class CSUSpectraExtractionRecipe(EmirRecipe):
"""Extract spectra in image taken with the CSU configured"""
# Recipe Requirements
obresult = ObservationResultRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_sky = MasterSkyRequirement()
nrows_side = Parameter(5, 'Number of rows to extract around the center')
slits_positions = Requirement(ArrayType,
'Positions and widths of the slits')
# Recipe products
frame = Product(DataFrameType)
rss = Product(DataFrameType)
def run(self, rinput):
_logger.info('starting extraction')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
hdr = hdulist[0].header
self.set_base_headers(hdr)
data1 = hdulist[0].data
_logger.info('Create output images')
rssdata = numpy.zeros(
(rinput.slits_positions.shape[0], data1.shape[1]), dtype='float32')
nrows = rinput.nrows_side
# Loop over slits
for idx, slit_coords in enumerate(rinput.slits_positions):
x, y, ax, ay = slit_coords # Coords in FITS coordinates
ref_col = wc_to_pix(x - 1)
ref_row = wc_to_pix(y - 1)
_logger.info('Processing slit in column %i, row=%i', ref_col,
ref_row)
# Simple extraction
_logger.info('Extract %i rows around center', nrows)
region = data1[ref_row - nrows:ref_row + nrows + 1, :]
rssdata[idx, :] = region.mean(axis=0)
hdurss = fits.PrimaryHDU(rssdata)
result = self.create_result(frame=hdulist, rss=hdurss)
return result
class TestMaskRecipe(EmirRecipe):
# Recipe Requirements
#
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_sky = MasterSkyRequirement()
pinhole_nominal_positions = Requirement(CoordinateList2DType,
'Nominal positions of the pinholes'
)
shift_coordinates = Parameter(True, 'Use header information to'
' shift the pinhole positions from (0,0) '
'to X_DTU, Y_DTU')
box_half_size = Parameter(4, 'Half of the computation box size in pixels')
recenter = Parameter(True, 'Recenter the pinhole coordinates')
max_recenter_radius = Parameter(2.0, 'Maximum distance for recentering')
median_filter_size = Parameter(5, 'Size of the median box')
canny_sigma = Parameter(3.0, 'Sigma for the canny algorithm')
obj_min_size = Parameter(200, 'Minimum size of the slit')
obj_max_size = Parameter(3000, 'Maximum size of the slit')
slit_size_ratio = Parameter(4.0, 'Minimum ratio between height and width for slits')
# Recipe Products
frame = Product(DataFrameType)
positions = Product(ArrayType)
positions_alt = Product(ArrayType)
slitstable = Product(ArrayType)
DTU = Product(ArrayType)
filter = Product(str)
readmode = Product(str)
ROTANG = Product(float)
DETPA = Product(float)
DTUPA = Product(float)
param_recenter = Product(bool)
param_max_recenter_radius = Product(float)
param_box_half_size = Product(float)
def run(self, rinput):
_logger.info('starting processing for slit detection')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
hdr = hdulist[0].header
self.set_base_headers(hdr)
_logger.debug('finding pinholes')
try:
filtername = hdr['FILTER']
readmode = hdr['READMODE']
rotang = hdr['ROTANG']
detpa = hdr['DETPA']
dtupa = hdr['DTUPA']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
except KeyError as error:
_logger.error(error)
raise RecipeError(error)
if rinput.shift_coordinates:
xdtur, ydtur, zdtur = dtur
xfac = xdtur / EMIR_PIXSCALE
yfac = -ydtur / EMIR_PIXSCALE
vec = numpy.array([yfac, xfac])
_logger.info('shift is %s', vec)
ncenters = rinput.pinhole_nominal_positions + vec
else:
_logger.info('using pinhole coordinates as they are')
ncenters = rinput.pinhole_nominal_positions
_logger.info('pinhole characterization')
positions = pinhole_char(
hdulist[0].data,
ncenters,
box=rinput.box_half_size,
recenter_pinhole=rinput.recenter,
maxdist=rinput.max_recenter_radius
)
_logger.info('alternate pinhole characterization')
positions_alt = pinhole_char2(
hdulist[0].data, ncenters,
recenter_pinhole=rinput.recenter,
recenter_half_box=rinput.box_half_size,
recenter_maxdist=rinput.max_recenter_radius
)
_logger.debug('finding slits')
# First, prefilter with median
median_filter_size = rinput.median_filter_size
canny_sigma = rinput.canny_sigma
obj_min_size = rinput.obj_min_size
obj_max_size = rinput.obj_max_size
data1 = hdulist[0].data
_logger.debug('Median filter with box %d', median_filter_size)
data2 = median_filter(data1, size=median_filter_size)
# Grey level image
img_grey = normalize(data2)
# Find edges with canny
_logger.debug('Find edges with canny, sigma %d', canny_sigma)
edges = canny(img_grey, sigma=canny_sigma)
# Fill edges
_logger.debug('Fill holes')
fill_slits = ndimage.binary_fill_holes(edges)
_logger.debug('Label objects')
label_objects, nb_labels = ndimage.label(fill_slits)
_logger.debug('%d objects found', nb_labels)
