def do_stage(self, images):
for image in images:
if len(image.data.shape) > 2:
logging_tags = logs.image_config_to_tags(
image, self.group_by_keywords)
logs.add_tag(logging_tags, 'filename',
os.path.basename(image.filename))
n_amps = image.data.shape[0]
nx, ny = get_mosaic_size(image, n_amps)
mosaiced_data = np.zeros((ny, nx), dtype=np.float32)
mosaiced_bpm = np.zeros((ny, nx), dtype=np.uint8)
for i in range(n_amps):
datasec = image.header['DATASEC{0}'.format(i + 1)]
amp_slice = fits_utils.parse_region_keyword(datasec)
logs.add_tag(logging_tags, 'DATASEC{0}'.format(i + 1),
datasec)
detsec = image.header['DETSEC{0}'.format(i + 1)]
mosaic_slice = fits_utils.parse_region_keyword(detsec)
logs.add_tag(logging_tags, 'DETSEC{0}'.format(i + 1),
datasec)
mosaiced_data[mosaic_slice] = image.data[i][amp_slice]
mosaiced_bpm[mosaic_slice] = image.bpm[i][amp_slice]
image.data = mosaiced_data
image.bpm = mosaiced_bpm
image.update_shape(nx, ny)
image.header['NAXIS'] = 2
image.header.pop('NAXIS3')
self.logger.info('Mosaiced image', extra=logging_tags)
return images
def do_stage(self, images):
for image in images:
if len(image.data.shape) > 2:
logging_tags = logs.image_config_to_tags(image, self.group_by_keywords)
logs.add_tag(logging_tags, 'filename', os.path.basename(image.filename))
n_amps = image.data.shape[0]
nx, ny = get_mosaic_size(image, n_amps)
mosaiced_data = np.zeros((ny, nx), dtype=np.float32)
mosaiced_bpm = np.zeros((ny, nx), dtype=np.uint8)
for i in range(n_amps):
datasec = image.header['DATASEC{0}'.format(i + 1)]
amp_slice = fits_utils.parse_region_keyword(datasec)
logs.add_tag(logging_tags, 'DATASEC{0}'.format(i + 1), datasec)
detsec = image.header['DETSEC{0}'.format(i + 1)]
mosaic_slice = fits_utils.parse_region_keyword(detsec)
logs.add_tag(logging_tags, 'DETSEC{0}'.format(i + 1), datasec)
mosaiced_data[mosaic_slice] = image.data[i][amp_slice]
mosaiced_bpm[mosaic_slice] = image.bpm[i][amp_slice]
image.data = mosaiced_data
image.bpm = mosaiced_bpm
image.update_shape(nx, ny)
image.header['NAXIS'] = 2
image.header.pop('NAXIS3')
self.logger.info('Mosaiced image', extra=logging_tags)
return images
def bpm_has_valid_size(bpm, image):
# If 3d, check and make sure the number of extensions is the same
if len(image.data.shape) > 2:
y_slices, x_slices = fits_utils.parse_region_keyword(image.extension_headers[0]['DATASEC'])
is_valid = image.data.shape[0] == bpm.shape[0]
else:
y_slices, x_slices = fits_utils.parse_region_keyword(image.header['DATASEC'])
is_valid = True
# Check if x and y dimensions are less than the datasec
is_valid &= bpm.shape[-1] >= x_slices.stop
is_valid &= bpm.shape[-2] >= y_slices.stop
return is_valid
def do_stage(self, image):
if image.data_is_3d():
logging_tags = {}
nx, ny = get_mosaic_size(image, image.get_n_amps())
mosaiced_data = np.zeros((ny, nx), dtype=np.float32)
mosaiced_bpm = np.zeros((ny, nx), dtype=np.uint8)
x_detsec_limits, y_detsec_limits = get_detsec_limits(
image, image.get_n_amps())
xmin = min(x_detsec_limits) - 1
ymin = min(y_detsec_limits) - 1
for i in range(image.get_n_amps()):
ccdsum = image.extension_headers[i].get('CCDSUM', image.ccdsum)
x_binning, y_binning = ccdsum.split(' ')
datasec = image.extension_headers[i]['DATASEC']
amp_slice = fits_utils.parse_region_keyword(datasec)
logging_tags['DATASEC{0}'.format(i + 1)] = datasec
detsec = image.extension_headers[i]['DETSEC']
mosaic_slice = get_windowed_mosaic_slices(
detsec, xmin, ymin, x_binning, y_binning)
logging_tags['DATASEC{0}'.format(i + 1)] = detsec
mosaiced_data[mosaic_slice] = image.data[i][amp_slice]
mosaiced_bpm[mosaic_slice] = image.bpm[i][amp_slice]
image.data = mosaiced_data
image.bpm = mosaiced_bpm
# Flag any missing data
image.bpm[image.data == 0.0] = 1
image.update_shape(nx, ny)
update_naxis_keywords(image, nx, ny)
logger.info('Mosaiced image', image=image, extra_tags=logging_tags)
return image
def get_windowed_mosaic_slices(detsec, xmin, ymin, x_binning, y_binning):
"""
Get the array slices to map the image coordinates for a given detector section to the output mosaic coordinates.
