class TargetAcquisitionRecipe(EmirRecipe):
"""
Acquire a target.
Recipe for the processing of target acquisition images.
**Observing modes:**
* Target acquisition
"""
# Requirements
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
# Products
telescope_offset = Product(TelescopeOffset)
def run(self, rinput):
return self.create_result(telescope_offset=TelescopeOffset())
class DitherSkyRecipe(EmirRecipe):
"""Recipe to process data taken in dither sky mode.
"""
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
skyframe = Product(MasterIntensityFlat)
def run(self, rinput):
_logger.debug('instrument %s, mode %s', rinput.obresult.instrument,
rinput.obresult.mode)
_logger.info('starting sky reduction with dither')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_segmentation(rinput,
flow,
method=median,
errors=True)
hdr = hdulist[0].header
self.set_base_headers(hdr)
_logger.info('end sky reduction with dither')
result = self.create_result(skyframe=hdulist)
return result
class SimpleSkyRecipe(EmirRecipe):
"""Recipe to process data taken in intensity flat-field mode.
"""
master_bpm = MasterBadPixelMaskRequirement()
obresult = ObservationResultRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
skyframe = Product(MasterIntensityFlat)
def run(self, rinput):
_logger.info('starting sky reduction')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput,
flow,
method=median,
errors=True)
hdr = hdulist[0].header
self.set_base_headers(hdr)
result = self.create_result(skyframe=hdulist)
return result
class WavelengthCalibrationRecipe(EmirRecipe):
"""Recipe to calibrate the spectral response.
**Observing modes:**
* Wavelength calibration (4.5)
**Inputs:**
* List of line positions
* Calibrations up to spectral flatfielding
**Outputs:**
* Wavelength calibration structure
**Procedure:**
* TBD
"""
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_spectral_ff = MasterSpectralFlatFieldRequirement()
cal = Product(WavelengthCalibration)
def run(self, rinput):
return self.create_result(cal=WavelengthCalibration())
class OffsetSpectraRecipe(EmirRecipe):
"""
Observing mode:
Offset spectra beyond the slit
"""
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_spectral_ff = Requirement(prods.MasterSpectralFlat,
'Master spectral flatfield')
st_calibration = Requirement(prods.SlitTransmissionCalibration,
'Slit tranmision calibration')
w_calibration = Requirement(prods.WavelengthCalibration,
'Wavelength calibration')
lines = Parameter('lines', None,
'List of x-lambda pairs of line coordinates')
spectra = Product(prods.Spectra)
catalog = Product(prods.LinesCatalog)
def run(self, rinput):
return self.create_result(spectra=prods.Spectra(),
catalog=prods.LinesCatalog())
class SlitTransmissionRecipe(EmirRecipe):
"""Recipe to calibrate the slit transmission.
**Observing modes:**
* Slit transmission calibration (4.4)
**Inputs:**
* A list of uniformly illuminated images of MSM
**Outputs:**
* A list of slit transmission functions
**Procedure:**
* TBD
"""
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
slit = Product(SlitTransmissionCalibration)
def run(self, rinput):
return self.create_result(slit=SlitTransmissionCalibration())
class ArcCalibrationRecipe(EmirRecipe):
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
lines_catalog = Requirement(LinesCatalog, "Catalog of lines")
polynomial_degree = Parameter(2,
'Polynomial degree of the arc calibration')
polynomial_coeffs = Product(ArrayType)
def run(self, rinput):
_logger.info('starting arc calibration')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput, flow=flow)
nslits = len(rinput.slits_catalog)
coeff_table = numpy.zeros((nslits, rinput.polynomial_degree + 1))
result = self.create_result(polynomial_coeffs=coeff_table)
return result
class TestSkyCorrectRecipe(EmirRecipe):
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_sky = Requirement(MasterIntensityFlat, 'Master Sky calibration')
frame = Product(DataFrameType)
def run(self, rinput):
_logger.info('starting simple sky reduction')
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput,
flow,
method=median)
hdr = hdulist[0].header
self.set_base_headers(hdr)
# Update SEC to 0
hdr['SEC'] = 0
result = self.create_result(frame=hdulist)
return result
class IntensityFlatRecipe(EmirRecipe):
"""Recipe to process data taken in intensity flat-field mode.