# Filter on the area of the labeled region
# Perhaps we could ignore this filtering and
# do it later?
_logger.debug('Filter objects by size')
# Sizes of regions
sizes = numpy.bincount(label_objects.ravel())
_logger.debug('Min size is %d', obj_min_size)
_logger.debug('Max size is %d', obj_max_size)
mask_sizes = (sizes > obj_min_size) & (sizes < obj_max_size)
# Filter out regions
nids, = numpy.where(mask_sizes)
mm = numpy.in1d(label_objects, nids)
mm.shape = label_objects.shape
fill_slits_clean = numpy.where(mm, 1, 0)
# and relabel
_logger.debug('Label filtered objects')
relabel_objects, nb_labels = ndimage.label(fill_slits_clean)
_logger.debug('%d objects found after filtering', nb_labels)
ids = list(six.moves.range(1, nb_labels + 1))
_logger.debug('Find regions and centers')
regions = ndimage.find_objects(relabel_objects)
centers = ndimage.center_of_mass(data2, labels=relabel_objects,
index=ids
)
table = char_slit(data2, regions,
slit_size_ratio=rinput.slit_size_ratio
)
result = self.create_result(frame=hdulist,
positions=positions,
positions_alt=positions_alt,
slitstable=table,
filter=filtername,
DTU=dtub,
readmode=readmode,
ROTANG=rotang,
DETPA=detpa,
DTUPA=dtupa,
param_recenter=rinput.recenter,
param_max_recenter_radius=rinput.max_recenter_radius,
param_box_half_size=rinput.box_half_size
)
return result
class TestSlitDetectionRecipe(EmirRecipe):
# Recipe Requirements
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_sky = MasterSkyRequirement()
median_filter_size = Parameter(5, 'Size of the median box')
canny_sigma = Parameter(3.0, 'Sigma for the canny algorithm')
canny_high_threshold = Parameter(0.04, 'High threshold for the Canny algorithm')
canny_low_threshold = Parameter(0.01, 'High threshold for the Canny algorithm')
# Recipe Results
frame = Product(DataFrameType)
slitstable = Product(ArrayType)
DTU = Product(ArrayType)
ROTANG = Product(float)
DETPA = Product(float)
DTUPA = Product(float)
def run(self, rinput):
_logger.info('starting slit processing')
_logger.info('basic image reduction')
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']
detpa = hdr['DETPA']
dtupa = hdr['DTUPA']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
except KeyError as error:
_logger.error(error)
raise RecipeError(error)
_logger.debug('finding slits')
# Filter values below 0.0
_logger.debug('Filter values below 0')
data1 = hdulist[0].data[:]
data1[data1 < 0.0] = 0.0
# First, prefilter with median
median_filter_size = rinput.median_filter_size
canny_sigma = rinput.canny_sigma
_logger.debug('Median filter with box %d', median_filter_size)
data2 = median_filter(data1, size=median_filter_size)
# Grey level image
img_grey = normalize_raw(data2)
# Find edges with Canny
_logger.debug('Find edges, Canny sigma %f', canny_sigma)
# These thresholds corespond roughly with
# value x (2**16 - 1)
high_threshold = rinput.canny_high_threshold
low_threshold = rinput.canny_low_threshold
_logger.debug('Find edges, Canny high threshold %f', high_threshold)
_logger.debug('Find edges, Canny low threshold %f', low_threshold)
edges = canny(img_grey, sigma=canny_sigma,
high_threshold=high_threshold,
low_threshold=low_threshold)
# Fill edges
_logger.debug('Fill holes')
# I do a dilation and erosion to fill
# possible holes in 'edges'
fill = ndimage.binary_dilation(edges)
fill2 = ndimage.binary_fill_holes(fill)
fill_slits = ndimage.binary_erosion(fill2)
_logger.debug('Label objects')
label_objects, nb_labels = ndimage.label(fill_slits)
_logger.debug('%d objects found', nb_labels)
ids = list(six.moves.range(1, nb_labels + 1))
_logger.debug('Find regions and centers')
