def setupResults(sql):
# rather than use the sql2Q method:
#q = sqlparse.sql2Q(sql)
start_time = time.time()
# ... we parse the query into its restrictables and logic:
if not sql.where:
return {}
logic, rs, count = sqlparse.splitWhere(sql.where)
# and replace any restrictions on ChemicalName or StoichiometricFormula
# with the equivalent on MoleculeInchiKey. Also convert wavelength
# (in Angstroms) to wavenumber:
for i in rs:
r, op, foo = rs[i][0], rs[i][1], rs[i][2:]
if r == 'MoleculeChemicalName':
rs[i] = ChemicalName2MoleculeInchiKey(op, foo)
if r == 'MoleculeStoichiometricFormula':
rs[i] = StoichiometricFormula2MoleculeInchiKey(op, foo)
if r == 'RadTransWavelength':
rs[i] = Wavelength2Wavenumber(op, foo)
if r == 'Pressure':
rs[i] = Pascals2Torr(op, foo)
# now rebuild the query as a dictionary of QTypes ...
qdict = {}
for i, r in rs.items():
q = sqlparse.restriction2Q(r)
qdict[i] = q
# ... and merge them to a single query object
q = sqlparse.mergeQwithLogic(qdict, logic)
if 'radiativecrosssections' in sql.requestables:
return setupXsecResults(q)
transitions = Trans.objects.filter(q).order_by('nu')
ntrans = transitions.count()
if settings.TRANSLIM is not None and ntrans > settings.TRANSLIM:
# we need to filter transitions again later, so can't take a slice
#transitions = transitions[:settings.TRANSLIM]
# so do this:
numax = transitions[settings.TRANSLIM].nu
transitions = Trans.objects.filter(q, Q(nu__lte=numax))
percentage = '%.1f' % (float(settings.TRANSLIM)/ntrans * 100)
ntrans = transitions.count()
print 'Results truncated to %s %%' % percentage
else:
percentage = '100'
print 'Results not truncated'
print 'TRANSLIM is', settings.TRANSLIM
print 'ntrans =',ntrans
ts = time.time()
ref_ids = attach_prms(transitions)
te = time.time()
print 'time to attach parameters = %.1f secs' % (te-ts)
ts = time.time()
sources = get_sources(ref_ids)
te = time.time()
print 'time to get sources = %.1f secs' % (te-ts)
# extract the state quantum numbers in a form that generators.py can use:
ts = time.time()
species, nspecies, nstates = get_species(transitions)
te = time.time()
print 'time to get species = %.1f secs' % (te-ts)
LOG('%s transitions retrieved from HITRAN database' % ntrans)
LOG('%s states retrieved from HITRAN database' % nstates)
LOG('%s species retrieved from HITRAN database' % nspecies)
nsources = 0
if sources is not None:
# sources is a Python list, not a queryset, so we can't use count()
nsources = len(sources)
print 'nsources =', nsources
print 'nspecies =', nspecies
print 'nstates =', nstates
print 'ntrans =', ntrans
headerinfo = {
'Truncated': '%s %%' % percentage,
'count-sources': nsources,
'count-species': nspecies,
'count-molecules': nspecies,
'count-states': nstates,
'count-radiative': ntrans
}
methods = [Method('MEXP', 'experiment', 'experiment'),
Method('MTHEORY', 'theory', 'theory')]
end_time = time.time()
print 'timed at %.1f secs' % (end_time - start_time)
# return the dictionary as described above
return {'HeaderInfo': headerinfo,
'Methods': methods,
'RadTrans': transitions,
'Sources': sources,
'Molecules': species,
'Environments': HITRANenvs,
'Functions': HITRANfuncs}
except Exception, e:
return {}
# ... we parse the query into its restrictables and logic:
if not sql.where:
return {}
logic, rs, count = splitWhere(sql.where)
# Convert MoleculeNormalModeHarmonicFrequency
# from MHz to cm-1:
for i in rs:
r, op, foo = rs[i][0], rs[i][1], rs[i][2:]
if r == "MoleculeNormalModeHarmonicFrequency":
rs[i] = MoleculeNormalModeHarmonicFrequency_MHz2cm(op, foo)
# now rebuild the query as a dictionary of QTypes ...
qdict = {}
for i, r in rs.items():
q = restriction2Q(r)
qdict[i] = q
# ... and merge them to a single query object
q = mergeQwithLogic(qdict, logic)
