"""
Code dealing with spectra (the actual data), in particular in the spectral
data model (sdm).
This assumes Spectral Data Model Version 1, but doesn't use very much of it.
"""
#c Copyright 2008-2023, the GAVO project <gavo@ari.uni-heidelberg.de>
#c
#c This program is free software, covered by the GNU GPL. See the
#c COPYING file in the source distribution.
import datetime
import io
import os
from gavo import base
from gavo import formats
from gavo import rsc
from gavo import svcs
from gavo import utils
from gavo.formats import fitstable
from gavo.protocols import products
# MIME types we can generate *from* SDM-compliant data; the values are
# either keys for formats.formatData, or None if we have special
# handling below.
GETDATA_FORMATS = {
base.votableType: "votable",
"application/x-votable+xml;serialization=tabledata": "votabletd",
"text/plain": "tsv",
"text/csv": "csv",
"application/fits": None,
"application/x-votable+xml;serialization=TABLEDATA;version=1.5": "vodml"}
_SDM1_IRREGULARS = {
"dataset.datamodel": "Spectrum.DataModel",
"dataset.type": "Spectrum.Type",
"dataset.length": "Spectrum.Length",
"dataset.timesi": "Spectrum.TimeSI",
"dataset.spectralsi": "Spectrum.SpectralSI",
"dataset.fluxsi": "Spectrum.FluxSI",
"access.format": None,
"access.reference": None,
"access.size": None,
"astrocoords.position2d.value2":
"Spectrum.Char.SpatialAxis.Coverage.Location.Value",
"target.pos.spoint": "Spectrum.Target.pos",
}
_SDM_TO_SED_UTYPES = {
"spec:Spectrum.Data.SpectralAxis.Value":
"sed:Segment.Points.SpectralCoord.Value",
"spec:Data.SpectralAxis.Value":
"sed:Segment.Points.SpectralCoord.Value",
"spec:Spectrum.Data.FluxAxis.Value": "sed:Segment.Points.Flux.Value",
"spec:Data.FluxAxis.Value": "sed:Segment.Points.Flux.Value",
}
################### Making SDM compliant tables (from SSA rows and
################### data descriptors making spectral data)
[docs]def makeSDMDataForPUBDID(pubDID, ssaTD, spectrumData,
sdmVersion=base.getConfig("ivoa", "sdmVersion")):
"""returns a rsc.Data instance containing an SDM compliant spectrum
for pubDID from ssaTable.
ssaTD is the definition of a table containing the SSA metadata,
spectrumData is a data element making a primary table containing
the spectrum data from an SSA row (typically, this is going to be
the tablesource property of an SSA service).
"""
matchingRows = ssaTD.doSimpleQuery(fragments="ssa_pubdid=%(pubDID)s",
params={"pubDID": pubDID})
if not matchingRows:
raise svcs.UnknownURI("No spectrum with pubdid %s known here"%
pubDID)
return makeSDMDataForSSARow(
matchingRows[0],
spectrumData,
sdmVersion=sdmVersion)
################## Special FITS hacks for SDM serialization
def _add_target_pos_cards(header, par):
"""_SDM_HEADER_MAPPING for target.pos.
"""
header.set("RA_TARG", par.value.x/utils.DEG)
header.set("DEC_TARG", par.value.y/utils.DEG)
def _add_location_cards(header, par):
"""_SDM_HEADER_MAPPING for target.pos.
"""
header.set("RA", par.value[0])
header.set("DEC", par.value[1])
[docs]def getColIndForUtype(header, colUtype):
"""returns the fits field index that has colUtype as its utype within
header.
If no such field exists, this raises a KeyError
"""
nCols = header.get("TFIELDS", 0)
for colInd in range(1, nCols+1):
key = "TUTYP%d"%colInd
if header.get(key, None)==colUtype:
return colInd
else:
raise KeyError(colUtype)
def _make_limits_func(colUtype, keyBase):
"""returns a _SDM_HEADER_MAPPING for declaring TDMIN/MAXn and friends.
colUtype determines the n here.
"""
def func(header, par):
try:
key = keyBase+str(getColIndForUtype(header, colUtype))
header.set(key, par.value, comment="[%s]"%par.unit)
except KeyError:
