def main():
import csv
from neurondm.core import auth
with open(auth.get_path('resources') / '26451489 table 1.csv', 'rt') as f:
rows = [list(r) for r in zip(*csv.reader(f))]
config = Config('markram-2015',
source_file=relative_path(__file__, no_wd_value=__file__))
table1(rows)
return config,
def main():
a = APIN()
config = Config('bolser-lewis', source_file=relative_path(__file__))
bags = list(a.bags)
for label, bag in bags:
BolserLewisNeuron(*bag, label=label, override=True)
Neuron.write()
Neuron.write_python()
return config,
def make_config(self):
# have to call Config here because transgenic lines doesn't exist
self.config = Config(name=self.name,
imports=[f'NIFRAW:{self.branch}/ttl/generated/allen-transgenic-lines.ttl'],
prefixes=self.prefixes,
branch=self.branch,
sources=tuple(), # TODO insert the link to the query...
source_file=relative_path(
__file__,
no_wd_value=__file__))
def main():
from pyontutils.utils import relative_path
config = Config('keast-2020',
source_file=relative_path(__file__, no_wd_value=__file__))
with Keast2020:
needs_keast_namespace()
config.write()
config.write_python()
return config
def build_neurons(self):
# have to call Config here because transgenic lines doesn't exist
self.config = Config(
name=self.name,
imports=[
f'NIFRAW:{self.branch}/ttl/generated/allen-transgenic-lines.ttl'
],
prefixes=self.prefixes,
branch=self.branch,
sources=tuple(), # TODO insert the link to the query...
source_file=relative_path(__file__))
for cell_line in self.neuron_data:
NeuronACT(*self.build_phenotypes(cell_line))
print(sorted(self.tag_names))
NeuronACT.write()
NeuronACT.write_python()
def main():
a = APIN()
config = Config('bolser-lewis',
source_file=relative_path(__file__, no_wd_value=__file__))
bags = list(a.bags)
for id, label, bag in bags:
BolserLewisNeuron(*bag, label=label, id_=id, override=True)
config.write()
labels = (
rdfs.label,
#ilxtr.genLabel, ilxtr.localLabel, ilxtr.simpleLabel,
#ilxtr.simpleLocalLabel, skos.prefLabel
)
to_remove = [t for t in config._written_graph
if t[1] in labels]
[config._written_graph.remove(t) for t in to_remove]
config._written_graph.write()
config.write_python()
return config,
def main():
from pyontutils.utils import relative_path
config = Config('keast-2020',
source_file=relative_path(__file__, no_wd_value=__file__))
with Keast2020:
needs_keast_namespace(config)
config.write()
labels = (
rdfs.label,
#ilxtr.genLabel, ilxtr.localLabel, ilxtr.simpleLabel,
#ilxtr.simpleLocalLabel, skos.prefLabel
)
to_remove = [t for t in config._written_graph
if t[1] in labels and '/neuron-type-keast-' in t[0]]
[config._written_graph.remove(t) for t in to_remove]
config._written_graph.write()
config.write_python()
return config,
def main():
branch=auth.get('neurons-branch')
remote = OntId('NIFTTL:') if branch == 'master' else OntId(f'NIFRAW:{branch}/')
ont_config = ontneurons(remote)
ont_neurons = ont_config.neurons()
bn_config = Config('basic-neurons',
# FIXME this should probably be pulled in automatically
# from the import statements, and it doesn't work even as is
# also a chicken and an egg problem here
imports=[remote.iri + 'ttl/generated/swanson.ttl'])
#RDFL = oq.plugin.get('rdflib') # FIXME ick
#rdfl = RDFL(bn_config.core_graph, OntId)
