Source code for jwst.model_blender.blendmeta

"""blendmeta - Merge metadata from multiple models.

    This module will create a new metadata instance and table from a
    list of input datamodels or filenames.
from collections import OrderedDict

from import fits

from .. import datamodels
from .. import associations
from ..datamodels import fits_support
from ..datamodels import schema as dm_schema

from .blendrules import KeywordRules

EMPTY_LIST = [None, '', ' ', 'INDEF', 'None']

__doctest_skip__ = ['blendmodels']

# Primary functional interface for the code
[docs]def blendmodels(product, inputs=None, output=None, verbose=False): """ Run main interface for blending metatdata from multiple models. Blend models that went into creating the original drzfile into a new metadata instance with a table that contains attribute values from all input datamodels. The product will be used to determine the names of the input models, should no filenames be provided in the 'inputs' parameter. The product will be updated 'in-place' with the new metadata attributes and FITS BinTableHDU table. The blended FITS table, with extname=HDRTAB, has 1 column for each metadata attribute recorded from the input models, one row for each input model, and column names are the FITS keywords for that metadata attribute. For example, values from `meta.observation.time` would be stored in the `TIME-OBS` column. Rules for what function to use to determine the blended output attribute value and what metadata attributes should be used as columns in the blended FITS table are defined in the datamodel schema. NOTE ==== Custom rules for a metadata value should be computed by the calling routine and used to update the metadata in the output model AFTER calling this function. Parameters ---------- product : str Name of combined product with metadata that needs updating. This can be specified as a single filename. When no value for `inputs` has been provided, this file will also evaluate `meta.asn` to determine the names of the input datamodels whose metadata need to be blended to create the new combined metadata. inputs : list, optional This can be either a list of filenames or a list of DataModels objects. If provided, the filenames provided in this list will be used to get the metadata which will be blended into the final output metadata. output : str, optional If provided, update `meta.filename` in the blended `product` to define what file this model will get written out to. verbose : bool, optional [Default: False] Print out additional messages during processing when specified. Example ------- This example shows how to blend the metadata from a set of DataModels already read in memory for the product created by the `resample` step. This example relies on the Association file used as the input to the `resample` step to specify all the inputs for blending using the following syntax:: >>> from .. import datamodels >>> asnfile = "jw99999-a3001_20170327t121212_coron3_001_asn.json" >>> asn = >>> input_models = [asn[3],asn[4]] # we know the last datasets are SCIENCE >>> blendmodels(asn.meta.resample.output, inputs=input_models) Alternatively, the filenames for all the inputs could be provided directly instead using:: >>> from ..associations import load_asn >>> asn = load_asn(open(asnfile)) >>> input_names = [i['expname'] for i in asn['products'][0]['members'][3:]] >>> blendmodels(asn['products'][0]['name'], inputs=input_names) """ if inputs in EMPTY_LIST: input_filenames = extract_filenames_from_product(product) inputs = [ for i in inputs] # return datamodels else: if isinstance(inputs, datamodels.DataModel): input_filenames = [i.meta.filename for i in inputs] else: input_filenames = inputs # assume list of filenames as input if verbose: print('Creating blended metadata from: ') for i in input_filenames: print('\t{}'.format(i)) newmeta, newtab = get_blended_metadata(inputs, verbose=verbose) # open product to update metadata from input models used to create product if isinstance(product, str): output_model = else: output_model = product ''' NOTE 17-Jan-2017: Effort needs to be made to insure that new blended values conform to the definitions of the attribute as provided by the schema. This will address #1650 for the jwst package. ''' # Start by identifying elements of the model which need to be ignored ignore_list = _build_schema_ignore_list(newmeta._schema) ignore_list += ['meta.wcs'] # Necessary since meta.wcs is not in schema # Now assign values from new_hdrs to output_model.meta flat_new_metadata = newmeta.to_flat_dict() for attr in flat_new_metadata: attr_use = not [attr.startswith(i) for i in ignore_list].count(True) if attr.startswith('meta') and attr_use: try: output_model[attr] = newmeta[attr] except KeyError: # Ignore keys that are in the asdf tree but not in the schema pass # Apply any user-specified filename for output product if output: output_model.meta.filename = output else: # Otherwise, determine output filename from metadata output = output_model.meta.filename # Now, append HDRTAB as new element in datamodel newtab_schema = build_tab_schema(newtab) if hasattr(output_model, 'hdrtab'): del output_model.hdrtab output_model.add_schema_entry('hdrtab', newtab_schema) output_model.hdrtab = fits_support.from_fits_hdu(newtab, newtab_schema) # Clean up for the next run del newmeta, newtab
