Library Use
This section describes the formal top level interfaces for CRDS intended as the main entry points for the calibration software or basic use. Functions at this level should be assumed to require network connectivity with the CRDS server.
To function correctly, these API calls may require the user to set the
environment variables CRDS_SERVER_URL and CRDS_PATH. See the section on
Installation for more details.
crds.getrecommendations()
Given dataset header containing parameters required to determine best references, and optionally a specific .pmap to use as the best references context, and optionally a list of the reference types for which reference files are to be determined, getrecommendations() will determine best references and return a mapping from reference types to reference file basenames:
def getrecommendations(parameters, reftypes=None, context=None, ignore_cache=False, observatory="jwst", fast=False): """ getrecommendations() returns the best references for the specified `parameters` and pipeline `context`. Unlike getreferences(), getrecommendations() does not attempt to cache the files locally. parameters { str: str,int,float,bool, ... } `parameters` should be a dictionary-like object mapping best reference matching parameters to their values for this dataset. reftypes [ str, ... ] If `reftypes` is None, return all possible reference types. Otherwise return the reference types specified by `reftypes`. context str Specifies the pipeline context, i.e. specific version (.pmap) of CRDS rules used to do the best references match. If `context` is None, use the latest available context. ignore_cache bool If `ignore_cache` is True, download files from server even if already present. observatory str nominally 'jwst' or 'hst'. fast bool If fast is True, skip verbose output, parameter screening, implicit config update, and bad reference checking. Returns { reftype : bestref_basename, ... } returns a mapping from types requested in `reftypes` to the path for each cached reference file. """
crds.getreferences()
Given dataset header containing parameters required to determine best references, and optionally a specific .pmap to use as the best references context, and optionally a list of the reference types for which reference files are to be determined, getreferences() will determine best references, cache them on the local file system, and return a mapping from reference types to reference file paths:
def getreferences(parameters, reftypes=None, context=None, ignore_cache=False, observatory="jwst"): """Return the mapping from the requested `reftypes` to their corresponding best reference file paths appropriate for a dataset described by `parameters` with CRDS rules defined by `context`:: parameters : A mapping of parameter names to parameter value strings for parameters which define best reference file matches. { str : str, int, float, bool } e.g. { 'INSTRUME' : 'ACS', 'CCDAMP' : 'ABCD', 'CCDGAIN' : '2.0', ... } reftypes : A list of reference type names. For HST these are the keywords used to record reference files in dataset headers. For JWST, these are the identifiers which will appear in instrument contexts and reference mappings. e.g. [ 'darkfile', 'biasfile'] If reftypes is None, return all reference types defined by the instrument mapping for the instrument specified in `parameters`. context : The name of the pipeline context mapping which should be used to define best reference lookup rules, or None. If `context` is None, use the latest operational pipeline mapping. str e.g. 'hst_0037.pmap' ignore_cache : If True, download all required mappings and references from the CRDS server. If False, download only those files not already in the local caches. observatory : The name of the observatory this query applies to, needed to support both 'hst' and 'jwst' from a single server. Returns ------- a mapping from reftypes to cached best reference file paths. { str : str } e.g. { 'biasfile' : '/path/to/file/hst_acs_biasfile_0042.fits', 'darkfile' : '/path/to/file/hst_acs_darkfile_0056.fits', } """
crds.assign_bestrefs()
The assign_bestrefs() higher level function call simulates the behavior of the
crds bestrefs program used in the archive pipeline for HST. Essentially, it
populates the headers of FITS dataset files with the best choice for each
reference type:
def assign_bestrefs(filepaths, context=None, reftypes=(), sync_references=False, verbosity=-1): """Assign best references to FITS files specified by `filepaths` filling in appropriate reference type keywords. Define best references using either .pmap `context` or the default CRDS operational context if context=None. If `reftypes` is defined, assign bestrefs to only the listed reftypes, otherwise assign all reftypes. If `sync_references` is True, download any missing reference files to the CRDS cache. Verbosity defines the level of CRDS log output: verbosity=-3 feeling lucky, no output verbosity=-2 only errors verbosity=-1 only warnings and errors verbosity=0 info, warnings, and errors verbosity=10 info + minimal progress output verbosity=30 info + simplified bestref assignments verbosity=50 info + keywords + exact values (standard) verbosity=60 info + bestrefs elimination ... -3 <= verbosity <= 100 NOTE: While assign_bestrefs() may work for JWST, it is primarily intended for HST and does not precisely simulate the actions performed by the JWST CAL s/w to handle reference files. The underlying machinery is the same, but header updates are not guaranteed to be identical, particularly regarding the reference types which are assigned values. Returns count of errors """
crds.get_default_context()
get_default_context() returns the name of the pipeline mapping which is
currently in operational use.
The default context defines the matching rules used to determine best reference files for a given set of parameters:
def get_default_context(): """Return the name of the latest pipeline mapping in use for processing files. Returns ------- pipeline context name e.g. 'hst_0007.pmap' """
Basic Operations on Mappings
Loading Rmaps
Perhaps the most fundamental thing you can do with a CRDS mapping is create an active object version by loading the file:
>>> import crds.rmap as rmap >>> hst = rmap.load_mapping("hst.pmap")
The load_mapping() function will take any mapping and instantiate it and all of
its child mappings into various nested Mapping subclasses: PipelineContext,
InstrumentContext, or ReferenceMapping.
Loading an rmap implicitly screens it for invalid syntax and requires that the rmap’s checksum (sha1sum) be valid by default.
Since HST has on the order of 70 mappings, this is a fairly slow process requiring a couple seconds to execute. In order to speed up repeated access to the same Mapping, there’s a mapping cache maintained by the rmap module and accessed like this:
>>> hst = rmap.get_cached_mapping("hst.pmap")
The behavior of the cached mapping is identical to the “loaded” mapping and subsequent calls are nearly instant.