# no field for utype
pass
return func
# A mapping from utypes to the corresponding FITS keywords
# There are some more complex cases, for which a function is a value
# here; the function is called with the FITS header and the parameter
# in question.
_SDM_HEADER_MAPPING = {
"datamodel": "VOCLASS",
"length": "DATALEN",
"type": "VOSEGT",
"coordsys.id": "VOCSID",
"coordsys.spaceframe.name": "RADECSYS",
"coordsys.spaceframe.equinox": "EQUINOX",
"coordsys.spaceframe.ucd": "SKY_UCD",
"coordsys.spaceframe.refpos": "SKY_REF",
"coordsys.timeframe.name": "TIMESYS",
"coordsys.timeframe.ucd": None,
"coordsys.timeframe.zero": "MJDREF",
"coordsys.timeframe.refpos": None,
"coordsys.spectralframe.refpos": "SPECSYS",
"coordsys.spectralframe.redshift": "REST_Z",
"coordsys.spectralframe.name": "SPECNAME",
"coordsys.redshiftframe.name": "ZNAME",
"coordsys.redshiftframe.refpos": "SPECSYSZ",
"curation.publisher": "VOPUB",
"curation.reference": "VOREF",
"curation.publisherid": "VOPUBID",
"curation.version": "VOVER",
"curation.contactname": "CONTACT",
"curation.contactemail": "EMAIL",
"curation.rights": "VORIGHTS",
"curation.date": "VODATE",
"curation.publisherdid": "DS_IDPUB",
"target.name": "OBJECT",
"target.description": "OBJDESC",
"target.class": "SRCCLASS",
"target.spectralclass": "SPECTYPE",
"target.redshift": "REDSHIFT",
"target.varampl": "TARGVAR",
"dataid.title": "TITLE",
"dataid.creator": "AUTHOR",
"dataid.datasetid": "DS_IDENT",
"dataid.creatordid": "CR_IDENT",
"dataid.date": "DATE",
"dataid.version": "VERSION",
"dataid.instrument": "INSTRUME",
"dataid.creationtype": "CRETYPE",
"dataid.logo": "VOLOGO",
# collection will need work when we properly implement it
"dataid.collection": "COLLECT1",
"dataid.contributor": "CONTRIB1",
"dataid.datasource": "DSSOURCE",
"dataid.bandpass": "SPECBAND",
"derived.snr": "DER_SNR",
"derived.redshift.value": "DER_Z",
"derived.redshift.staterror": "DER_ZERR",
"derived.redshift.confidence": "DER_ZCNF",
"derived.varampl": "DER_VAR",
"timesi": "TIMESDIM",
"spectralsi": "SPECSDIM",
"fluxsi": "FLUXSDIM",
"char.fluxaxis.name": None,
"char.fluxaxis.unit": None,
"char.fluxaxis.ucd": None,
"char.spectralaxis.name": None,
"char.spectralaxis.unit": None,
"char.spectralaxis.ucd": None,
"char.timeaxis.name": None,
"char.timeaxis.ucd": None,
"char.spatialaxis.name": None,
"char.spatialaxis.unit": None,
"char.fluxaxis.accuracy.staterror": "STAT_ERR",
"char.fluxaxis.accuracy.syserror": "SYS_ERR",
"char.timeaxis.accuracy.staterror": "TIME_ERR",
"char.timeaxis.accuracy.syserror": "TIME_SYE",
"char.timeaxis.resolution": "TIME_RES",
"char.fluxaxis.calibration": "FLUX_CAL",
"char.spectralaxis.calibration": "SPEC_CAL",
"char.spectralaxis.coverage.location.value": "SPEC_VAL",
"char.spectralaxis.coverage.bounds.extent": "SPEC_BW",
"char.spectralaxis.coverage.bounds.start":
_make_limits_func("spec:spectrum.data.spectralaxis.value", "TDMIN"),
"char.spectralaxis.coverage.bounds.stop":
_make_limits_func("spec:spectrum.data.spectralaxis.value", "TDMAX"),
"char.spectralaxis.samplingprecision.": None,
"samplingprecisionrefval.fillfactor": "SPEC_FIL",
"char.spectralaxis.samplingprecision.SampleExtent": "SPEC BIN",
"char.spectralaxis.accuracy.binsize": "SPEC_BIN",
"char.spectralaxis.accuracy.staterror": "SPEC_ERR",
"char.spectralaxis.accuracy.syserror": "SPEC_SYE",
"char.spectralaxis.resolution": "SPEC_RES",
"char.spectralaxis.respower": "SPEC_RP",
"char.spectralaxis.coverage.support.extent": "SPECWID",
"char.timeaxis.unit": "TIMEUNIT",
"char.timeaxis.accuracy.binsize": "TIMEDEL",
"char.timeaxis.calibration": "TIME_CAL",
"char.timeaxis.coverage.location.value": "TMID",
"char.timeaxis.coverage.bounds.extent": "TELAPSE",
"char.timeaxis.coverage.bounds.start": "TSTART",
"char.timeaxis.coverage.bounds.stop": "TSTOP",
"char.timeaxis.coverage.support.extent": "EXPOSURE",
"char.timeaxis.samplingprecision.samplingprecisionrefval.fillfactor": "DTCOR",
"char.timeaxis.samplingprecision.sampleextent": "TIMEDEL",
"char.spatialaxis.ucd": "SKY_UCD",
"char.spatialaxis.accuracy.staterr": "SKY_ERR",
"char.spatialaxis.accuracy.syserror": "SKY_SYE",
"char.spatialaxis.calibration": "SKY_CAL",
"char.spatialaxis.resolution": "SKY_RES",
"char.spatialaxis.coverage.bounds.extent": "APERTURE",
"char.spatialaxis.coverage.support.area": "REGION",
"char.spatialaxis.coverage.support.extent": "AREA",
"char.spatialaxis.samplingprecision.samplingprecisionrefval.fillfactor":
"SKY_FILL",
# special handling through functions
"target.pos": _add_target_pos_cards,
"char.spatialaxis.coverage.location.value": _add_location_cards,
}
[docs]def makeSDMFITS(sdmData):
"""returns sdmData in an SDM-compliant FITS.