regions = ndimage.find_objects(label_objects)
centers = ndimage.center_of_mass(data2, labels=label_objects,
index=ids
)
table = char_slit(data2, regions,
slit_size_ratio=-1.0
)
result = self.create_result(frame=hdulist,
slitstable=table,
DTU=dtub,
ROTANG=rotang,
DETPA=detpa,
DTUPA=dtupa
)
return result
class MaskSpectraExtractionRecipe(EmirRecipe):
'''
'''
# Recipe Requirements
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_sky = MasterSkyRequirement()
median_filter_size = Parameter(5, 'Size of the median box')
slits_positions = Requirement(ArrayType,
'Positions and widths of the slits')
frame = Product(DataFrameType)
rss = Product(DataFrameType)
regions = Product(ArrayType)
#slitstable = Product(ArrayType)
#DTU = Product(ArrayType)
#ROTANG = Product(float)
#DETPA = Product(float)
#DTUPA = Product(float)
def run(self, rinput):
_logger.info('starting extraction')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
hdr = hdulist[0].header
self.set_base_headers(hdr)
# First, prefilter with median
median_filter_size = rinput.median_filter_size
data1 = hdulist[0].data
_logger.debug('Median filter with box %d', median_filter_size)
data2 = median_filter(data1, size=median_filter_size)
# Normalize input between -1 and +1
data3 = img_norm(data2)
# Tracing parameters
ws = 10
step = 15
hs = 15
tol = 2
doplot = False
npol = 5
_logger.info('Create output images')
rssdata = numpy.zeros(
(rinput.slits_positions.shape[0], data3.shape[1]), dtype='float32')
# FIXME, number of columns depends on polynomial degree
regiontable = numpy.zeros(
(rinput.slits_positions.shape[0], 4 + 2 * (npol + 1)),
dtype='float32')
count = 0
# Loop over slits
for slit_coords in rinput.slits_positions:
col, y1, y2 = convert_to_(*slit_coords)
_logger.info('Processing slit in column %i, row1=%i, row2=%i', col,
y1, y2)
xmin, xmax, ymin, ymax, pfit1, pfit2 = ex_region(data3,
col,
y1,
y2,
step,
hs,
ws,
tol=tol,
doplot=doplot)
_logger.info('Spectrum region is %i, %i, %i, %i', xmin, xmax, ymin,
ymax)
try:
region = data1[ymin:ymax + 1, xmin:xmax + 1]
rssdata[count, xmin:xmax + 1] = region.mean(axis=0)
except ValueError as err:
_logger.error("Error collapsing spectrum: %s", err)
# IN FITS convention
_logger.info('Create regions table')
regiontable[count, :4] = xmin + 1, xmax + 1, ymin + 1, ymax + 1
#regiontable[count, 4:4 + npol + 1] = pfit1
#regiontable[count, 4 + npol + 1:] = pfit2
count += 1
hdurss = fits.PrimaryHDU(rssdata)
result = self.create_result(frame=hdulist,
rss=hdurss,
regions=regiontable)
return result
class TestSlitMaskDetectionRecipe(EmirRecipe):
# Recipe Requirements
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_sky = MasterSkyRequirement()
median_filter_size = Parameter(5, 'Size of the median box')
canny_sigma = Parameter(3.0, 'Sigma for the Canny algorithm')
canny_high_threshold = Parameter(0.04, 'High threshold for the Canny algorithm')
canny_low_threshold = Parameter(0.01, 'High threshold for the Canny algorithm')
obj_min_size = Parameter(200, 'Minimum size of the slit')
obj_max_size = Parameter(3000, 'Maximum size of the slit')
slit_size_ratio = Parameter(4.0, 'Minimum ratio between height and width for slits')
# Recipe Results
frame = Product(DataFrameType)
slitstable = Product(ArrayType)
DTU = Product(ArrayType)
ROTANG = Product(float)
DETPA = Product(float)
DTUPA = Product(float)
def run(self, rinput):
_logger.info('starting slit processing')
_logger.info('basic image reduction')
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']
detpa = hdr['DETPA']
dtupa = hdr['DTUPA']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
except KeyError as error:
_logger.error(error)
raise RecipeError(error)
_logger.debug('finding slits')
# First, prefilter with median
median_filter_size = rinput.median_filter_size
canny_sigma = rinput.canny_sigma
obj_min_size = rinput.obj_min_size
obj_max_size = rinput.obj_max_size
data1 = hdulist[0].data
_logger.debug('Median filter with box %d', median_filter_size)
data2 = median_filter(data1, size=median_filter_size)
# Grey level image
img_grey = normalize_raw(data2)
# Find edges with Canny
_logger.debug('Find edges with Canny, sigma %f', canny_sigma)
# These thresholds corespond roughly with
# value x (2**16 - 1)
high_threshold = rinput.canny_high_threshold
low_threshold = rinput.canny_low_threshold
_logger.debug('Find edges, Canny high threshold %f', high_threshold)
_logger.debug('Find edges, Canny low threshold %f', low_threshold)
edges = canny(img_grey, sigma=canny_sigma,
high_threshold=high_threshold,
low_threshold=low_threshold)
# Fill edges
_logger.debug('Fill holes')
fill_slits = ndimage.binary_fill_holes(edges)
_logger.debug('Label objects')
label_objects, nb_labels = ndimage.label(fill_slits)
_logger.debug('%d objects found', nb_labels)
# Filter on the area of the labeled region
# Perhaps we could ignore this filtering and
# do it later?
_logger.debug('Filter objects by size')
# Sizes of regions
sizes = numpy.bincount(label_objects.ravel())
_logger.debug('Min size is %d', obj_min_size)
_logger.debug('Max size is %d', obj_max_size)
mask_sizes = (sizes > obj_min_size) & (sizes < obj_max_size)
# Filter out regions
nids, = numpy.where(mask_sizes)
mm = numpy.in1d(label_objects, nids)
mm.shape = label_objects.shape
fill_slits_clean = numpy.where(mm, 1, 0)
#plt.imshow(fill_slits_clean)
# and relabel
_logger.debug('Label filtered objects')
relabel_objects, nb_labels = ndimage.label(fill_slits_clean)
_logger.debug('%d objects found after filtering', nb_labels)
ids = list(six.moves.range(1, nb_labels + 1))
_logger.debug('Find regions and centers')
regions = ndimage.find_objects(relabel_objects)
centers = ndimage.center_of_mass(data2, labels=relabel_objects,
index=ids
)
table = char_slit(data2, regions,
slit_size_ratio=rinput.slit_size_ratio
)
result = self.create_result(frame=hdulist, slitstable=table,
DTU=dtub,
ROTANG=rotang,
DETPA=detpa,
DTUPA=dtupa
)
return result
class TestPointSourceRecipe(EmirRecipe):
# Recipe Requirements
#
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_sky = MasterSkyRequirement()
shift_coordinates = Parameter(
True, 'Use header information to'
' shift the pinhole positions from (0,0) '
'to X_DTU, Y_DTU')
box_half_size = Parameter(4, 'Half of the computation box size in pixels')
recenter = Parameter(True, 'Recenter the pinhole coordinates')
max_recenter_radius = Parameter(2.0, 'Maximum distance for recentering')
# Recipe Products
frame = Product(DataFrameType)
positions = Product(ArrayType)
positions_alt = Product(ArrayType)
DTU = Product(ArrayType)
filter = Product(str)
readmode = Product(str)
ROTANG = Product(float)
DETPA = Product(float)
DTUPA = Product(float)
param_recenter = Product(bool)
param_max_recenter_radius = Product(float)
param_box_half_size = Product(float)
def run(self, rinput):
_logger.info('starting processing for object detection')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
hdr = hdulist[0].header
self.set_base_headers(hdr)
_logger.debug('finding point sources')
try:
filtername = hdr['FILTER']
readmode = hdr['READMODE']
rotang = hdr['ROTANG']
detpa = hdr['DETPA']
dtupa = hdr['DTUPA']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
except KeyError as error:
_logger.error(error)
raise RecipeError(error)
