def blendfitsdata(input_list, output_model):
"""
Primary interface for JWST pipeline use of fitsblender.blendheaders
This function will update the output_model datamodel with the blended metadata
from the list of FITS objects generated from the input_list of filenames.
"""
new_hdrs, new_table = blendheaders.get_blended_headers(input_list)
# Now merge the keyword values from new_hdrs into the metatdata for the
# output datamodel
#
# start by building dict which maps all FITS keywords in schema to their
# attribute in the schema
fits_dict = schema.build_fits_dict(output_model.schema)
# Now assign values from new_hdrs to output_model.meta using fits_dict map
for hdr in new_hdrs:
for kw in hdr:
if kw in fits_dict:
output_model[fits_dict[kw]] = hdr[kw]
# Now, append HDRTAB as new element in datamodel
new_schema = build_tab_schema(new_table)
output_model.add_schema_entry('hdrtab', new_schema)
output_model.hdrtab = fits_support.from_fits_hdu(new_table, new_schema)
def blendmetadata(input_list, output_model):
""" Primary interface for JWST pipeline use of fitsblender.blendheaders
This function will update the output_model datamodel with the blended metadata
from the list of FITS objects generated from the input_list of filenames.
"""
new_hdrs,new_table = blendheaders.get_blended_headers(input_list)
# Now merge the keyword values from new_hdrs into the metatdata for the
# output datamodel
#
# start by building dict which maps all FITS keywords in schema to their
# attribute in the schema
fits_dict = schema.build_fits_dict(output_model.schema)
# Now assign values from new_hdrs to output_model.meta using fits_dict map
for hdr in new_hdrs:
for kw in hdr:
if kw in fits_dict:
output_model[fits_dict[kw]] = hdr[kw]
# Now, append HDRTAB as new element in datamodel
new_schema = build_tab_schema(new_table)
output_model.add_schema_entry('hdrtab',new_schema)
output_model.hdrtab = fits_support.from_fits_hdu(new_table, new_schema) ## CHANGED
def getTemplates(fnames, blend=True):
""" Process all headers to produce a set of combined headers
that follows the rules defined by each instrument.
"""
if not blend:
newhdrs = blendheaders.getSingleTemplate(fnames[0])
newtab = None
else:
# apply rules to create final version of headers, plus table
newhdrs, newtab = blendheaders.get_blended_headers(inputs=fnames)
cleanTemplates(newhdrs[1], newhdrs[2], newhdrs[3])
return newhdrs, newtab
def getTemplates(fnames, blend=True):
""" Process all headers to produce a set of combined headers
that follows the rules defined by each instrument.
"""
if not blend:
newhdrs = blendheaders.getSingleTemplate(fnames[0])
newtab = None
else:
# apply rules to create final version of headers, plus table
newhdrs, newtab = blendheaders.get_blended_headers(inputs=fnames)
cleanTemplates(newhdrs[1],newhdrs[2],newhdrs[3])
return newhdrs, newtab
def getTemplates(fnames, blend=True, rules_file=None):
""" Process all headers to produce a set of combined headers
that follows the rules defined by each instrument.
"""
if not blend:
newhdrs = blendheaders.getSingleTemplate(fnames[0])
newtab = None
else:
# apply rules to create final version of headers, plus table
# TODO: NEED to add pointer to dataset-specific rules file as
# 'rules_file' parameter.
newhdrs, newtab = blendheaders.get_blended_headers(
inputs=fnames, rules_file=rules_file)
cleanTemplates(newhdrs[1], newhdrs[2], newhdrs[3])
return newhdrs, newtab