def compute_fwhm_2d_simple(img, xc, yc):
X = np.arange(0, img.shape[1], 1.0)
Y = np.arange(0, img.shape[0], 1.0)
xpix = wc_to_pix_1d(xc - X[0])
ypix = wc_to_pix_1d(yc - Y[0])
peak = img[ypix, xpix]
res11 = img[ypix, :]
res22 = img[:, xpix]
fwhm_x, _codex, _msgx = compute_fwhm_1d(X, res11 - 0.5 * peak, xc, xpix)
fwhm_y, _codey, _msgy = compute_fwhm_1d(Y, res22 - 0.5 * peak, yc, ypix)
return peak, fwhm_x, fwhm_y
def compute_fw_at_frac_max_1d_simple(Y, xc, X=None, f=0.5):
'''Compute the full width at fraction f of the maximum'''
if X is None:
X = np.arange(Y.shape[0])
xpix = wc_to_pix_1d(xc - X[0])
peak = Y[xpix]
fwhm_x, _codex, _msgx = compute_fwhm_1d(X, Y - f * peak, xc, xpix)
return peak, fwhm_x
def compute_fwhm_2d_spline(img, xc, yc):
Y = np.arange(0.0, img.shape[1], 1.0)
X = np.arange(0.0, img.shape[0], 1.0)
xpix = wc_to_pix_1d(xc)
ypix = wc_to_pix_1d(yc)
# The image is already cropped
bb = itpl.RectBivariateSpline(X, Y, img)
# We assume that the peak is in the center...
peak = bb(xc, yc)[0, 0]
U = X
V = bb.ev(U, [yc for _ in U]) - 0.5 * peak
fwhm_x, _codex, _msgx = compute_fwhm_1d(U, V, yc, ypix)
U = Y
V = bb.ev([xc for _ in U], U) - 0.5 * peak
fwhm_y, _codey, _msgy = compute_fwhm_1d(U, V, xc, xpix)
return peak, fwhm_x, fwhm_y
def run(self, rinput):
self.logger.info('starting processing for bars detection')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
hdr = hdulist[0].header
self.set_base_headers(hdr)
try:
rotang = hdr['ROTANG']
tsutc1 = hdr['TSUTC1']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
csupos = datamodel.get_csup_from_header(hdr)
csusens = datamodel.get_cs_from_header(hdr)
except KeyError as error:
self.logger.error(error)
raise RecipeError(error)
self.logger.debug('finding bars')
# Processed array
arr = hdulist[0].data
# Median filter of processed array (two times)
mfilter_size = rinput.median_filter_size
self.logger.debug('median filtering X, %d columns', mfilter_size)
arr_median = median_filter(arr, size=(1, mfilter_size))
self.logger.debug('median filtering X, %d rows', mfilter_size)
arr_median = median_filter(arr_median, size=(mfilter_size, 1))
# Median filter of processed array (two times) in the other direction
# for Y coordinates
self.logger.debug('median filtering Y, %d rows', mfilter_size)
arr_median_alt = median_filter(arr, size=(mfilter_size, 1))
self.logger.debug('median filtering Y, %d columns', mfilter_size)
arr_median_alt = median_filter(arr_median_alt, size=(1, mfilter_size))
xfac = dtur[0] / EMIR_PIXSCALE
yfac = -dtur[1] / EMIR_PIXSCALE
vec = [yfac, xfac]
self.logger.debug('DTU shift is %s', vec)
# and the table of approx positions of the slits
barstab = rinput.bars_nominal_positions
# Currently, we only use fields 0 and 2
# of the nominal positions file
# Number or rows used
# These other parameters cab be tuned also
bstart = 1
bend = 2047
self.logger.debug('ignoring columns outside %d - %d', bstart, bend - 1)
# extract a region to average
wy = (rinput.average_box_row_size // 2)
wx = (rinput.average_box_col_size // 2)
self.logger.debug('extraction window is %d rows, %d cols', 2 * wy + 1,
2 * wx + 1)
# Fit the peak with these points
wfit = 2 * (rinput.fit_peak_npoints // 2) + 1
self.logger.debug('fit with %d points', wfit)
# Minimum threshold
threshold = 5 * EMIR_RON
# Savitsky and Golay (1964) filter to compute the X derivative
# scipy >= xx has a savgol_filter function
# for compatibility we do it manually
allpos = {}