#OntTerm.query.ladd(rdfl) # FIXME ick
bn_config.load_existing()
bn_neurons = bn_config.neurons()
#OntTerm.query._services = OntTerm.query._services[:-1] # FIXME ick
ndl_config = Config('neuron_data_lifted')
ndl_config.load_existing() # FIXME this is extremely slow
ndl_neurons = sorted(ndl_config.neurons())
resources = auth.get_path('resources')
cutcsv = resources / 'cut-development.csv'
with open(cutcsv.as_posix(), 'rt') as f:
rows = [l for l in csv.reader(f)]
bc = byCol(rows)
(_, *labels), *_ = zip(*bc)
labels_set0 = set(labels)
ns = []
skipped = []
bamscok = (NIFSTD.BAMSC1125,)
for n in (ont_neurons + ndl_neurons):
if n.id_ and 'BAMSC' in n.id_:
if n.id_ not in bamscok:
skipped.append(n)
continue
l = str(n.origLabel)
if l is not None:
for replace, match in rename_rules.items(): # HEH
l = l.replace(match, replace)
if l in labels:
n._origLabel = l
ns.append(n)
ns = sorted(ns)
sns = set(n.origLabel for n in ns)
labels_set1 = labels_set0 - sns
agen = [c.label for c in bc if c.autogenerated]
sagen = set(agen)
added = [c.label for c in bc if c.added]
sadded = set(added)
ans = []
sans = set()
missed = set()
_bl = [] # XXX NOTE THE CONTINUE BELOW
for n in bn_neurons:
continue # we actually get all of these with uberon, will map between them later
# can't use capitalize here because there are proper names that stay uppercase
l = n.label.replace('(swannt) ',
'').replace('Intrinsic',
'intrinsic').replace('Projection',
'projection')
for replace, match in rename_rules.items(): # HEH
l = l.replace(match, replace)
if l in agen:
n._origLabel = l
ans.append(n)
sans.add(l)
else:
missed.add(l)
_bl.append(l)
agen_missing = sagen - sans
labels_set2 = labels_set1 - sans
nlx_labels = [c.label for c in bc if c.neurolex]
snlx_labels = set(nlx_labels)
class SourceCUT(resSource):
sourceFile = 'nifstd/resources/cut-development.csv' # FIXME relative to git workingdir...
source_original = True
sources = SourceCUT(),
swanr = rdflib.Namespace(interlex_namespace('swanson/uris/readable/'))
SWAN = interlex_namespace('swanson/uris/neuroanatomical-terminology/terms/')
SWAA = interlex_namespace('swanson/uris/neuroanatomical-terminology/appendix/')
config = Config('cut-development-raw', sources=sources, source_file=relative_path(__file__),
prefixes={'swanr': swanr,
'SWAN': SWAN,
'SWAA': SWAA,})
ins = [None if OntId(n.id_).prefix == 'TEMP' else n.id_ for n in ns]
ians = [None] * len(ans)
with NeuronCUT(CUT.Mammalia):
mamns = [NeuronCUT(*zap(n.pes), id_=i, label=n._origLabel, override=bool(i)).adopt_meta(n)
for i, n in zip(ins + ians, ns + ans)]
smatch, rem = get_smatch(labels_set2)
labels_set3 = labels_set2 - smatch
added_unmapped = sadded & labels_set3
# TODO preserve the names from neuronlex on import ...
Neuron.write()
Neuron.write_python()
raw_neurons = config.neurons()
# do this before creating the new config
# even though we are in theory tripling number of neurons in the current config graph
# it won't show up in the next config (and this is why we need to reengineer)
raw_neurons_ind_undep = [n.asUndeprecated().asIndicator() for n in raw_neurons]
config = Config('cut-development', sources=sources, source_file=relative_path(__file__),
prefixes={'swanr': swanr,
'SWAN': SWAN,
'SWAA': SWAA,})
