self.z[ifiber, 2] = -1
self.z_err[ifiber, 2] = -1
self.z[ifiber, 3] = -1
self.z_err[ifiber, 3] = -1
self.z[ifiber, 4] = -1
self.z_err[ifiber, 4] = -1
elif len(zminvals) == 3:
self.z[ifiber, 3] = -1
self.z_err[ifiber, 3] = -1
self.z[ifiber, 4] = -1
self.z_err[ifiber, 4] = -1
elif len(zminvals) == 4:
self.z[ifiber, 4] = -1
self.z_err[ifiber, 4] = -1
imin = 0
while (self.z[ifiber,1] == 0) | (self.z[ifiber,2] == 0) | \
(self.z[ifiber,3] == 0) | (self.z[ifiber,4] == 0):
imin += 1
nextpos = zminlocs[n.where(
zminvals == n.sort(zminvals)[imin])[0][0]]
if (abs(posinvec - nextpos) > width) & (nextpos != 0) & \
(nextpos != bestzvec.shape[0]-1) :
xp = n.linspace(self.zbase[nextpos - 1],
self.zbase[nextpos + 1], 1000)
f = quadfit(self.zbase[nextpos - 1:nextpos + 2],
bestzvec[nextpos - 1:nextpos + 2])
fit = quad_for_fit(xp, f[0], f[1], f[2])
self.z[ifiber,
imin] = xp[n.where(fit == n.min(fit))[0][0]]
self.z_err[ifiber, imin] = self.estimate_z_err(xp, fit)
else:
self.z[ifiber, imin] = -1.
self.z_err[ifiber, imin] = -1.
# Flag fibers with small delta chi2 in redshift
self.flag_small_dchi2(ifiber,
bestzvec,
threshold=threshold,
width=width)
def z_refine(self, threshold=23.3, width=15):
# Default threshold of 23.3 is delta (chi_r)**2 = .005, and
# width is 1000 km/s
self.threshold = threshold
self.width = width
for ifiber in range(self.zchi2.shape[0]):
self.minvector.append( (ifiber,) +
n.unravel_index(self.zchi2[ifiber].argmin(),
self.zchi2[ifiber].shape))
bestzvec = n.zeros( self.zchi2.shape[-1])
for iz in range(self.zchi2.shape[-1]):
bestzvec[iz] = n.min( self.zchi2[ifiber,...,iz] )
posinvec = n.where( bestzvec == n.min(bestzvec) )[0][0]
# Flag and skip interpolation fit if best chi2 is at edge of z-range
if (posinvec == 0) or (posinvec == bestzvec.shape[0]-1):
self.flag_z_fitlimit(ifiber)
self.z[ifiber] = -1.
self.z_err[ifiber] = -1.
else:
# Find best redshift using global chi2 minimum
xp = n.linspace(self.zbase[posinvec-1], self.zbase[posinvec+1],
1000)
f = quadfit(self.zbase[posinvec-1:posinvec+2],
bestzvec[posinvec-1:posinvec+2])
fit = quad_for_fit(xp, f[0], f[1], f[2])
#p.plot(xp, fit, color='red', hold=False)
#p.plot(self.zbase[posinvec-1:posinvec+2],
#bestzvec[posinvec-1:posinvec+2], 'ko', hold=True)
self.z[ifiber,0] = xp[n.where(fit == n.min(fit))[0][0]]
self.z_err[ifiber,0] = self.estimate_z_err(xp, fit)
# Find second- and third-, and fourth-best redshifts
zspline = gs.GridSpline(bestzvec)
zminlocs = n.round(zspline.get_min())
zminvals = zspline.get_val(zminlocs)
if len(zminvals) == 1:
self.z[ifiber,1] = -1.
self.z_err[ifiber,1] = -1.