# We build a queryset of database matches on the Transision model
# since through this model (in our example) we are be able to
# reach all other models. Note that a queryset is actually not yet
# hitting the database, making it very efficient.
molecularspecies = models.MolecularSpecies.objects.filter(q).distinct()
# count the number of matches, make a simple trunkation if there are
# too many (record the coverage in the returned header)
nmolecularspecies = molecularspecies.count()
if limit < nmolecularspecies:
nmolecularspecies = molecularspecies[:limit]
percentage = "%.1f" % (float(limit) / nmolecularspecies * 100)
def setupResults(sql):
# rather than use the sql2Q method:
#q = sqlparse.sql2Q(sql)
start_time = time.time()
# ... we parse the query into its restrictables and logic:
if not sql.where:
return {}
logic, rs, count = sqlparse.splitWhere(sql.where)
# and replace any restrictions on ChemicalName or StoichiometricFormula
# with the equivalent on MoleculeInchiKey. Also convert wavelength
# (in Angstroms) to wavenumber:
for i in rs:
r, op, foo = rs[i][0], rs[i][1], rs[i][2:]
if r == 'MoleculeChemicalName':
rs[i] = ChemicalName2MoleculeInchiKey(op, foo)
if r == 'MoleculeStoichiometricFormula':
rs[i] = StoichiometricFormula2MoleculeInchiKey(op, foo)
if r == 'RadTransWavelength':
rs[i] = Wavelength2Wavenumber(op, foo)
if r == 'Pressure':
rs[i] = Pascals2Torr(op, foo)
# now rebuild the query as a dictionary of QTypes ...
qdict = {}
for i, r in rs.items():
q = sqlparse.restriction2Q(r)
qdict[i] = q
# ... and merge them to a single query object
q = sqlparse.mergeQwithLogic(qdict, logic)
if 'radiativecrosssections' in sql.requestables:
return setupXsecResults(q)
transitions = Trans.objects.filter(q).order_by('nu')
ntrans = transitions.count()
if settings.TRANSLIM is not None and ntrans > settings.TRANSLIM:
# we need to filter transitions again later, so can't take a slice
#transitions = transitions[:settings.TRANSLIM]
# so do this:
numax = transitions[settings.TRANSLIM].nu
transitions = Trans.objects.filter(q, Q(nu__lte=numax))
percentage = '%.1f' % (float(settings.TRANSLIM) / ntrans * 100)
ntrans = transitions.count()
print 'Results truncated to %s %%' % percentage
else:
percentage = '100'
print 'Results not truncated'
print 'TRANSLIM is', settings.TRANSLIM
print 'ntrans =', ntrans
ts = time.time()
ref_ids = attach_prms(transitions)
te = time.time()
print 'time to attach parameters = %.1f secs' % (te - ts)
ts = time.time()
sources = get_sources(ref_ids)
te = time.time()
print 'time to get sources = %.1f secs' % (te - ts)
# extract the state quantum numbers in a form that generators.py can use:
ts = time.time()
species, nspecies, nstates = get_species(transitions)
te = time.time()
print 'time to get species = %.1f secs' % (te - ts)
LOG('%s transitions retrieved from HITRAN database' % ntrans)
LOG('%s states retrieved from HITRAN database' % nstates)
LOG('%s species retrieved from HITRAN database' % nspecies)
nsources = 0
if sources is not None:
# sources is a Python list, not a queryset, so we can't use count()
nsources = len(sources)
print 'nsources =', nsources
print 'nspecies =', nspecies
print 'nstates =', nstates
print 'ntrans =', ntrans
headerinfo = {
'Truncated': '%s %%' % percentage,
'count-sources': nsources,
'count-species': nspecies,
'count-molecules': nspecies,
'count-states': nstates,
'count-radiative': ntrans
}
methods = [
Method('MEXP', 'experiment', 'experiment'),
Method('MTHEORY', 'theory', 'theory')
]
end_time = time.time()
print 'timed at %.1f secs' % (end_time - start_time)
# return the dictionary as described above
if (ntrans > 0 or nstates > 0 or nspecies > 0):
return {
'HeaderInfo': headerinfo,
'Methods': methods,
'RadTrans': transitions,
'Sources': sources,
'Molecules': species,
'Environments': HITRANenvs,
'Functions': HITRANfuncs
}
# return an empty dictionary to trigger a 204 if there's no data
return {}
except Exception, e:
return {}
# ... we parse the query into its restrictables and logic:
if not sql.where:
return {}
logic, rs, count = splitWhere(sql.where)
#Convert MoleculeNormalModeHarmonicFrequency
# from MHz to cm-1:
for i in rs:
r, op, foo = rs[i][0], rs[i][1], rs[i][2:]
if r == 'MoleculeNormalModeHarmonicFrequency':
rs[i] = MoleculeNormalModeHarmonicFrequency_MHz2cm(op, foo)
# now rebuild the query as a dictionary of QTypes ...
qdict = {}
for i, r in rs.items():
q = restriction2Q(r)
qdict[i] = q
# ... and merge them to a single query object
q = mergeQwithLogic(qdict, logic)
# We build a queryset of database matches on the Transision model
# since through this model (in our example) we are be able to
# reach all other models. Note that a queryset is actually not yet
# hitting the database, making it very efficient.
molecularspecies = models.MolecularSpecies.objects.filter(q).distinct()
# count the number of matches, make a simple trunkation if there are
# too many (record the coverage in the returned header)
nmolecularspecies = molecularspecies.count()
if limit < nmolecularspecies:
nmolecularspecies = molecularspecies[:limit]
percentage = '%.1f' % (float(limit) / nmolecularspecies * 100)