Recipe to process intensity flat-fields. The flat-on and
flat-off images are combined (method?) separately and the subtracted
to obtain a thermal subtracted flat-field.
**Observing modes:**
* Intensity Flat-Field
**Inputs:**
* A master dark frame
* Non linearity
* A model of the detector.
**Outputs:**
* TBD
**Procedure:**
* A combined thermal subtracted flat field, normalized to median 1,
with with variance extension and quality flag.
"""
master_bpm = MasterBadPixelMaskRequirement()
obresult = ObservationResultRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
flatframe = Product(MasterIntensityFlat)
def run(self, rinput):
_logger.info('starting flat reduction')
errors = True
flow = self.init_filters(rinput)
hdulist = basic_processing_with_combination(rinput,
flow,
method=median,
errors=errors)
hdr = hdulist[0].header
self.set_base_headers(hdr)
mm = hdulist[0].data.mean()
hdr['CCDMEAN'] = mm
hdulist[0].data /= mm
if errors:
hdulist['variance'].data /= (mm * mm)
result = self.create_result(flatframe=hdulist)
return result
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 DarkRecipe(EmirRecipe):
"""Recipe to process data taken in Dark current image Mode.
Recipe to process dark images. The dark images will be combined
using the median.
They do have to be of the same exposure time t.
**Observing mode:**
* Dark current Image (3.2)
**Inputs:**
**Outputs:**
* A combined dark frame, with variance extension.
"""
master_bpm = MasterBadPixelMaskRequirement()
obresult = ObservationResultRequirement()
master_bias = MasterBiasRequirement()
darkframe = Product(MasterDark)
def run(self, rinput):
_logger.info('starting dark reduction')
flow = self.init_filters(rinput)
iinfo = gather_info_frames(rinput.obresult.frames)
ref_exptime = 0.0
for el in iinfo[1:]:
if abs(el['texp'] - ref_exptime) > 1e-4:
_logger.error('image with wrong exposure time')
raise RecipeError('image with wrong exposure time')
hdulist = basic_processing_with_combination(rinput,
flow,
method=median,
errors=True)
pdata = hdulist[0].data
# update hdu header with
# reduction keywords
hdr = hdulist[0].header
self.set_base_headers(hdr)
hdr['CCDMEAN'] = pdata.mean()
_logger.info('dark reduction ended')
result = self.create_result(darkframe=hdulist)
return result
class SpectralFlatRecipe(EmirRecipe):
master_bpm = MasterBadPixelMaskRequirement()
obresult = ObservationResultRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
flatframe = Product(MasterSpectralFlat)
def run(self, rinput):
return self.create_result(flatframe=MasterSpectralFlat())
class NBImageRecipeInput(RecipeInput):
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
extinction = Parameter(0.0, 'Mean atmospheric extinction')
sources = Parameter(
[], 'List of x, y coordinates to measure FWHM', optional=True)
offsets = Offsets_Requirement()
sky_images = Parameter(5, 'Images used to estimate the background'
' before and after current image')
sky_images_sep_time = SkyImageSepTime_Requirement()
check_photometry_levels = Parameter(
[0.5, 0.8], 'Levels to check the flux of the objects')
check_photometry_actions = Parameter(
['warn', 'warn', 'default'], 'Actions to take on images')
class TestBiasCorrectRecipe(EmirRecipe):
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
frame = Product(DataFrameType)
def run(self, rinput):
_logger.info('starting simple bias reduction')
flow = self.init_filters(rinput)
hdu = basic_processing_with_combination(rinput, flow, method=median)
hdr = hdu.header
hdr['NUMRNAM'] = (self.__class__.__name__, 'Numina recipe name')
hdr['NUMRVER'] = (self.__version__, 'Numina recipe version')
hdulist = fits.HDUList([hdu])
result = self.create_result(frame=hdulist)
return result
class RasterSpectraRecipe(EmirRecipe):
"""
Observing mode:
Raster spectra
"""
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
master_spectral_ff = Requirement(prods.MasterSpectralFlat,
'Master spectral flatfield')
st_calibration = Requirement(prods.SlitTransmissionCalibration,
'Slit tranmision calibration')