self.z[ifiber,2] = -1
self.z_err[ifiber,2] = -1
self.z[ifiber,3] = -1
self.z_err[ifiber,3] = -1
self.z[ifiber,4] = -1
self.z_err[ifiber,4] = -1
elif len(zminvals) == 2:
self.z[ifiber,2] = -1
self.z_err[ifiber,2] = -1
self.z[ifiber,3] = -1
self.z_err[ifiber,3] = -1
self.z[ifiber,4] = -1
self.z_err[ifiber,4] = -1
elif len(zminvals) == 3:
self.z[ifiber,3] = -1
self.z_err[ifiber,3] = -1
self.z[ifiber,4] = -1
self.z_err[ifiber,4] = -1
elif len(zminvals) == 4:
self.z[ifiber,4] = -1
self.z_err[ifiber,4] = -1
imin = 0
while (self.z[ifiber,1] == 0) | (self.z[ifiber,2] == 0) | \
(self.z[ifiber,3] == 0) | (self.z[ifiber,4] == 0):
imin += 1
nextpos = zminlocs[n.where(
zminvals == n.sort(zminvals)[imin])[0][0]]
if (abs(posinvec - nextpos) > width) & (nextpos != 0) & \
(nextpos != bestzvec.shape[0]-1) :
xp = n.linspace(self.zbase[nextpos-1],
self.zbase[nextpos+1], 1000)
f = quadfit(self.zbase[nextpos-1:nextpos+2],
bestzvec[nextpos-1:nextpos+2])
fit = quad_for_fit(xp, f[0], f[1], f[2])
self.z[ifiber,imin] = xp[n.where(
fit == n.min(fit))[0][0]]
self.z_err[ifiber,imin] = self.estimate_z_err(xp, fit)
else:
self.z[ifiber,imin] = -1.
self.z_err[ifiber,imin] = -1.
# Flag fibers with small delta chi2 in redshift
self.flag_small_dchi2(ifiber, bestzvec, threshold=threshold,
width=width)
def z_refine2(self, threshold=23.3, width=15, num_z=5):
# Default threshold of 23.3 is delta (chi_r)**2 = .005,
# and width is 1000 km/s
self.num_z = num_z
self.threshold = threshold
self.width = width
self.z = n.zeros((self.zchi2.shape[0], num_z))
self.z_err = n.zeros((self.zchi2.shape[0], num_z))
self.minvectors = []
self.chi2vals = []
for ifiber in range(self.zchi2.shape[0]):
allminvectors = []
bestminvectors = []
bestchi2vals = []
bestzvec = n.zeros(self.zchi2.shape[-1])
# Make vector of minimum chi2 at each trial redshift
for iz in range(self.zchi2.shape[-1]):
bestzvec[iz] = n.min(self.zchi2[ifiber, ..., iz])
# Location in zchi2[ifiber,...,iz] of min
allminvectors.append(
n.unravel_index(self.zchi2[ifiber, ..., iz].argmin(),
self.zchi2[ifiber, ..., iz].shape))
zspline = gs.GridSpline(bestzvec) # Spline up bestzvec
zminlocs = list(map(int, n.round(
zspline.get_min()))) # Minimun locations
zminvals = zspline.get_val(zminlocs) # Minimum values
z_ind = 0
stop = False
#import pdb; pdb.set_trace()
if len(zminvals) == 0:
self.flag_null_fit(ifiber)
self.z[ifiber] = -1.
self.z_err[ifiber] = -1.
bestminvectors = [(-1, )] * num_z
bestchi2vals = [n.max(bestzvec)] * num_z
else:
while (z_ind < num_z) & (stop == False):
# Location in zminvals vector of minimum
thisminloc = zminvals.argmin()
# Location in bestzvec of minimum
posinvec = zminlocs[thisminloc]
# Flag and skip interpretation if best fit chi2 is
# at edge of z-range
if (posinvec == 0) or (posinvec == bestzvec.shape[0] - 1):
# If it's the first redshift, set all num_z
# redshifts and errors to -1
if z_ind == 0:
self.flag_z_fitlimit(ifiber)
self.z[ifiber] = -1.