This only works for SDM version 1. Behaviour with version 2 SDM
data is undefined.
"""
sdmData.getPrimaryTable().IgnoreTableParams = None
hdus = fitstable.makeFITSTable(sdmData)
sdmHdr = hdus[1].header
makeBasicSDMHeader(sdmData, sdmHdr)
srcName = fitstable.writeFITSTableFile(hdus)
with open(srcName, "rb") as f:
data = f.read()
os.unlink(srcName)
return data
################## Serializing SDM compliant tables
################## Manipulation of SDM compliant tables
# The idea here is that you can push in a table, the function does some
# magic, and it returns that table. The getData implementation (see ssap.py)
# and some SODA data functions (//soda)
# use these functions to provide some spectrum transformations. We
# may want to provide some plugin system so people can add their own
# transformations, but let's first see someone request that.
[docs]def getFluxColumn(sdmTable):
"""returns the column containing the flux in sdmTable.
"""
return sdmTable.tableDef.getByUtypes("spec:Spectrum.Data.FluxAxis.Value")
[docs]def getSpectralColumn(sdmTable):
"""returns the column containing the spectral coordinate in sdmTable.
"""
return sdmTable.tableDef.getByUtypes(
"spec:Spectrum.Data.SpectralAxis.Value")
[docs]def mangle_cutout(sdmTable, low, high):
"""returns only those rows from sdmTable for which the spectral coordinate
is between low and high.
Both low and high must be given. If you actually want half-open intervals,
do it in interface code (low=-1 and high=1e308 should do fine).
"""
spectralColumn = getSpectralColumn(sdmTable)
# we may swap limits later; let's memorise whether we have an empty
# interval.
returnEmpty = low>high
spectralUnit = spectralColumn.unit
# convert low and high from meters to the unit on the
# spectrum's spectral axis
converter = base.getSpecConverter("m", spectralUnit)
low, high = converter(low), converter(high)
# swap limits in case of (freq, energy) -> wavelength
low, high = min(low, high), max(low, high)
# Whoa! we should have an API that allows replacing table rows safely
# (this stuff will blow up when we have indices):
spectralName = spectralColumn.name
if returnEmpty:
sdmTable.rows=[]
else:
sdmTable.rows=[
row for row in sdmTable.rows if low<=row[spectralName]<=high]
specVals = [r[spectralName] for r in sdmTable.rows]
if specVals:
invConverter = base.getSpecConverter(spectralUnit, "m")
lim1, lim2 = invConverter(min(specVals)), invConverter(max(specVals))
specstart, specend = min(lim1, lim2), max(lim1, lim2)
sdmTable.setParam("ssa_specext", specend-specstart)
sdmTable.setParam("ssa_specstart", specstart)
sdmTable.setParam("ssa_specend", specend)
sdmTable.setParam("ssa_specmid", (specstart+specend)/2)
return sdmTable
[docs]def mangle_fluxcalib(sdmTable, newCalib):
"""returns sdmTable with a new calibration.
Currently, we can only normalize the spectrum to the maximum value.
"""
newCalib = newCalib.lower()
if newCalib==sdmTable.getParam("ssa_fluxcalib").lower():
return sdmTable
fluxName = getFluxColumn(sdmTable).name
try:
# TODO: parameterize this
errorName = sdmTable.tableDef.getColumnByUCD(
"stat.error;phot.flux;em.opt").name
except base.StructureError:
# no (known) error column
errorName = None
if newCalib=="relative":
# whoa! we're changing this in place; I guess that should be
# legalized for tables in general.
normalizer = float(max(row[fluxName] for row in sdmTable.rows))
for row in sdmTable.rows:
row[fluxName] = row[fluxName]/normalizer
if errorName:
for row in sdmTable.rows:
row[errorName] = row[errorName]/normalizer
sdmTable.setParam("ssa_fluxcalib", "RELATIVE")
sdmTable.tableDef = sdmTable.tableDef.copy(sdmTable.tableDef.parent)
sdmTable.tableDef.getColumnByName("flux").unit = ""
return sdmTable
raise base.ValidationError("Do not know how to turn a %s spectrum"
" into a %s one."%(sdmTable.getParam("ssa_fluxcalib"), newCalib),
"FLUXCALIB")