ypos3_kernel = None
slits = numpy.zeros((EMIR_NBARS, 8), dtype='float')
self.logger.info('start finding bars')
for ks in [3, 5, 7, 9]:
self.logger.debug('kernel size is %d', ks)
# S and G kernel for derivative
kw = ks * (ks * ks - 1) / 12.0
coeffs_are = -numpy.arange((1 - ks) // 2, (ks - 1) // 2 + 1) / kw
if ks == 3:
ypos3_kernel = coeffs_are
self.logger.debug('kernel weights are %s', coeffs_are)
self.logger.debug('derive image in X direction')
arr_deriv = convolve1d(arr_median, coeffs_are, axis=-1)
# Axis 0 is
#
self.logger.debug('derive image in Y direction (with kernel=3)')
arr_deriv_alt = convolve1d(arr_median_alt, ypos3_kernel, axis=0)
positions = []
for coords in barstab:
lbarid = int(coords[0])
rbarid = lbarid + EMIR_NBARS
ref_y_coor = coords[1] + vec[1]
poly_coeffs = coords[2:]
prow = wc_to_pix_1d(ref_y_coor) - 1
fits_row = prow + 1 # FITS pixel index
# A function that returns the center of the bar
# given its X position
def center_of_bar(x):
# Pixel values are 0-based
return polyval(x + 1 - vec[0], poly_coeffs) + vec[1] - 1
self.logger.debug('looking for bars with ids %d - %d', lbarid,
rbarid)
self.logger.debug('reference y position is Y %7.2f',
ref_y_coor)
# if ref_y_coor is outlimits, skip this bar
# ref_y_coor is in FITS format
if (ref_y_coor >= 2047) or (ref_y_coor <= 1):
self.logger.debug(
'reference y position is outlimits, skipping')
positions.append([lbarid, fits_row, fits_row, 1, 0, 3])
positions.append([rbarid, fits_row, fits_row, 1, 0, 3])
continue
# Left bar
self.logger.debug('measure left border (%d)', lbarid)
centery, xpos, fwhm, st = char_bar_peak_l(arr_deriv,
prow,
bstart,
bend,
threshold,
center_of_bar,
wx=wx,
wy=wy,
wfit=wfit)
xpos1 = xpos
positions.append(
[lbarid, centery + 1, fits_row, xpos + 1, fwhm, st])
# Right bar
self.logger.debug('measure rigth border (%d)', rbarid)
centery, xpos, fwhm, st = char_bar_peak_r(arr_deriv,
prow,
bstart,
bend,
threshold,
center_of_bar,
wx=wx,
wy=wy,
wfit=wfit)
positions.append(
[rbarid, centery + 1, fits_row, xpos + 1, fwhm, st])
xpos2 = xpos
#
if st == 0:
self.logger.debug('measure top-bottom borders')
try:
y1, y2, statusy = char_bar_height(arr_deriv_alt,
xpos1,
xpos2,
centery,
threshold,
wh=35,
wfit=wfit)
except Exception as error:
self.logger.warning('Error computing height: %s',
error)
statusy = 44
if statusy in [0, 40]:
# Main border is detected
positions[-1][1] = y2 + 1
positions[-2][1] = y2 + 1
else:
# Update status
positions[-1][-1] = 4
positions[-2][-1] = 4
else:
self.logger.debug('slit is not complete')
y1, y2 = 0, 0
# Update positions
self.logger.debug(
'bar %d centroid-y %9.4f, row %d x-pos %9.4f, FWHM %6.3f, status %d',
*positions[-2])
self.logger.debug(
'bar %d centroid-y %9.4f, row %d x-pos %9.4f, FWHM %6.3f, status %d',
*positions[-1])
if ks == 5:
slits[lbarid -
1] = [xpos1, y2, xpos2, y2, xpos2, y1, xpos1, y1]
# FITS coordinates
slits[lbarid - 1] += 1.0
self.logger.debug('inserting bars %d-%d into "slits"',
lbarid, rbarid)
allpos[ks] = numpy.asarray(
positions, dtype='float') # GCS doesn't like lists of lists
self.logger.debug('end finding bars')
result = self.create_result(
frame=hdulist,
slits=slits,
positions9=allpos[9],
positions7=allpos[7],
positions5=allpos[5],
positions3=allpos[3],
DTU=dtub,
ROTANG=rotang,
TSUTC1=tsutc1,
csupos=csupos,
csusens=csusens,
)
return result
def run(self, rinput):
logger = logging.getLogger('numina.recipes.emir')