Seeing Referenced Names
CRDS Mapping classes all know how to show you the files referenced by themselves and their descendents. The ACS instrument context has a reference mapping for each of it’s associated file kinds:
>>> acs = rmap.get_cached_mapping("hst_acs.imap") >>> acs.mapping_names() ['hst_acs.imap', 'hst_acs_idctab.rmap', 'hst_acs_darkfile.rmap', 'hst_acs_atodtab.rmap', 'hst_acs_cfltfile.rmap', 'hst_acs_spottab.rmap', 'hst_acs_mlintab.rmap', 'hst_acs_dgeofile.rmap', 'hst_acs_bpixtab.rmap', 'hst_acs_oscntab.rmap', 'hst_acs_ccdtab.rmap', 'hst_acs_crrejtab.rmap', 'hst_acs_pfltfile.rmap', 'hst_acs_biasfile.rmap', 'hst_acs_mdriztab.rmap']
The ACS atod reference mapping (rmap) refers to 4 different reference files:
>>> acs_atod = rmap.get_cached_mapping("hst_acs_atodtab.rmap") >>> acs_atod.reference_names() ['j4d1435hj_a2d.fits', 'kcb1734hj_a2d.fits', 'kcb1734ij_a2d.fits', 't3n1116mj_a2d.fits']
Computing Best References
The primary function of CRDS is the computation of best reference files based
upon a dictionary of dataset metadata. Hence, both an InstrumentContext and a
ReferenceMapping can meaningfully return the best references for a dataset based
upon a parameter dictionary. It’s possible to define a header as any Python
dictionary provided you have sufficient knowledge of the parameters:
>>> hdr = { ... what matters most ... }
On the other hand, if your dataset is a FITS file and you want to do something quick and dirty, you can ask CRDS what dataset metadata may matter for determining best references:
>>> hdr = acs.get_minimum_header("test_data/j8bt05njq_raw.fits") {'CCDAMP': 'C', 'CCDGAIN': '2.0', 'DATE-OBS': '2002-04-13', 'DETECTOR': 'HRC', 'FILTER1': 'F555W', 'FILTER2': 'CLEAR2S', 'FW1OFFST': '0.0', 'FW2OFFST': '0.0', 'FWSOFFST': '0.0', 'LTV1': '19.0', 'LTV2': '0.0', 'NAXIS1': '1062.0', 'NAXIS2': '1044.0', 'OBSTYPE': 'IMAGING', 'TIME-OBS': '18:16:35'}
Here we say may matter because CRDS is currently unaware of specific instrument configurations and is returning metadata about filekinds which may be inappropriate.
Once you have your dataset parameters, you can ask an InstrumentContext for
the best references for all filekinds for that instrument:
>>> acs.get_best_references(hdr) {'atodtab': 'kcb1734ij_a2d.fits', 'biasfile': 'm4r1753rj_bia.fits', 'bpixtab': 'm8r09169j_bpx.fits', 'ccdtab': 'o1515069j_ccd.fits', 'cfltfile': 'NOT FOUND n/a', 'crrejtab': 'n4e12510j_crr.fits', 'darkfile': 'n3o1059hj_drk.fits', 'dgeofile': 'o8u2214mj_dxy.fits', 'flshfile': 'NOT FOUND n/a', 'idctab': 'p7d1548qj_idc.fits', 'imphttab': 'vbb18105j_imp.fits', 'mdriztab': 'ub215378j_mdz.fits', 'mlintab': 'NOT FOUND n/a', 'oscntab': 'm2j1057pj_osc.fits', 'pfltfile': 'o3u1448rj_pfl.fits', 'shadfile': 'kcb1734pj_shd.fits', 'spottab': 'NOT FOUND n/a'}
In the above results, FITS files are the recommended best references, while a value of “NOT FOUND n/a” indicates that no result was expected for the current instrument mode as defined in the header. Other values of “NOT FOUND xxx” include an error message xxx which hints at why no result was found, such as an invalid dataset parameter value or simply a matching failure.
You can ask a ReferenceMapping for the best reference for single the filekind
it manages:
>>> acs_atod.get_best_ref(hdr) >>> 'kcb1734ij_a2d.fits'
Often it is convenient to simply refer to a pipeline/observatory context file,
and hence PipelineContext can also return the best references for a dataset,
but this is really just shorthand for returning the best references for the
instrument of that dataset:
>>> hdr = hst.get_minimum_header("test_data/j8bt05njq_raw.fits") >>> hst.get_best_references(hdr) ... for this hdr, same as acs.get_best_references(hdr) ...
Here it is critical to call get_minimum_header on the pipeline context, hst, because this will make it include the “INSTRUME” parameter needed to choose the ACS instrument.
Mapping Checksums
CRDS mappings contain sha1sum checksums over the entire contents of the mapping, with the exception of the checksum itself. When a CRDS Mapping of any kind is loaded, the checksum is transparently verified to ensure that the Mapping contents are intact.
Ignoring Checksums!
Ordinarily, during pipeline operations, ignoring checksums should not be done. Ironically though, the first thing you may want to do as a developer is ignore the checksum while you load a mapping you’ve edited:
>>> pipeline = rmap.load_mapping("hst.pmap", ignore_checksum=True)
Alternately you can set an environment variable to ignore the mapping checksum while you iterate on new versions of the mapping:
$ export CRDS_IGNORE_MAPPING_CHECKSUM=1
Adding Checksums
Once you’ve finished your masterpiece ReferenceMapping, it can be sealed with
a checksum like this:
$ crds checksum /where/it/really/is/hst_acs_my_masterpiece.rmap