self.z_err[ifiber] = -1.
bestminvectors = [(-1, )] * num_z
bestchi2vals = [n.max(bestzvec)] * num_z
stop = True
# If it's not the first, and if it's on the
# left edge, set the 15 pixels to the right to
# n.max(bestzvec) and move on
elif posinvec == 0:
k = 0
while True:
k += 1
try:
if n.abs(zminlocs[thisminloc+k] - \
zminlocs[thisminloc]) < self.width:
zminvals[thisminloc +
k] = n.max(bestzvec)
else:
break
except IndexError:
break
z_ind += 1
# If it's not the first, and if it's on the
# right edge, set the 15 pixels to the left =
# n.max(bestzvec) and move on
elif posinvec == bestzvec.shape[0] - 1:
k = 0
while True:
k += 1
try:
if n.abs(zminlocs[thisminloc-k] - \
zminlocs[thisminloc]) < self.width:
zminvals[thisminloc -
k] = n.max(bestzvec)
else:
break
except IndexError:
break
z_ind += 1
# Fit with quadratic and find minimum and error
else:
xp = n.linspace(self.zbase[posinvec - 1],
self.zbase[posinvec + 1], 1000)
f = quadfit(self.zbase[posinvec - 1:posinvec + 2],
bestzvec[posinvec - 1:posinvec + 2])
fit = quad_for_fit(xp, f[0], f[1], f[2])
if not xp[n.where(fit == n.min(fit))[0][0]] in \
self.z[ifiber]:
self.z[ifiber, z_ind] = xp[n.where(
fit == n.min(fit))[0][0]]
self.z_err[ifiber,
z_ind] = self.estimate_z_err(xp, fit)
bestchi2vals.append(bestzvec[posinvec])
bestminvectors.append(allminvectors[posinvec] +
(posinvec, ))
else:
self.z[ifiber, z_ind] = -1.
self.z_err[ifiber, z_ind] = -1.
bestchi2vals.append(n.max(bestzvec))
bestminvectors.append((-1, ))
zminvals[thisminloc] = n.max(bestzvec)
k = 0
# Set width points to the right and left of minimum
# to n.max(bestzvec)
while True:
k += 1
try:
if n.abs(zminlocs[thisminloc+k] - \
zminlocs[thisminloc]) < self.width:
zminvals[thisminloc + k] = n.max(bestzvec)
else:
break
except IndexError:
break
k = 0
while True:
k += 1
try:
if n.abs(zminlocs[thisminloc-k] - \
zminlocs[thisminloc]) < self.width:
zminvals[thisminloc - k] = n.max(bestzvec)
else:
break
except IndexError:
break
z_ind += 1
self.minvectors.append(bestminvectors)
self.chi2vals.append(bestchi2vals)
self.flag_small_dchi2_2()
def z_refine2(self, threshold=23.3, width=15, num_z=5):
# Default threshold of 23.3 is delta (chi_r)**2 = .005,
# and width is 1000 km/s
self.num_z = num_z
self.threshold = threshold
self.width = width
self.z = n.zeros((self.zchi2.shape[0],num_z))
self.z_err = n.zeros((self.zchi2.shape[0],num_z))
self.minvectors = []
self.chi2vals = []
for ifiber in range(self.zchi2.shape[0]):
allminvectors = []
bestminvectors = []
bestchi2vals = []
bestzvec = n.zeros( self.zchi2.shape[-1] )
# Make vector of minimum chi2 at each trial redshift
for iz in range(self.zchi2.shape[-1]):
bestzvec[iz] = n.min( self.zchi2[ifiber,...,iz] )
# Location in zchi2[ifiber,...,iz] of min
allminvectors.append( n.unravel_index(
self.zchi2[ifiber,...,iz].argmin(),
self.zchi2[ifiber,...,iz].shape ) )
zspline = gs.GridSpline(bestzvec) # Spline up bestzvec
zminlocs = list(map(int,n.round(zspline.get_min()))) # Minimun locations
zminvals = zspline.get_val(zminlocs) # Minimum values
z_ind = 0
stop = False
#import pdb; pdb.set_trace()
if len(zminvals) == 0:
self.flag_null_fit(ifiber)
self.z[ifiber] = -1.