logger.info('starting processing for bars detection')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
hdr = hdulist[0].header
self.set_base_headers(hdr)
try:
rotang = hdr['ROTANG']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
csupos = datamodel.get_csup_from_header(hdr)
csusens = datamodel.get_cs_from_header(hdr)
except KeyError as error:
logger.error(error)
raise RecipeError(error)
logger.debug('finding bars')
arr = hdulist[0].data
# Median filter
logger.debug('median filtering')
mfilter_size = rinput.median_filter_size
arr_median = median_filter(arr, size=mfilter_size)
# Image is mapped between 0 and 1
# for the full range [0: 2**16]
logger.debug('image scaling to 0-1')
arr_grey = normalize_raw(arr_median)
# Find borders
logger.debug('find borders')
canny_sigma = rinput.canny_sigma
# These threshols corespond roughly with
# value x (2**16 - 1)
high_threshold = rinput.canny_high_threshold
low_threshold = rinput.canny_low_threshold
edges = canny(arr_grey,
sigma=canny_sigma,
high_threshold=high_threshold,
low_threshold=low_threshold)
# Number or rows used
# These other parameters cab be tuned also
total = 5
maxdist = 1.0
bstart = 100
bend = 1900
positions = []
nt = total // 2
xfac = dtur[0] / EMIR_PIXSCALE
yfac = -dtur[1] / EMIR_PIXSCALE
vec = [yfac, xfac]
logger.debug('DTU shift is %s', vec)
# Based on the 'edges image'
# and the table of approx positions of the slits
barstab = rinput.bars_nominal_positions
# Currently, we only use fields 0 and 2
# of the nominal positions file
for coords in barstab:
lbarid = int(coords[0])
rbarid = lbarid + 55
ref_y_coor = coords[2] + vec[1]
prow = wc_to_pix_1d(ref_y_coor) - 1
fits_row = prow + 1 # FITS pixel index
logger.debug('looking for bars with ids %d - %d', lbarid, rbarid)
logger.debug('reference y position is Y %7.2f', ref_y_coor)
# Find the position of each bar
bpos = find_position(edges, prow, bstart, bend, total)
nbars_found = len(bpos)
# If no bar is found, append and empty token
if nbars_found == 0:
logger.debug('bars %d, %d not found at row %d', lbarid, rbarid,
fits_row)
thisres1 = (lbarid, fits_row, 0, 0, 1)
thisres2 = (rbarid, fits_row, 0, 0, 1)
elif nbars_found == 2:
# Order values by increasing X
centl, centr = sorted(bpos, key=lambda cen: cen[0])
c1 = centl[0]
c2 = centr[0]
logger.debug('bars found at row %d between %7.2f - %7.2f',
fits_row, c1, c2)
# Compute FWHM of the collapsed profile
cslit = arr_grey[prow - nt:prow + nt + 1, :]
pslit = cslit.mean(axis=0)
# Add 1 to return FITS coordinates
epos, epos_f, error = locate_bar_l(pslit, c1)
thisres1 = lbarid, fits_row, epos + 1, epos_f + 1, error
epos, epos_f, error = locate_bar_r(pslit, c2)
thisres2 = rbarid, fits_row, epos + 1, epos_f + 1, error
elif nbars_found == 1:
logger.warn(
'only 1 edge found at row %d, not yet implemented',
fits_row)
thisres1 = (lbarid, fits_row, 0, 0, 1)
thisres2 = (rbarid, fits_row, 0, 0, 1)
else:
logger.warn(
'3 or more edges found at row %d, not yet implemented',
fits_row)
thisres1 = (lbarid, fits_row, 0, 0, 1)
thisres2 = (rbarid, fits_row, 0, 0, 1)
positions.append(thisres1)
positions.append(thisres2)
logger.debug('end finding bars')
result = self.create_result(
frame=hdulist,
positions=positions,
DTU=dtub,
ROTANG=rotang,
csupos=csupos,
csusens=csusens,
param_median_filter_size=rinput.median_filter_size,
param_canny_high_threshold=rinput.canny_high_threshold,
param_canny_low_threshold=rinput.canny_low_threshold)
return result