Parameters
----------
detsec: str
Detector section keyword from the header in IRAF section format.
xmin: int
Minimum x detector coordinate for the mosaic to go the bottom left of the array
ymin: int
Minimum y detector coordinate for the mosaic to go the bottom left of the array
x_binning: int
Binning factor in the x direction for the output mosaic
y_binning: int
Binning factor in the y direction for the output mosaic
Returns
-------
y_slice, x_slice: slice, slice
Slice to index the output mosaic data array for the given detsec.
"""
unbinned_slice = fits_utils.parse_region_keyword(detsec)
return (slice((unbinned_slice[0].start - ymin) // int(y_binning),
(unbinned_slice[0].stop - ymin) // int(y_binning),
unbinned_slice[0].step),
slice((unbinned_slice[1].start - xmin) // int(x_binning),
(unbinned_slice[1].stop - xmin) // int(x_binning),
unbinned_slice[1].step))
def bpm_has_valid_size(bpm, image):
# If 3d, check and make sure the number of extensions is the same
if len(image.data.shape) > 2:
y_slices, x_slices = fits_utils.parse_region_keyword(
image.extension_headers[0]['DATASEC'])
is_valid = image.data.shape[0] == bpm.shape[0]
else:
y_slices, x_slices = fits_utils.parse_region_keyword(
image.header['DATASEC'])
is_valid = True
# Check if x and y dimensions are less than the datasec
is_valid &= bpm.shape[-1] >= x_slices.stop
is_valid &= bpm.shape[-2] >= y_slices.stop
return is_valid
def _subtract_overscan_2d(image):
overscan_region = fits_utils.parse_region_keyword(image.header.get('BIASSEC'))
if overscan_region is not None:
overscan_level = stats.sigma_clipped_mean(image.data[overscan_region], 3)
image.header['L1STATOV'] = (1, 'Status flag for overscan correction')
else:
overscan_level = 0.0
image.header['L1STATOV'] = (0, 'Status flag for overscan correction')
image.header['OVERSCAN'] = (overscan_level, 'Overscan value that was subtracted')
image.data -= overscan_level
return overscan_level
def _subtract_overscan_2d(image):
overscan_region = fits_utils.parse_region_keyword(image.header.get('BIASSEC'))
if overscan_region is not None:
overscan_level = stats.sigma_clipped_mean(image.data[overscan_region], 3)
image.header['L1STATOV'] = (1, 'Status flag for overscan correction')
else:
overscan_level = 0.0
image.header['L1STATOV'] = (0, 'Status flag for overscan correction')
image.header['OVERSCAN'] = (overscan_level, 'Overscan value that was subtracted')
image.data -= overscan_level
return overscan_level
def _subtract_overscan_3d(image, i):
overscan_region = fits_utils.parse_region_keyword(image.extension_headers[i].get('BIASSEC'))
if overscan_region is not None:
overscan_level = stats.sigma_clipped_mean(image.data[i][overscan_region], 3)
image.header['L1STATOV'] = (1, 'Status flag for overscan correction')
else:
overscan_level = 0.0
image.header['L1STATOV'] = (0, 'Status flag for overscan correction')
overscan_comment = 'Overscan value that was subtracted from Q{0}'.format(i + 1)
image.header['OVERSCN{0}'.format(i + 1)] = (overscan_level, overscan_comment)
image.data[i] -= overscan_level
return overscan_level
def _subtract_overscan_3d(image, i):
overscan_region = fits_utils.parse_region_keyword(image.extension_headers[i].get('BIASSEC'))
if overscan_region is not None:
overscan_level = stats.sigma_clipped_mean(image.data[i][overscan_region], 3)
image.header['L1STATOV'] = (1, 'Status flag for overscan correction')
else:
overscan_level = 0.0
image.header['L1STATOV'] = (0, 'Status flag for overscan correction')
overscan_comment = 'Overscan value that was subtracted from Q{0}'.format(i + 1)
