def find_segments_helper(seg_cnt):
# Global is for inter process communication
global segdat, objdat, polyorder
SegTrace = namedtuple("Trace", "segnum xs ys poly")
PAD = 2
the_seg = (segdat == seg_cnt)
span = spanrange(the_seg)
mnsr = np.max((span[0].y - PAD, 0))
mxsr = np.min((span[1].y + PAD, segdat.shape[1]-1))
y_slc = slice(mnsr, mxsr)
y_off = (span[0].y+span[1].y)/2.0
n_el = span[1].x - span[0].x
if n_el < 50:
tr = {"seg_cnt": seg_cnt, "xs": np.array(np.nan),
"mean_ys": np.array(np.nan),
"coeff_ys": np.array(np.nan),
"trace_sigma": np.nan,
"ok": False}
return tr
means = np.zeros(n_el)
amps = np.zeros(n_el)
sds = np.zeros(n_el)
trace_profile = np.zeros(mxsr-mnsr)
for i in xrange(n_el):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
XX = i+span[0].x
profile = np.median(objdat[y_slc, XX-3:XX+3], 1)
profile -= np.min(profile)
trace_profile += profile
xs = np.arange(len(profile)) + span[0].y
means[i] = np.sum(xs*profile)/np.sum(profile)-PAD
xs = np.arange(n_el) + span[0].x
poly = np.polyfit(xs, means, polyorder)
tracefit = gfit1d(trace_profile, par=[0, len(trace_profile)/2., 1.7], quiet=1)
tr = {"seg_cnt": seg_cnt, "xs": np.array(xs), "mean_ys": np.array(means),
"coeff_ys": np.array(poly), "ok": True, "trace_sigma": np.abs(tracefit.params[2])}
outstr = '\r%4.4i: fwhm=%3.2f pix' % (seg_cnt, np.abs(tracefit.params[2])*2.355)
print outstr,
sys.stdout.flush()
return tr
if plot:
pl.plot(xs, means, 'x')
pl.plot(xs, ff(xs))
def gFitTrace(self, specimage, y1, y2):
"""
Fit a gaussian to each column of an image.
"""
sizex, sizey = specimage.shape
smoytrace = np.zeros(sizey).astype(np.float)
boxcar_kernel = signal.boxcar(3) / 3.0
for c in np.arange(sizey):
col = specimage[:, c]
col = col - np.median(col)
smcol = ni.convolve(col, boxcar_kernel).astype(np.float)
fit = gfit.gfit1d(smcol, quiet=1, maxiter=15)
smoytrace[c] = fit.params[1]
return np.array(smoytrace)
def gFitTrace(self, specimage, y1, y2):
"""
Fit a gaussian to each column of an image.
"""
sizex, sizey = specimage.shape
smoytrace = np.zeros(sizey).astype(np.float)
boxcar_kernel = signal.boxcar(3) / 3.0
for c in np.arange(sizey):
col = specimage[:, c]
col = col - np.median(col)
smcol = ni.convolve(col, boxcar_kernel).astype(np.float)
fit = gfit.gfit1d(smcol, quiet=1, maxiter=15)
smoytrace[c] = fit.params[1]
return np.array(smoytrace)
def find_segments_helper(seg_cnt):
# Global is for inter process communication
global segdat, objdat, polyorder
SegTrace = namedtuple("Trace", "segnum xs ys poly")
PAD = 2
the_seg = (segdat == seg_cnt)
span = spanrange(the_seg)
mnsr = np.max((span[0].y - PAD, 0))
mxsr = np.min((span[1].y + PAD, segdat.shape[1] - 1))
y_slc = slice(mnsr, mxsr)
y_off = (span[0].y + span[1].y) / 2.0
n_el = span[1].x - span[0].x
if n_el < 50:
tr = {
"seg_cnt": seg_cnt,
"xs": np.array(np.nan),
"mean_ys": np.array(np.nan),
"coeff_ys": np.array(np.nan),
"trace_sigma": np.nan,
"ok": False
}
return tr
means = np.zeros(n_el)
amps = np.zeros(n_el)
sds = np.zeros(n_el)
trace_profile = np.zeros(mxsr - mnsr)
for i in xrange(n_el):
XX = i + span[0].x
profile = np.median(objdat[y_slc, XX - 3:XX + 3], 1)
profile -= np.min(profile)
trace_profile += profile
xs = np.arange(len(profile)) + span[0].y
means[i] = np.sum(xs * profile) / np.sum(profile) - PAD
xs = np.arange(n_el) + span[0].x
poly = np.polyfit(xs, means, polyorder)
tracefit = gfit1d(trace_profile,
par=[0, len(trace_profile) / 2., 1.7],
quiet=1)
tr = {
"seg_cnt": seg_cnt,
"xs": np.array(xs),
"mean_ys": np.array(means),
"coeff_ys": np.array(poly),
"ok": True,
"trace_sigma": np.abs(tracefit.params[2])
}
print '%4.4i: fwhm=%3.2f pix' % (seg_cnt,
np.abs(tracefit.params[2]) * 2.355)
return tr
if plot:
pl.plot(xs, means, 'x')
pl.plot(xs, ff(xs))
def find_segments_helper(seg_cnt):
"""Trace a single spaxel segment and return the solution
Args:
seg_cnt (int): the segment number
Returns:
dict: Returns a segmentation map Dictionary
containing::
{ "seg_cnt": Segment ID number,
"xs": List of x positions of trace,
"mean_ys": Measured Y position (average) of the trace,
"coeff_ys": polyfit coefficients to the mean_ys,
"trace_sigma": Width of trace in pixels (1 sigma),
"ok": Trace has more than 50 pixels }
"""
# Global is for inter process communication
global segdat, objdat, polyorder
# Padding in pixels around trace in Y
PAD = 2
# Get this segment
the_seg = (segdat == seg_cnt)
# Test for tiny segment
test = the_seg.nonzero()
if len(test[0]) < 10 or len(test[1]) < 10:
tr = {
"seg_cnt": seg_cnt,
"xs": np.array(np.nan),
"mean_ys": np.array(np.nan),
"coeff_ys": np.array(np.nan),
"trace_sigma": np.array(np.nan),
"ok": False
}
outstr = '\r%4.4i: %4.4i, TINY SEGMENT, %-5s' % (seg_cnt, len(test[0]),
tr['ok'])
print outstr
sys.stdout.flush()
# We are OK
else:
# Get segment's geometry
span = spanrange(the_seg)
mnsr = np.max((span[0].y - PAD, 0))
mxsr = np.min((span[1].y + PAD, segdat.shape[1] - 1))
y_slc = slice(mnsr, mxsr)