self.z_err[ifiber] = -1.
bestminvectors = [(-1,)]*num_z
bestchi2vals = [n.max(bestzvec)]*num_z
else:
while (z_ind < num_z) & (stop == False):
# Location in zminvals vector of minimum
thisminloc = zminvals.argmin()
# Location in bestzvec of minimum
posinvec = zminlocs[thisminloc]
# Flag and skip interpretation if best fit chi2 is
# at edge of z-range
if (posinvec == 0) or (posinvec == bestzvec.shape[0]-1):
# If it's the first redshift, set all num_z
# redshifts and errors to -1
if z_ind == 0:
self.flag_z_fitlimit(ifiber)
self.z[ifiber] = -1.
self.z_err[ifiber] = -1.
bestminvectors = [(-1,)]*num_z
bestchi2vals = [n.max(bestzvec)]*num_z
stop = True
# If it's not the first, and if it's on the
# left edge, set the 15 pixels to the right to
# n.max(bestzvec) and move on
elif posinvec == 0:
k = 0
while True:
k += 1
try:
if n.abs(zminlocs[thisminloc+k] - \
zminlocs[thisminloc]) < self.width:
zminvals[thisminloc+k] = n.max(bestzvec)
else:
break
except IndexError:
break
z_ind += 1
# If it's not the first, and if it's on the
# right edge, set the 15 pixels to the left =
# n.max(bestzvec) and move on
elif posinvec == bestzvec.shape[0]-1:
k = 0
while True:
k += 1
try:
if n.abs(zminlocs[thisminloc-k] - \
zminlocs[thisminloc]) < self.width:
zminvals[thisminloc-k] = n.max(bestzvec)
else:
break
except IndexError:
break
z_ind += 1
# Fit with quadratic and find minimum and error
else:
xp = n.linspace(self.zbase[posinvec-1],
self.zbase[posinvec+1], 1000)
f = quadfit(self.zbase[posinvec-1:posinvec+2],
bestzvec[posinvec-1:posinvec+2])
fit = quad_for_fit(xp, f[0], f[1], f[2])
if not xp[n.where(fit == n.min(fit))[0][0]] in \
self.z[ifiber]:
self.z[ifiber,z_ind] = xp[n.where(fit ==
n.min(fit))[0][0]]
self.z_err[ifiber,z_ind] = self.estimate_z_err(
xp, fit)
bestchi2vals.append( bestzvec[posinvec] )
bestminvectors.append( allminvectors[posinvec] +
(posinvec,) )
else:
self.z[ifiber,z_ind] = -1.
self.z_err[ifiber,z_ind] = -1.
bestchi2vals.append(n.max(bestzvec))
bestminvectors.append((-1,))
zminvals[thisminloc] = n.max(bestzvec)
k = 0
# Set width points to the right and left of minimum
# to n.max(bestzvec)
while True:
k += 1
try:
if n.abs(zminlocs[thisminloc+k] - \
zminlocs[thisminloc]) < self.width:
zminvals[thisminloc+k] = n.max(bestzvec)
else:
break
except IndexError:
break
k = 0
while True:
k += 1
try:
if n.abs(zminlocs[thisminloc-k] - \
zminlocs[thisminloc]) < self.width:
zminvals[thisminloc-k] = n.max(bestzvec)
else:
break
except IndexError:
break
z_ind += 1
self.minvectors.append( bestminvectors )
self.chi2vals.append( bestchi2vals )
self.flag_small_dchi2_2()