image.header['OVERSCN{0}'.format(i + 1)] = (overscan_level, overscan_comment)
image.data[i] -= overscan_level
return overscan_level
def _trim_image(image):
trimsec = fits_utils.parse_region_keyword(image.header['TRIMSEC'])
image.data = image.data[trimsec]
image.bpm = image.bpm[trimsec]
# Update the NAXIS and CRPIX keywords
image.header['NAXIS1'] = trimsec[1].stop - trimsec[1].start
image.header['NAXIS2'] = trimsec[0].stop - trimsec[0].start
image.header['CRPIX1'] -= trimsec[1].start
image.header['CRPIX2'] -= trimsec[0].start
image.header['L1STATTR'] = (1, 'Status flag for overscan trimming')
return image.header['NAXIS1'], image.header['NAXIS2']
def get_mosaic_size(image, n_amps):
x_pixel_limits = []
y_pixel_limits = []
for i in range(n_amps):
header_keyword = image.header['DETSEC{0}'.format(i + 1)]
y_slice, x_slice = fits_utils.parse_region_keyword(header_keyword)
x_pixel_limits.append(x_slice.start + 1)
x_pixel_limits.append(x_slice.stop)
y_pixel_limits.append(y_slice.start + 1)
y_pixel_limits.append(y_slice.stop)
# Clean out any Nones
x_pixel_limits = [x if x is not None else 1 for x in x_pixel_limits]
y_pixel_limits = [y if y is not None else 1 for y in y_pixel_limits]
nx = np.max(x_pixel_limits) - np.min(x_pixel_limits) + 1
ny = np.max(y_pixel_limits) - np.min(y_pixel_limits) + 1
return nx, ny
def get_mosaic_size(image, n_amps):
x_pixel_limits = []
y_pixel_limits = []
for i in range(n_amps):
header_keyword = image.header['DETSEC{0}'.format(i + 1)]
y_slice, x_slice = fits_utils.parse_region_keyword(header_keyword)
x_pixel_limits.append(x_slice.start + 1)
x_pixel_limits.append(x_slice.stop)
y_pixel_limits.append(y_slice.start + 1)
y_pixel_limits.append(y_slice.stop)
# Clean out any Nones
x_pixel_limits = [x if x is not None else 1 for x in x_pixel_limits]
y_pixel_limits = [y if y is not None else 1 for y in y_pixel_limits]
nx = np.max(x_pixel_limits) - np.min(x_pixel_limits) + 1
ny = np.max(y_pixel_limits) - np.min(y_pixel_limits) + 1
return nx, ny
def _trim_image(image):
trimsec = fits_utils.parse_region_keyword(image.header['TRIMSEC'])
if trimsec is not None:
image.data = image.data[trimsec]
image.bpm = image.bpm[trimsec]
# Update the NAXIS and CRPIX keywords
image.header['NAXIS1'] = trimsec[1].stop - trimsec[1].start
image.header['NAXIS2'] = trimsec[0].stop - trimsec[0].start
if 'CRPIX1' in image.header:
image.header['CRPIX1'] -= trimsec[1].start
if 'CRPIX2' in image.header:
image.header['CRPIX2'] -= trimsec[0].start
image.header['L1STATTR'] = (1, 'Status flag for overscan trimming')
else:
logger.warning('TRIMSEC was not defined.', image=image, extra_tags={'trimsec': image.header['TRIMSEC']})
image.header['L1STATTR'] = (0, 'Status flag for overscan trimming')
return image.header['NAXIS1'], image.header['NAXIS2']
def _trim_image(image):
trimsec = fits_utils.parse_region_keyword(image.header['TRIMSEC'])
if trimsec is not None:
image.data = image.data[trimsec]
image.bpm = image.bpm[trimsec]
# Update the NAXIS and CRPIX keywords
image.header['NAXIS1'] = trimsec[1].stop - trimsec[1].start
image.header['NAXIS2'] = trimsec[0].stop - trimsec[0].start
image.header['CRPIX1'] -= trimsec[1].start
image.header['CRPIX2'] -= trimsec[0].start
image.header['L1STATTR'] = (1, 'Status flag for overscan trimming')
else:
logging_tags = logs.image_config_to_tags(image, None)
logs.add_tag(logging_tags, 'filename', os.path.basename(image.filename))
logs.add_tag(logging_tags, 'trimsec', image.header['TRIMSEC'])
logger.warning('TRIMSEC was not defined.', extra=logging_tags)