# FIXME the call to asUndprecated currenlty triggers addition
# to the current config and output graph as a side effect (ick!)
ids_updated_neurons = [n.asUndeprecated() for n in raw_neurons]
assert len(ids_updated_neurons) == len(raw_neurons)
Neuron.write()
Neuron.write_python()
progress = (len(labels_set0), len(sns), len(sans), len(smatch),
len(labels_set1), len(labels_set2), len(labels_set3))
prog_report = ('\nProgress:\n'
f'total: {progress[0]}\n'
f'from nlx: {progress[1]}\n'
f'from basic: {progress[2]}\n'
f'from match: {progress[3]}\n'
f'TODO after nlx: {progress[4]}\n'
f'TODO after basic: {progress[5]}\n'
f'TODO after match: {progress[6]}\n')
print(prog_report)
assert progress[0] == progress[1] + progress[4], 'neurolex does not add up'
assert progress[4] == progress[2] + progress[5], 'basic does not add up'
lnlx = set(n.lower() for n in snlx_labels)
sos = set(n.origLabel.lower() if n.origLabel else None for n in ndl_neurons) # FIXME load origLabel
nlx_review = lnlx - sos
nlx_missing = sorted(nlx_review)
print(f'\nNeuroLex listed as source but no mapping (n = {len(nlx_review)}):')
_ = [print(l) for l in nlx_missing]
partial = {k:v for k, v in rem.items() if v and v not in terminals}
print(f'\nPartially mapped (n = {len(partial)}):')
if partial:
mk = max((len(k) for k in partial.keys())) + 2
for k, v in sorted(partial.items()):
print(f'{k:<{mk}} {v!r}')
#print(f'{k!r:<{mk}}{v!r}')
#pprint(partial, width=200)
unmapped = sorted(labels_set3)
print(f'\nUnmapped (n = {len(labels_set3)}):')
_ = [print(l) for l in unmapped]
no_location = [n for n in Neuron.neurons()
if noneMembers((ilxtr.hasSomaLocatedIn, ilxtr.hasSomaLocatedInLayer), *n.unique_predicates)]
if __name__ == '__main__':
review_rows = export_for_review(config, unmapped, partial, nlx_missing)
breakpoint()
return config, unmapped, partial, nlx_missing
def main():
# load in our existing graph
# note: while it would be nice to allow specification of phenotypes to be decoupled
# from insertion into the graph... maybe we could enable this, but it definitely seems
# to break a number of nice features... and we would need the phenotype graph anyway
Config('temporary-graph')
EXISTING_GRAPH = graphBase.in_graph
#EXISTING_GRAPH = rdflib.Graph()
#graphBase.in_graph = EXISTING_GRAPH
#graphBase.core_graph = EXISTING_GRAPH
local_prefix = auth.get_path('ontology-local-repo') / 'ttl'
sources = (f'{local_prefix}/NIF-Neuron-Defined.ttl',
f'{local_prefix}/NIF-Neuron.ttl',
f'{local_prefix}/NIF-Neuron-Phenotype.ttl',
f'{local_prefix}/phenotype-core.ttl',
f'{local_prefix}/phenotypes.ttl',
f'{local_prefix}/hbp-special.ttl')
for file in sources:
EXISTING_GRAPH.parse(file, format='turtle')
#EXISTING_GRAPH.namespace_manager.bind('PR', makePrefixes('PR')['PR'])
#graphBase.core_graph = EXISTING_GRAPH
#graphBase.out_graph = rdflib.Graph()
graphBase.__import_name__ = 'neurondm.lang'
proot = graphBase.core_graph.qname(PHENO_ROOT)
mroot = graphBase.core_graph.qname(MOD_ROOT)
graphBase._predicates, _psupers = getPhenotypePredicates(
EXISTING_GRAPH, proot, mroot)
g = makeGraph('merged',
prefixes={k: str(v)
for k, v in EXISTING_GRAPH.namespaces()},
graph=EXISTING_GRAPH)
reg_neurons = list(g.g.subjects(rdfs.subClassOf, _NEURON_CLASS))
tc_neurons = [
_ for (_, ) in
g.g.query('SELECT DISTINCT ?match WHERE {?match rdfs:subClassOf+ %s}' %
g.g.qname(_NEURON_CLASS))
]
def_neurons = g.get_equiv_inter(_NEURON_CLASS)
nodef = sorted(set(tc_neurons) - set(def_neurons))
og1 = MeasuredNeuron.out_graph = rdflib.Graph(
) # there is only 1 out_graph at a time, load and switch
mns = [MeasuredNeuron(id_=n) for n in nodef]
mnsp = [n for n in mns if n.pes]
graphBase.out_graph = rdflib.Graph(
) # XXX NEVER DO THIS IT IS EVIL ZALGO WILL EAT YOUR FACE
graphBase.ng.g = graphBase.out_graph
# and he did, had to swtich to graphBase for exactly this reason >_<
dns = [Neuron(id_=n) for n in sorted(def_neurons)]
#dns += [Neuron(*m.pes) if m.pes else m.id_ for m in mns]
dns += [Neuron(*m.pes) for m in mns if m.pes]
# reset everything for export
config = Config('phenotype-direct', source_file=relative_path(__file__))
#Neuron.out_graph = graphBase.out_graph # each subclass of graphBase has a distinct out graph IF it was set manually
#Neuron.out_graph = rdflib.Graph()
#ng = makeGraph('', prefixes={}, graph=Neuron.out_graph)
#ng.filename = Neuron.ng.filename
Neuron.mro()[1].existing_pes = {
} # wow, new adventures in evil python patterns mro()[1]
dns = [Neuron(*d.pes) for d in set(dns)
] # TODO remove the set and use this to test existing bags?