# y_off = (span[0].y + span[1].y) / 2.0
# How long is it in X?
n_el = span[1].x - span[0].x
# Don't fit short traces, but flag them by setting "ok" to False
if n_el < 50:
# Flag the sigma with zero
sig = 0.
# Load up the trace with NaNs
tr = {
"seg_cnt": seg_cnt,
"xs": np.array(np.nan),
"mean_ys": np.array(np.nan),
"coeff_ys": np.array(np.nan),
"trace_sigma": np.array(np.nan),
"ok": False
}
# Trace is long enough, so let's fit it!
else:
means = np.zeros(n_el)
trace_profile = np.zeros(mxsr - mnsr)
for i in xrange(n_el):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
XX = i + span[0].x
profile = np.median(objdat[y_slc, XX - 3:XX + 3], 1)
profile -= np.min(profile)
trace_profile += profile
xs = np.arange(len(profile)) + span[0].y
means[i] = np.sum(xs * profile) / np.sum(profile) - PAD
xs = np.arange(n_el) + span[0].x
poly = np.array(np.polyfit(xs, means, polyorder))
tracefit = gfit1d(trace_profile,
par=[0, len(trace_profile) / 2., 1.7], quiet=1)
sig = np.abs(tracefit.params[2])
tr = {
"seg_cnt": seg_cnt,
"xs": np.array(xs),
"mean_ys": np.array(means),
"coeff_ys": poly,
"trace_sigma": sig,
"ok": True
}
outstr = '\r%4.4i: %4.4i, fwhm=%3.2f pix, %-5s' % (seg_cnt, n_el,
sig * 2.355, tr['ok'])
print outstr,
sys.stdout.flush()
return tr
def find_segments_helper(seg_cnt):
"""Trace a single spaxel segment and return the solution
Args:
seg_cnt (int): the segment number
Returns:
dict: Returns a segmentation map Dictionary
containing::
{ "seg_cnt": Segment ID number,
"xs": List of x positions of trace,
"mean_ys": Measured Y position (average) of the trace,
"coeff_ys": polyfit coefficients to the mean_ys,
"trace_sigma": Width of trace in pixels (1 sigma),
"ok": Trace has more than 50 pixels }
"""
# Global is for inter process communication
global segdat, objdat, polyorder
# Padding in pixels around trace in Y
PAD = 2
# Get this segment
the_seg = (segdat == seg_cnt)
# Test for tiny segment
test = the_seg.nonzero()
if len(test[0]) < 10 or len(test[1]) < 10:
tr = {
"seg_cnt": seg_cnt,
"xs": np.array(np.nan),
"mean_ys": np.array(np.nan),
"coeff_ys": np.array(np.nan),
"trace_sigma": np.array(np.nan),
"ok": False
}
outstr = '\r%4.4i: %4.4i, TINY SEGMENT, %-5s' % (seg_cnt, len(
test[0]), tr['ok'])
print outstr
sys.stdout.flush()
# We are OK
else:
# Get segment's geometry
span = spanrange(the_seg)
mnsr = np.max((span[0].y - PAD, 0))
mxsr = np.min((span[1].y + PAD, segdat.shape[1] - 1))
y_slc = slice(mnsr, mxsr)
# y_off = (span[0].y + span[1].y) / 2.0
# How long is it in X?
n_el = span[1].x - span[0].x
# Don't fit short traces, but flag them by setting "ok" to False
if n_el < 50:
# Flag the sigma with zero
sig = 0.
# Load up the trace with NaNs
tr = {
"seg_cnt": seg_cnt,
"xs": np.array(np.nan),
"mean_ys": np.array(np.nan),
"coeff_ys": np.array(np.nan),
"trace_sigma": np.array(np.nan),
"ok": False
}
# Trace is long enough, so let's fit it!
else:
means = np.zeros(n_el)
trace_profile = np.zeros(mxsr - mnsr)
for i in xrange(n_el):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
XX = i + span[0].x
profile = np.median(objdat[y_slc, XX - 3:XX + 3], 1)
profile -= np.min(profile)
trace_profile += profile
xs = np.arange(len(profile)) + span[0].y
means[i] = np.sum(xs * profile) / np.sum(profile) - PAD
xs = np.arange(n_el) + span[0].x
poly = np.array(np.polyfit(xs, means, polyorder))
tracefit = gfit1d(trace_profile,
par=[0, len(trace_profile) / 2., 1.7],
quiet=1)
sig = np.abs(tracefit.params[2])
tr = {
"seg_cnt": seg_cnt,
"xs": np.array(xs),
"mean_ys": np.array(means),
"coeff_ys": poly,
"trace_sigma": sig,
"ok": True
}
outstr = '\r%4.4i: %4.4i, fwhm=%3.2f pix, %-5s' % (seg_cnt, n_el, sig *
2.355, tr['ok'])
print outstr,
sys.stdout.flush()
return tr