w_calibration = Requirement(prods.WavelengthCalibration,
'Wavelength calibration')
cube = Product(prods.DataCube)
def run(self, rinput):
return self.create_result(cube=prods.DataCube())
class MicroditheredImageRecipeInput(RecipeInput):
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
extinction = Parameter(0.0, 'Mean atmospheric extinction')
sources = Parameter([],
'List of x, y coordinates to measure FWHM',
optional=True)
offsets = Offsets_Requirement()
iterations = Parameter(4, 'Iterations of the recipe')
sky_images = Parameter(
5, 'Images used to estimate the background before '
'and after current image')
sky_images_sep_time = SkyImageSepTime_Requirement()
check_photometry_levels = Parameter(
[0.5, 0.8], 'Levels to check the flux of the objects')
check_photometry_actions = Parameter(['warn', 'warn', 'default'],
'Actions to take on images')
subpixelization = Parameter(4, 'Number of subdivisions in each pixel side')
window = Parameter([], 'Region of interesting data', optional=True)
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))
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 MultiTwilightFlatRecipe(EmirRecipe):
"""Create a list of twilight flats"""
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
twflatframes = Product(ListOfType(MasterIntensityFlat))
def run(self, rinput):
results = []
self.logger.info('starting multiflat flat reduction')
# Uncomment this line
# to revert to non-ramp
# flow = self.init_filters(rinput)
saturation = 45000.0
iinfo = gather_info_frames(rinput.obresult.frames)
image_groups = {}
self.logger.info('group images by filter')
for idx, info in enumerate(iinfo):
filt = info['filter']
if filt not in image_groups:
self.logger.debug('new filter %s', filt)
image_groups[filt] = []
img = rinput.obresult.frames[idx]
self.logger.debug('image %s in group %s', img, filt)
image_groups[filt].append(img)
for filt, frames in image_groups.items():
self.logger.info('processing filter %s', filt)
# Uncomment this line and comment the following
# to revert to non-ramp
# res = self.run_per_filter(frames, flow)
try:
res = self.run_per_filter_ramp(frames, saturation=saturation)
results.append(res)
except ValueError:
self.logger.info('filter %s cannot be processed', filt)
self.logger.info('end multiflat flat reduction')
result = self.create_result(twflatframes=results)
return result
def run_per_filter(self, frames, flow):
errors = True
self.logger.debug('using errors: %s', errors)
hdulist = basic_processing_with_combination_frames(frames,
flow,
method=median,
errors=errors)
hdr = hdulist[0].header
self.set_base_headers(hdr)
mm = hdulist[0].data.mean()
self.logger.info('mean value of flat is: %f', mm)
hdr['CCDMEAN'] = mm
self.logger.debug('normalize image')
hdulist[0].data /= mm
if errors:
self.logger.debug('normalize VAR extension')
hdulist['variance'].data /= (mm * mm)
return hdulist
def run_per_filter_ramp(self, frames, saturation, errors=False):
imgs = [frame.open() for frame in frames]
return self.run_img_per_filter_ramp(imgs, saturation, errors)
def run_img_per_filter_ramp(self, imgs, saturation, errors=False):
nimages = len(imgs)
if nimages == 0:
raise ValueError('len(images) == 0')
median_frames = numpy.empty((nimages, ))
exptime_frames = []
utc_frames = []
# generate 3D cube
bshape = self.datamodel.shape
flat_frames = numpy.empty((bshape[0], bshape[1], nimages))
for idx, image in enumerate(imgs):
flat_frames[:, :, idx] = image['primary'].data
exptime_frames.append(image[0].header['EXPTIME'])
median_frames[idx] = numpy.median(image['primary'].data)
utc_frames.append(image[0].header['UTC'])
self.logger.debug('image %d exptime %f median %f UTC %s', idx,
exptime_frames[idx], median_frames[idx],
utc_frames[-1])
# filter saturated images
good_images = median_frames < saturation
ngood_images = good_images.sum()
slope_scaled_var = None
slope_scaled_num = None
if ngood_images == 0:
self.logger.warning('We have only %d good images', ngood_images)
raise ValueError('No images under saturation')