data = hdulist[0].data
# Copy needed in numpy 1.7
# This seems already bitswapped??
# FIXME: check this works offline/online
# ndata = data.byteswap().newbyteorder()
# data = data.byteswap(inplace=True).newbyteorder()
snr_detect = 5.0
fwhm = 4.0
npixels = 15
box_shape = [64, 64]
_logger.info('point source detection2')
_logger.info('using internal mask to remove corners')
# Corners
mask = numpy.zeros_like(data, dtype='int32')
mask[2000:, 0:80] = 1
mask[2028:, 2000:] = 1
mask[:50, 1950:] = 1
mask[:100, :50] = 1
# Remove corner regions
_logger.info('compute background map, %s', box_shape)
bkg = sep.Background(data)
_logger.info('reference fwhm is %5.1f pixels', fwhm)
_logger.info('detect threshold, %3.1f over background', snr_detect)
_logger.info('convolve with gaussian kernel, FWHM %3.1f pixels', fwhm)
sigma = fwhm * gaussian_fwhm_to_sigma
#
kernel = Gaussian2DKernel(sigma)
kernel.normalize()
thresh = snr_detect * bkg.globalrms
data_s = data - bkg.back()
objects, segmap = sep.extract(data - bkg.back(),
thresh,
minarea=npixels,
filter_kernel=kernel.array,
segmentation_map=True,
mask=mask)
fits.writeto('segmap.fits', segmap)
_logger.info('detected %d objects', len(objects))
# Hardcoded values
rs2 = 15.0
fit_rad = 10.0
flux_min = 1000.0
flux_max = 30000.0
_logger.debug('Flux limit is %6.1f %6.1f', flux_min, flux_max)
# FIXME: this should be a view, not a copy
xall = objects['x']
yall = objects['y']
mm = numpy.array([xall, yall]).T
_logger.info('computing FWHM')
# Find objects with pairs inside fit_rad
kdtree = KDTree(mm)
nearobjs = (kdtree.query_ball_tree(kdtree, r=fit_rad))
positions = []
for idx, obj in enumerate(objects):
x0 = obj['x']
y0 = obj['y']
sl = image_box2d(x0, y0, data.shape, (fit_rad, fit_rad))
# sl_sky = image_box2d(x0, y0, data.shape, (rs2, rs2))
part_s = data_s[sl]
# Logical coordinates
xx0 = x0 - sl[1].start
yy0 = y0 - sl[0].start
_, fwhm_x, fwhm_y = compute_fwhm_2d_simple(part_s, xx0, yy0)
if min(fwhm_x, fwhm_x) < 3:
continue
if flux_min > obj['peak'] or flux_max < obj['peak']:
continue
# nobjs is the number of object inside fit_rad
nobjs = len(nearobjs[idx])
flag = 0 if nobjs == 1 else 1
positions.append([idx, x0, y0, obj['peak'], fwhm_x, fwhm_y, flag])
_logger.info('saving photometry')
positions = numpy.array(positions)
positions_alt = positions
_logger.info('end processing for object detection')
result = self.create_result(
frame=hdulist,
positions=positions_alt,
positions_alt=positions_alt,
filter=filtername,
DTU=dtub,
readmode=readmode,
ROTANG=rotang,
DETPA=detpa,
DTUPA=dtupa,
param_recenter=rinput.recenter,
param_max_recenter_radius=rinput.max_recenter_radius,
param_box_half_size=rinput.box_half_size)
return result
class BarDetectionRecipe(EmirRecipe):
# Recipe Requirements
#
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_sky = MasterSkyRequirement()
bars_nominal_positions = Requirement(CoordinateList2DType,
'Nominal positions of the bars')
median_filter_size = Parameter(5, 'Size of the median box')
canny_sigma = Parameter(3.0, 'Sigma for the canny algorithm')
canny_high_threshold = Parameter(0.04,
'High threshold for the canny algorithm')
canny_low_threshold = Parameter(0.01,
'High threshold for the canny algorithm')
# Recipe Products
frame = Product(DataFrameType)
positions = Product(ArrayType)
DTU = Product(ArrayType)
ROTANG = Product(float)
csupos = Product(ArrayType)
csusens = Product(ArrayType)