[docs]def get_blended_metadata(input_models, verbose=False): """ Return a blended metadata instance and table based on the input datamodels. This will serve as the primary interface for blending datamodels. Parameters ---------- input_models : list Either a single list of filenames from which to extract the metadata to be blended, or a list of `datamodels.DataModel` objects to be blended. The input models are assumed to have the blending rules defined as an integral part of the schema definition for the model. Returns ------- metadata : list A list of blended metadata instances, one for each i new_table : object Single fits.TableHDU object that contains the combined results from all input headers(extension). Each row will correspond to an image, and each column corresponds to a single keyword listed in the rules. """ if not isinstance(input_models, list) and \ not isinstance(input_models, datamodels.ModelContainer): input_models = [input_models] # Turn input filenames into a set of metadata objects if isinstance(input_models[0], str): # convert `input_models` to a list of datamodels input_models = [ for i in input_models] num_files = len(input_models) # Determine what blending rules need to be merged to create the final # blended headers. There will be a separate set of rules for each # instrument, and all rules get merged into a composite set of rules that # get applied to all input headers regardless of instrument. # # Instrument identification will be extracted from the INSTRUME keyword # from the PRIMARY header of each input # icache = {} for i in range(num_files): model = input_models[i] inst = if inst not in icache: # initialize the appropriate class for this data's instrument inst_class = KeywordRules(model) # Interpret rules for this class based on image that # initialized this instrument's rules inst_class.interpret_rules(model) # Now add this interpreted class to the cache icache[inst] = inst_class # Create final merged set of rules final_rules = None for inst in icache: if final_rules is None: final_rules = icache[inst] else: final_rules.merge(icache[inst]) # apply rules to datamodels metadata new_meta, newtab = final_rules.apply(input_models) if len(newtab) > 0: # Now merge the results for all the tables into single table extension new_table = fits.BinTableHDU.from_columns(newtab) new_table.header['EXTNAME'] = 'HDRTAB' else: new_table = None return new_meta, new_table
[docs]def cat_headers(hdr1, hdr2): """ Create new `` object from concatenating 2 input Headers """ nhdr = hdr1.copy() for c in nhdr.append(c) return fits.Header(nhdr)
[docs]def extract_filenames_from_product(product): """ Returns the list of filenames with extensions of input observations that were used to generate the product. """ asn_table = product.meta.asn.table_name asn = associations.load_asn(asn_table) prod = asn['products'][0] fnames = [m['expname'] for m in prod['members'] if m['exptype'].upper() == 'SCIENCE'] return fnames
[docs]def build_tab_schema(new_table): """Return new schema definition that describes the input table.""" hdrtab = OrderedDict() hdrtab['title'] = 'Combined header table' hdrtab['fits_hdu'] = 'HDRTAB' datatype = [] for col in new_table.columns: cname = ctype = convert_dtype(str(col.dtype)) c = OrderedDict() c['name'] = cname c['datatype'] = ctype datatype.append(c) hdrtab['datatype'] = datatype return hdrtab
[docs]def convert_dtype(value): """Convert numarray column dtype into YAML-compatible format description""" if 'S' in value: # working with a string description str_len = int(value[value.find('S') + 1:]) new_dtype = ['ascii', str_len] else: new_dtype = str(value) return new_dtype
def _build_schema_ignore_list(schema): """ Create a list of metadata that should be ignored when blending. Parameters ---------- schema : JSON schema fragment The schema in which to search. Returns ------- results : list List with schema attributes that needs to be ignored """ def build_rules_list(subschema, path, combiner, ctx, recurse): # Only interpret elements of the meta component of the model if len(path) > 1 and path[0] == 'meta' and 'items' not in path: attr = '.'.join(path) if subschema.get('properties'): return # Ignore ObjectNodes kwtype = subschema.get('type') if kwtype == 'array': results.append(attr) else: return results = [] dm_schema.walk_schema(schema, build_rules_list, results) return results