elif ngood_images < 2:
self.logger.warning('We have only %d good images', ngood_images)
# Reference image
ref_image = imgs[0]
slope_scaled = numpy.ones(bshape) * exptime_frames[0]
if errors:
slope_scaled_var = numpy.zeros_like(slope_scaled)
slope_scaled_num = numpy.zeros_like(
slope_scaled, dtype='int16') + ngood_images
else:
nsaturated = nimages - good_images.sum()
if nsaturated > 0:
self.logger.debug(
'we have %d images with median value over saturation (%f)',
nsaturated, saturation)
m = flat_frames[:, :, good_images]
# Reshape array to obtain a 2D array
m_r = m.reshape((bshape[0] * bshape[1], ngood_images))
self.logger.debug('fitting slopes with Theil-Sen')
# self.logger.debug('fitting slopes with mean-squares')
# ll = nppol.polyfit(median_frames[good_images], m_r.T, deg=1)
ll = self.filter_nsigma(median_frames[good_images], m_r.T)
slope = ll[1].reshape(bshape)
base = ll[0].reshape(bshape)
# First good frame
index_of_first_good = numpy.nonzero(good_images)[0][0]
slope_scaled = slope * exptime_frames[index_of_first_good]
if errors:
slope_scaled_var = numpy.zeros_like(slope_scaled)
slope_scaled_num = numpy.zeros_like(
slope_scaled, dtype='int16') + ngood_images
cdata = []
for idx, img in enumerate(imgs):
if good_images[idx]:
cdata.append(img)
result = self.compose_result(cdata, slope_scaled, errors,
slope_scaled_var, slope_scaled_num)
return result
def filter_nsigma(self, median_val, image_val, nsigma=10.0, nloop=1):
# Initial estimation
ll = fit_theil_sen(median_val, image_val)
ni = 0
self.logger.debug('initial estimation')
while ni < nloop:
# Prediction
self.logger.debug('loop %d', ni + 1)
base, slope = ll
image_val_pred = base + median_val[:, numpy.newaxis] * slope
image_diff = image_val - image_val_pred
# Compute MAD
mad = compute_mad(image_diff)
sigma_robust = nsigma * 1.4826 * mad
self.logger.debug('compute robust std deviation')
self.logger.debug('min %7.1f max %7.1f mean %7.1f',
sigma_robust.min(), sigma_robust.max(),
sigma_robust.mean())
# Check values over sigma
mask_over = numpy.abs(image_diff) >= sigma_robust[:, numpy.newaxis]
self.logger.debug('values over sigma: %d', mask_over.sum())
# Insert expected values in image
# instead of masking
image_val[mask_over] = image_val_pred[mask_over]
#
self.logger.debug('Theil-Sen fit')
ll = fit_theil_sen(median_val, image_val)
ni += 1
return ll
def compose_result(self,
imgs,
slope_scaled,
errors=False,
slope_scaled_var=None,
slope_scaled_num=None):
self.logger.debug('update result header')
cnum = len(imgs)
method_name = 'Theil-Sen'
base_header = imgs[0][0].header
cdata = imgs
hdu = fits.PrimaryHDU(data=slope_scaled, header=base_header)
self.set_base_headers(hdu.header)
hdu.header['history'] = "Combined %d images using '%s'" % (cnum,
method_name)
hdu.header['history'] = 'Combination time {}'.format(
datetime.datetime.utcnow().isoformat())
for img in cdata:
hdu.header['history'] = "Image {}".format(
self.datamodel.get_imgid(img))
prevnum = base_header.get('NUM-NCOM', 1)
hdu.header['NUM-NCOM'] = prevnum * cnum
hdu.header['UUID'] = str(uuid.uuid1())
# Headers of last image
hdu.header['TSUTC2'] = cdata[-1][0].header['TSUTC2']
# TODO: use BPM to compute mean
mm = hdu.data.mean()
self.logger.info('mean value of flat is: %f', mm)
hdu.header['CCDMEAN'] = mm
self.logger.debug('normalize image')
hdu.data /= mm
if errors:
varhdu = fits.ImageHDU(slope_scaled_var, name='VARIANCE')
num = fits.ImageHDU(slope_scaled_num, name='MAP')
self.logger.debug('normalize VAR extension')
varhdu.data /= (mm * mm)
result = fits.HDUList([hdu, varhdu, num])
else:
result = fits.HDUList([hdu])
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(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 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 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 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 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 DitheredImageRecipe(DirectImageCommon):
"""Recipe for the reduction of imaging mode observations.