param_median_filter_size = Product(float)
param_canny_high_threshold = Product(float)
param_canny_low_threshold = Product(float)
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
class TestPinholeRecipe(EmirRecipe):
# Recipe Requirements
#
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_sky = MasterSkyRequirement()
pinhole_nominal_positions = Requirement(CoordinateList2DType,
'Nominal positions of the pinholes'
)
shift_coordinates = Parameter(True, 'Use header information to'
' shift the pinhole positions from (0,0) '
'to X_DTU, Y_DTU')
box_half_size = Parameter(4, 'Half of the computation box size in pixels')
recenter = Parameter(True, 'Recenter the pinhole coordinates')
max_recenter_radius = Parameter(2.0, 'Maximum distance for recentering')
# Recipe Products
frame = Product(DataFrameType)
positions = Product(ArrayType)
positions_alt = Product(ArrayType)
DTU = Product(ArrayType)
filter = Product(str)
readmode = Product(str)
ROTANG = Product(float)
DETPA = Product(float)
DTUPA = Product(float)
param_recenter = Product(bool)
param_max_recenter_radius = Product(float)
param_box_half_size = Product(float)
def run(self, rinput):
_logger.info('starting processing for slit detection')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
hdr = hdulist[0].header
self.set_base_headers(hdr)
_logger.debug('finding pinholes')
try:
filtername = hdr['FILTER']
readmode = hdr['READMODE']
rotang = hdr['ROTANG']
detpa = hdr['DETPA']
dtupa = hdr['DTUPA']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
except KeyError as error:
_logger.error(error)
raise RecipeError(error)
if rinput.shift_coordinates:
xdtur, ydtur, zdtur = dtur
xfac = xdtur / EMIR_PIXSCALE
yfac = -ydtur / EMIR_PIXSCALE
vec = numpy.array([yfac, xfac])
_logger.info('shift is %s', vec)
ncenters = rinput.pinhole_nominal_positions + vec
else:
_logger.info('using pinhole coordinates as they are')
ncenters = rinput.pinhole_nominal_positions
_logger.info('pinhole characterization')
positions = pinhole_char(
hdulist[0].data,
ncenters,
box=rinput.box_half_size,
recenter_pinhole=rinput.recenter,
maxdist=rinput.max_recenter_radius
)
_logger.info('alternate pinhole characterization')
positions_alt = pinhole_char2(
hdulist[0].data, ncenters,
recenter_pinhole=rinput.recenter,
recenter_half_box=rinput.box_half_size,
recenter_maxdist=rinput.max_recenter_radius
)
result = self.create_result(frame=hdulist,
positions=positions,
positions_alt=positions_alt,
filter=filtername,
DTU=dtub,
readmode=readmode,
ROTANG=rotang,
DETPA=detpa,
DTUPA=dtupa,
param_recenter=rinput.recenter,
param_max_recenter_radius=rinput.max_recenter_radius,
param_box_half_size=rinput.box_half_size
)
return result
class BarDetectionRecipe(EmirRecipe):
# Recipe Requirements
#
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_sky = MasterSkyRequirement()
bars_nominal_positions = Requirement(NominalPositions,
'Nominal positions of the bars')
median_filter_size = Parameter(5, 'Size of the median box')
average_box_row_size = Parameter(
7, 'Number of rows to average for fine centering (odd)')
average_box_col_size = Parameter(
21, 'Number of columns to extract for fine centering (odd)')
fit_peak_npoints = Parameter(
3, 'Number of points to use for fitting the peak (odd)')
# Recipe Products
frame = Product(DataFrameType)
# derivative = Product(DataFrameType)
slits = Product(ArrayType)
positions3 = Product(ArrayType)