image.header['L1STATTR'] = (0, 'Status flag for overscan trimming')
return image.header['NAXIS1'], image.header['NAXIS2']
def _trim_image(image):
trimsec = fits_utils.parse_region_keyword(image.header['TRIMSEC'])
if trimsec is not None:
image.data = image.data[trimsec]
image.bpm = image.bpm[trimsec]
# Update the NAXIS and CRPIX keywords
image.header['NAXIS1'] = trimsec[1].stop - trimsec[1].start
image.header['NAXIS2'] = trimsec[0].stop - trimsec[0].start
if 'CRPIX1' in image.header:
image.header['CRPIX1'] -= trimsec[1].start
if 'CRPIX2' in image.header:
image.header['CRPIX2'] -= trimsec[0].start
image.header['L1STATTR'] = (1, 'Status flag for overscan trimming')
else:
logger.warning('TRIMSEC was not defined.',
image=image,
extra_tags={'trimsec': image.header['TRIMSEC']})
image.header['L1STATTR'] = (0, 'Status flag for overscan trimming')
return image.header['NAXIS1'], image.header['NAXIS2']
def get_mosaic_size(image, n_amps):
x_pixel_limits = []
y_pixel_limits = []
for i in range(n_amps):
detsec_keyword = image.extension_headers[i]['DETSEC']
detsec = fits_utils.parse_region_keyword(detsec_keyword)
if detsec is not None:
x_pixel_limits.append(detsec[1].start + 1)
x_pixel_limits.append(detsec[1].stop)
y_pixel_limits.append(detsec[0].start + 1)
y_pixel_limits.append(detsec[0].stop)
else:
x_pixel_limits.append(None)
y_pixel_limits.append(None)
# Clean out any Nones
x_pixel_limits = [x if x is not None else 1 for x in x_pixel_limits]
y_pixel_limits = [y if y is not None else 1 for y in y_pixel_limits]
nx = np.max(x_pixel_limits) - np.min(x_pixel_limits) + 1
ny = np.max(y_pixel_limits) - np.min(y_pixel_limits) + 1
return nx, ny
def get_mosaic_size(image, n_amps):
x_pixel_limits = []
y_pixel_limits = []
for i in range(n_amps):
detsec_keyword = image.extension_headers[i]['DETSEC']
detsec = fits_utils.parse_region_keyword(detsec_keyword)
if detsec is not None:
x_pixel_limits.append(detsec[1].start + 1)
x_pixel_limits.append(detsec[1].stop)
y_pixel_limits.append(detsec[0].start + 1)
y_pixel_limits.append(detsec[0].stop)
else:
x_pixel_limits.append(None)
y_pixel_limits.append(None)
# Clean out any Nones
x_pixel_limits = [x if x is not None else 1 for x in x_pixel_limits]
y_pixel_limits = [y if y is not None else 1 for y in y_pixel_limits]
nx = np.max(x_pixel_limits) - np.min(x_pixel_limits) + 1
ny = np.max(y_pixel_limits) - np.min(y_pixel_limits) + 1
return nx, ny
def get_detsec_limits(image, n_amps):
"""
Parse the detector section keyword from each extension and return a list of x and y detector positions
Parameters
----------
image: banzai.images.Image
image with detector section header keywords to parse
n_amps: int
Number of amplifiers (fits extensions)
Returns
-------
x_detsec_limits, y_detsec_limits: list. list
x and y list of the detector positions in the each of the DETSEC keywords
"""
x_pixel_limits = []
y_pixel_limits = []
for i in range(n_amps):
detsec_keyword = image.extension_headers[i]['DETSEC']
detsec = fits_utils.parse_region_keyword(detsec_keyword)
if detsec is not None:
# Convert starts from 0 indexed to 1 indexed
x_pixel_limits.append(detsec[1].start + 1)
y_pixel_limits.append(detsec[0].start + 1)
# Note python is not inclusive at the end (unlike IRAF)
x_pixel_limits.append(detsec[1].stop)
y_pixel_limits.append(detsec[0].stop)
else:
x_pixel_limits.append(None)
y_pixel_limits.append(None)
# Clean out any Nones
x_pixel_limits = [x if x is not None else 1 for x in x_pixel_limits]
y_pixel_limits = [y if y is not None else 1 for y in y_pixel_limits]
return x_pixel_limits, y_pixel_limits