#from neurons.lang import WRITEPYTHON
#WRITEPYTHON(sorted(dns))
#ng.add_ont(TEMP['defined-neurons'], 'Defined Neurons', 'NIFDEFNEU',
#'VERY EXPERIMENTAL', '0.0.0.1a')
#ng.add_trip(TEMP['defined-neurons'], owl.imports, rdflib.URIRef('file:///home/tom/git/NIF-Ontology/ttl/phenotype-core.ttl'))
#ng.add_trip(TEMP['defined-neurons'], owl.imports, rdflib.URIRef('file:///home/tom/git/NIF-Ontology/ttl/phenotypes.ttl'))
#ng.write()
ontinfo = (
(Neuron.ng.ontid, rdf.type, owl.Ontology),
(Neuron.ng.ontid, rdfs.label,
rdflib.Literal('phenotype direct neurons')),
(Neuron.ng.ontid, rdfs.comment,
rdflib.Literal('Neurons derived directly from phenotype definitions')
),
)
[Neuron.out_graph.add(t) for t in ontinfo]
Neuron.write()
Neuron.write_python()
bads = [
n for n in Neuron.ng.g.subjects(rdf.type, owl.Class)
if len(list(Neuron.ng.g.predicate_objects(n))) == 1
]
if __name__ == '__main__':
breakpoint()
return config
#!/usr/bin/env python3
import rdflib
import ontquery
#from pyontutils.core import OntId, OntTerm
from pyontutils.utils import relative_path
from pyontutils.namespaces import makePrefixes, makeNamespaces
from pyontutils.namespaces import NIFRID, ilxtr, hasRole, definition
from pyontutils.closed_namespaces import rdf, rdfs, owl
from neurondm.lang import *
from neurondm import *
from neurondm.phenotype_namespaces import *
extra = False # construct extra classes
config = Config('huang-2017', source_file=relative_path(__file__))
OntTerm.query.add(ontquery.plugin.get('rdflib')(Neuron.core_graph, OntId))
class NeuronHuang2017(NeuronEBM):
owlClass = ilxtr.NeuronHuang2017
shortname = 'Huang2017'
Neuron, Neuron_ = NeuronHuang2017, Neuron
dex = 'ilxtr:hasDriverExpressionPhenotype'
induced_exp = 'ilxtr:hasDriverExpressionInducedPhenotype'
const_exp = 'ilxtr:hasDriverExpressionConstitutivePhenotype'
class Genes(LocalNameManager):
# FIXME ilxtr.hasRNAExpressionPhenotype
import rdflib
import ontquery
#from pyontutils.core import OntId, OntTerm
from pyontutils.utils import relative_path
from pyontutils.namespaces import makePrefixes, makeNamespaces
from pyontutils.namespaces import NIFRID, ilxtr, hasRole, definition
from pyontutils.closed_namespaces import rdf, rdfs, owl
from neurondm.lang import *
from neurondm import *
from neurondm.phenotype_namespaces import *
from IPython import embed
config = Config(
'huang-2017',
imports=['NIFRAW:neurons/ttl/generated/neurons/phenotype-direct.ttl'],
source_file=relative_path(__file__))
OntTerm.query.add(ontquery.plugin.get('rdflib')(Neuron.core_graph, OntId))
class NeuronHuang2017(NeuronEBM):
owlClass = ilxtr.NeuronHuang2017
shortname = 'Huang2017'
Neuron, Neuron_ = NeuronHuang2017, Neuron
dex = 'ilxtr:hasDriverExpressionPhenotype'
class Genes(LocalNameManager):
# FIXME ilxtr.hasRNAExpressionPhenotype
#!/usr/bin/env python3
import rdflib
import ontquery
#from pyontutils.core import OntId, OntTerm
from pyontutils.utils import relative_path
from pyontutils.namespaces import makePrefixes, makeNamespaces
from pyontutils.namespaces import NIFRID, ilxtr, hasRole, definition
from pyontutils.closed_namespaces import rdf, rdfs, owl
from neurondm.lang import *
from neurondm import *
from neurondm.phenotype_namespaces import *
extra = False # construct extra classes
config = Config('huang-2017',
source_file=relative_path(__file__, no_wd_value=__file__))
OntTerm.query.add(ontquery.plugin.get('rdflib')(Neuron.core_graph, OntId))
class NeuronHuang2017(NeuronEBM):