positions5 = Product(ArrayType)
positions7 = Product(ArrayType)
positions9 = Product(ArrayType)
DTU = Product(ArrayType)
ROTANG = Product(float)
TSUTC1 = Product(float)
csupos = Product(ArrayType)
csusens = Product(ArrayType)
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)
self.save_intermediate_img(hdulist, 'reduced_image.fits')
try:
rotang = hdr['ROTANG']
tsutc1 = hdr['TSUTC1']
dtub, dtur = datamodel.get_dtur_from_header(hdr)
csupos = datamodel.get_csup_from_header(hdr)
if len(csupos) != 2 * EMIR_NBARS:
raise RecipeError('Number of CSUPOS != 2 * NBARS')
csusens = datamodel.get_cs_from_header(hdr)
except KeyError as error:
self.logger.error(error)
raise RecipeError(error)
self.logger.debug('start finding bars')
allpos, slits = self.find_bars(hdulist, rinput, csupos, dtur)
self.logger.debug('end finding bars')
if self.intermediate_results:
with open('ds9.reg', 'w') as ds9reg:
self.to_ds9_reg(ds9reg, slits)
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 median_filtering(self, hdulist, rinput):
# Processed array
arr = hdulist[0].data
# Median filter of processed array (two times)
mfilter_size = rinput.median_filter_size
self.logger.debug('median filtering 1')
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))
self.save_intermediate_array(arr_median, 'median_image.fits')
# Median filter of processed array (two times) in the other direction
# for Y coordinates
self.logger.debug('median filtering 2')
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))
self.save_intermediate_array(arr_median_alt, 'median_image_alt.fits')
return arr_median, arr_median_alt
def find_bars(self, hdulist, rinput, csupos, dtur):
self.logger.debug('filtering image')
# Processed array
arr_median, arr_median_alt = self.median_filtering(hdulist, rinput)
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('find peaks in derivative image')
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)
self.save_intermediate_array(arr_deriv,
'deriv_image_k%d.fits' % ks)
# 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 = []
self.logger.info('using bar parameters')
for idx in range(EMIR_NBARS):
params_l = barstab[idx]
params_r = barstab[idx + EMIR_NBARS]
lbarid = int(params_l[0])
# CSUPOS for this bar
rbarid = lbarid + EMIR_NBARS
current_csupos_l = csupos[lbarid - 1]
current_csupos_r = csupos[rbarid - 1]
self.logger.debug('CSUPOS for bar %d is %f', lbarid,
current_csupos_l)
self.logger.debug('CSUPOS for bar %d is %f', rbarid,
current_csupos_r)
ref_y_coor_virt = params_l[1] # Do I need to add vec[1]?
ref_x_l_coor_virt = params_l[3] + current_csupos_l * params_l[2]
ref_x_r_coor_virt = params_r[3] + current_csupos_r * params_r[2]
# Transform to REAL..
ref_x_l_coor, ref_y_l_coor = dist.exvp(ref_x_l_coor_virt,
ref_y_coor_virt)
ref_x_r_coor, ref_y_r_coor = dist.exvp(ref_x_r_coor_virt,
ref_y_coor_virt)
# FIXME: check if DTU has to be applied
# ref_y_coor = ref_y_coor + vec[1]
prow = coor_to_pix_1d(ref_y_l_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_l(x):
# Pixel values are 0-based
# return ref_y_coor + vec[1] - 1
# FIXME: check if DTU has to be applied
return ref_y_l_coor - 1
def center_of_bar_r(x):
# Pixel values are 0-based
# return ref_y_coor + vec[1] - 1
# FIXME: check if DTU has to be applied
return ref_y_r_coor - 1
self.logger.debug('looking for bars with ids %d - %d', lbarid,
rbarid)
self.logger.debug('ref Y virtual position is %7.2f',