Recipe to reduce observations obtained in imaging mode, considering
different possibilities depending on the size of the offsets
between individual images.
In particular, the following observing modes are considered: stare imaging,
nodded beamswitched imaging, and dithered imaging.
A critical piece of information here is a table that clearly specifies
which images can be labeled as *science*, and which ones as *sky*.
Note that some images are used both as *science* and *sky*
(when the size of the targets is small compared to the offsets).
**Observing modes:**
* StareImage
* Nodded/Beam-switched images
* Dithered images
**Inputs:**
* Science frames + [Sky Frames]
* Observing mode name: **stare image**, **nodded beamswitched image**,
or **dithered imaging**
* A table relating each science image with its sky image(s) (TBD if
it's in the FITS header and/or in other format)
* Offsets between them (Offsets must be integer)
* Master Dark
* Bad pixel mask (BPM)
* Non-linearity correction polynomials
* Master flat (twilight/dome flats)
* Master background (thermal background, only in K band)
* Exposure Time (must be the same in all the frames)
* Airmass for each frame
* Detector model (gain, RN, lecture mode)
* Average extinction in the filter
* Astrometric calibration (TBD)
**Outputs:**
* Image with three extensions: final image scaled to the individual
exposure time, variance and exposure time map OR number of images
combined (TBD)
**Procedure:**
Images are corrected from dark, non-linearity and flat. Then, an iterative
process starts:
* Sky is computed from each frame, using the list of sky images of each
science frame. The objects are avoided using a mask (from the second
iteration on).
* The relative offsets are the nominal from the telescope. From the second
iteration on, we refine them using objects of appropriate brightness
(not too bright, not to faint).
* We combine the sky-subtracted images, output is: a new image, a variance
image and a exposure map/number of images used map.
* An object mask is generated.
* We recompute the sky map, using the object mask as an additional input.
From here we iterate (typically 4 times).
* Finally, the images are corrected from atmospheric extinction and flux
calibrated.
* A preliminary astrometric calibration can always be used (using
the central coordinates of the pointing and the plate scale
in the detector).
A better calibration might be computed using available stars (TBD).
"""
obresult = ObservationResultRequirement()
master_bpm = MasterBadPixelMaskRequirement()
master_bias = MasterBiasRequirement()
master_dark = MasterDarkRequirement()
master_flat = MasterIntensityFlatFieldRequirement()
extinction = Extinction_Requirement()
sources = Catalog_Requirement()
offsets = Offsets_Requirement()
iterations = Parameter(4, 'Iterations of the recipe')
sky_images = Parameter(
5, 'Images used to estimate the '
'background before and after current image')
sky_images_sep_time = SkyImageSepTime_Requirement()
check_photometry_levels = Parameter(
[0.5, 0.8], 'Levels to check the flux of the objects')
check_photometry_actions = Parameter(
['warn', 'warn', 'default'], 'Actions to take on images')
frame = Product(DataFrameType)
catalog = Product(SourcesCatalog)
def run(self, recipe_input):
frame, catalog = self.process(recipe_input, window=None, subpix=1,
stop_after=DirectImageCommon.FULLRED)
result = self.create_result(frame=frame, catalog=catalog)
return result