owlClass = ilxtr.NeuronHuang2017
shortname = 'Huang2017'
Neuron, Neuron_ = NeuronHuang2017, Neuron
dex = 'ilxtr:hasDriverExpressionPhenotype'
induced_exp = 'ilxtr:hasDriverExpressionInducedPhenotype'
const_exp = 'ilxtr:hasDriverExpressionConstitutivePhenotype'
def gid(i):
return OntId(f'npokb:{i}')
def main():
ndl_config = Config('neuron_data_lifted')
ndl_config.load_existing()
ndl_neurons = ndl_config.neurons()
bn_config = Config('basic-neurons')
bn_config.load_existing()
bn_neurons = bn_config.neurons()
resources = Path(devconfig.resources)
cutcsv = resources / 'common-usage-types.csv'
with open(cutcsv.as_posix(), 'rt') as f:
rows = [l for l in csv.reader(f)]
bc = byCol(rows)
(_, *labels), *_ = zip(*bc)
labels_set0 = set(labels)
ns = []
for n in ndl_neurons:
l = str(n.origLabel)
if l is not None:
for replace, match in rename_rules.items(): # HEH
l = l.replace(match, replace)
if l in labels:
n._origLabel = l
ns.append(n)
sns = set(n.origLabel for n in ns)
labels_set1 = labels_set0 - sns
agen = [c.label for c in bc if c.autogenerated]
sagen = set(agen)
added = [c.label for c in bc if c.added]
sadded = set(added)
ans = []
sans = set()
missed = set()
for n in bn_neurons:
continue # we actually get all of these with uberon, will map between them later
# can't use capitalize here because there are proper names that stay uppercase
l = n.label.replace('(swannt) ',
'').replace('Intrinsic',
'intrinsic').replace('Projection',
'projection')
for replace, match in rename_rules.items(): # HEH
l = l.replace(match, replace)
if l in agen:
n._origLabel = l
ans.append(n)
sans.add(l)
else:
missed.add(l)
agen_missing = sagen - sans
labels_set2 = labels_set1 - sans
nlx_labels = [c.label for c in bc if c.neurolex]
snlx_labels = set(nlx_labels)
class SourceCUT(resSource):
sourceFile = 'nifstd/resources/common-usage-types.csv' # FIXME relative to git workingdir...
source_original = True
sources = SourceCUT(),
swanr = rdflib.Namespace(interlex_namespace('swanson/uris/readable/'))
config = Config('common-usage-types-raw', sources=sources, source_file=relative_path(__file__),
prefixes={'swanr':swanr,
'SWAN':interlex_namespace('swanson/uris/neuroanatomical-terminology/terms/'),
'SWAA':interlex_namespace('swanson/uris/neuroanatomical-terminology/appendix/'),})
ins = [None if OntId(n.id_).prefix == 'TEMP' else n.id_ for n in ns]
ians = [None] * len(ans)
def zap(pes):
for pe in pes:
if pe not in (Phenotype('BIRNLEX:212', ilxtr.hasTaxonRank),
Phenotype('NCBITaxon:7742', ilxtr.hasTaxonRank),
Phenotype('BIRNLEX:252', ilxtr.hasTaxonRank),
Phenotype('BIRNLEX:516', ilxtr.hasTaxonRank),):
yield pe
with Neuron(CUT.Mammalia):
mamns = [NeuronCUT(*zap(n.pes), id_=i, label=n._origLabel, override=bool(i)).adopt_meta(n)
for i, n in zip(ins + ians, ns + ans)]
contains_rules = make_contains_rules()
skip = set()
smatch = set()
rem = {}
for l in labels_set2:
pes = tuple()
l_rem = l
for match, pheno in contains_rules.items():
t = None
if match not in skip and pheno == OntTerm:
try:
t = OntTerm(term=match)
print('WTF', match, t)
if t.validated:
pheno = Phenotype(t.u, ilxtr.hasSomaLocatedIn)
else:
pheno = None
except oq.exceptions.NotFoundError:
skip.add(match)
pheno = None
if match in skip and pheno == OntTerm:
pheno = None
if match in l_rem and pheno:
l_rem = l_rem.replace(match, '').strip()
pes += (pheno,)
if l_rem in exact_rules:
pes += (exact_rules[l_rem],)
l_rem = ''
if l_rem == ' neuron':
l_rem = ''