ref_y_coor_virt)
self.logger.debug('ref X virtual positions are %7.2f %7.2f',
ref_x_l_coor_virt, ref_x_r_coor_virt)
self.logger.debug('ref X positions are %7.2f %7.2f',
ref_x_l_coor, ref_x_r_coor)
self.logger.debug('ref Y positions are %7.2f %7.2f',
ref_y_l_coor, ref_y_r_coor)
# if ref_y_coor is outlimits, skip this bar
# ref_y_coor is in FITS format
if (ref_y_l_coor >= 2047) or (ref_y_l_coor <= 1):
self.logger.debug(
'reference y position is outlimits, skipping')
positions.append(
[lbarid, fits_row, fits_row, fits_row, 1, 1, 0, 3])
positions.append(
[rbarid, fits_row, fits_row, fits_row, 1, 1, 0, 3])
continue
# minimal width of the slit
minwidth = 0.9
if abs(ref_x_l_coor_virt - ref_x_r_coor_virt) < minwidth:
self.logger.debug(
'slit is less than %d virt pixels, skipping', minwidth)
positions.append(
[lbarid, fits_row, fits_row, fits_row, 1, 1, 0, 3])
positions.append(
[rbarid, fits_row, fits_row, fits_row, 1, 1, 0, 3])
continue
# Left bar
# Dont add +1 to virtual pixels
self.logger.debug('measure left border (%d)', lbarid)
regionw = 10
bstart1 = coor_to_pix_1d(ref_x_l_coor - regionw)
bend1 = coor_to_pix_1d(ref_x_l_coor + regionw) + 1
centery, centery_virt, xpos1, xpos1_virt, fwhm, st = char_bar_peak_l(
arr_deriv,
prow,
bstart1,
bend1,
threshold,
center_of_bar_l,
wx=wx,
wy=wy,
wfit=wfit)
insert1 = [
lbarid, centery + 1, centery_virt, fits_row, xpos1 + 1,
xpos1_virt, fwhm, st
]
positions.append(insert1)
# Right bar
# Dont add +1 to virtual pixels
self.logger.debug('measure rigth border (%d)', rbarid)
bstart2 = coor_to_pix_1d(ref_x_r_coor - regionw)
bend2 = coor_to_pix_1d(ref_x_r_coor + regionw) + 1
centery, centery_virt, xpos2, xpos2_virt, fwhm, st = char_bar_peak_r(
arr_deriv,
prow,
bstart2,
bend2,
threshold,
center_of_bar_l,
wx=wx,
wy=wy,
wfit=wfit)
# This centery/centery_virt should be equal to ref_y_coor_virt
insert2 = [
rbarid, centery + 1, centery_virt, fits_row, xpos2 + 1,
xpos2_virt, fwhm, st
]
positions.append(insert2)
# FIXME: hardcoded value
y1_virt = ref_y_coor_virt - 16.242
y2_virt = ref_y_coor_virt + 16.242
_, y1 = dist.exvp(xpos1_virt + 1, y1_virt + 1)
_, y2 = dist.exvp(xpos2_virt + 1, y2_virt + 1)
# Update positions
msg = 'bar %d, centroid-y %9.4f centroid-y virt %9.4f, ' \
'row %d, x-pos %9.4f x-pos virt %9.4f, FWHM %6.3f, status %d'
self.logger.debug(msg, *positions[-2])
self.logger.debug(msg, *positions[-1])
if ks == 5:
slits[lbarid - 1] = numpy.array(
[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
return allpos, slits
def to_ds9_reg(self, ds9reg, slits):
"""Transform fiber traces to ds9-region format.
Parameters
----------
ds9reg : BinaryIO
Handle to output file name in ds9-region format.
"""
# open output file and insert header
ds9reg.write('# Region file format: DS9 version 4.1\n')
ds9reg.write(
'global color=green dashlist=8 3 width=1 font="helvetica 10 '
'normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 '
'move=1 delete=1 include=1 source=1\n')
ds9reg.write('physical\n')
for idx, slit in enumerate(slits, 1):
xpos1, y2, xpos2, y2, xpos2, y1, xpos1, y1 = slit
xc = 0.5 * (xpos1 + xpos2) + 1
yc = 0.5 * (y1 + y2) + 1
xd = (xpos2 - xpos1)
yd = (y2 - y1)
ds9reg.write('box({0},{1},{2},{3},0)\n'.format(xc, yc, xd, yd))
ds9reg.write('# text({0},{1}) color=red text={{{2}}}\n'.format(
xpos1 - 5, yc, idx))
ds9reg.write('# text({0},{1}) color=red text={{{2}}}\n'.format(
xpos2 + 5, yc, idx + EMIR_NBARS))