elif l_rem.endswith(' cell'):
l_rem = l_rem[:-len(' cell')]
#print('l_rem no cell:', l_rem)
elif l_rem.endswith(' neuron'):
l_rem = l_rem[:-len(' neuron')]
#print('l_rem no neuron:', l_rem)
hrm = [pe for pe in pes if pe.e == ilxtr.hasSomaLocatedIn]
if ' ' in l_rem:
#print('l_rem:', l_rem)
#embed()
maybe_region, rest = l_rem.split(' ', 1)
elif noneMembers(l_rem, *terminals) and not hrm:
maybe_region, rest = l_rem, ''
#print('MR:', maybe_region)
else:
#print(hrm)
maybe_region = None
if maybe_region:
prefix_rank = ('UBERON', 'SWAN', 'BIRNLEX', 'SAO', 'NLXANAT')
def key(ot):
ranked = ot.prefix in prefix_rank
arg = ot._query_result._QueryResult__query_args['term'].lower()
return (not ranked,
prefix_rank.index(ot.prefix) if ranked else 0,
not (arg == ot.label.lower()))
#t = OntTerm(term=maybe_region)
# using query avoids the NoExplicitIdError
ots = sorted((qr.OntTerm for qr in OntTerm.query(term=maybe_region,
exclude_prefix=('FMA',))), key=key)
if not ots:
log.error(f'No match for {maybe_region!r}')
else:
t = ots[0]
if 'oboInOwl:id' in t.predicates: # uberon replacement
t = OntTerm(t.predicates['oboInOwl:id'])
t.set_next_repr('curie', 'label')
log.info(f'Match for {maybe_region!r} was {t!r}')
if t.validated:
l_rem = rest
pheno = Phenotype(t.u, ilxtr.hasSomaLocatedIn) # FIXME
pes += (pheno,)
if pes:
smatch.add(l)
rem[l] = l_rem
with Neuron(CUT.Mammalia):
NeuronCUT(*zap(pes), id_=make_cut_id(l), label=l, override=True)
labels_set3 = labels_set2 - smatch
added_unmapped = sadded & labels_set3
# TODO preserve the names from neuronlex on import ...
Neuron.write()
Neuron.write_python()
raw_neurons = config.neurons()
config = Config('common-usage-types', sources=sources, source_file=relative_path(__file__),
prefixes={'swanr':swanr,
'SWAN':interlex_namespace('swanson/uris/neuroanatomical-terminology/terms/'),
'SWAA':interlex_namespace('swanson/uris/neuroanatomical-terminology/appendix/'),})
ids_updated_neurons = [n.asUndeprecated() for n in raw_neurons]
assert len(ids_updated_neurons) == len(raw_neurons)
Neuron.write()
Neuron.write_python()
progress = len(labels_set0), len(sns), len(sans), len(smatch), len(labels_set1), len(labels_set2), len(labels_set3)
print('\nProgress:\n'
f'total: {progress[0]}\n'
f'from nlx: {progress[1]}\n'
f'from basic: {progress[2]}\n'
f'from match: {progress[3]}\n'
f'TODO after nlx: {progress[4]}\n'
f'TODO after basic: {progress[5]}\n'
f'TODO after match: {progress[6]}\n')
assert progress[0] == progress[1] + progress[4], 'neurolex does not add up'
assert progress[4] == progress[2] + progress[5], 'basic does not add up'
lnlx = set(n.lower() for n in snlx_labels)
sos = set(n.origLabel.lower() if n.origLabel else None for n in ndl_neurons) # FIXME load origLabel
nlx_review = lnlx - sos
nlx_missing = sorted(nlx_review)
print(f'\nNeuroLex listed as source but no mapping (n = {len(nlx_review)}):')
_ = [print(l) for l in nlx_missing]
partial = {k:v for k, v in rem.items() if v and v not in terminals}
print(f'\nPartially mapped (n = {len(partial)}):')
if partial:
mk = max((len(k) for k in partial.keys())) + 2
for k, v in sorted(partial.items()):
print(f'{k:<{mk}} {v!r}')
#print(f'{k!r:<{mk}}{v!r}')
#pprint(partial, width=200)
unmapped = sorted(labels_set3)
print(f'\nUnmapped (n = {len(labels_set3)}):')
_ = [print(l) for l in unmapped]
if __name__ == '__main__':
rows = export_for_review(config, unmapped, partial, nlx_missing)
embed()
return config, unmapped, partial, nlx_missing