def test_ttl_simple(self):
# this fails when
# test_integration.py is run
# AND
# test_roundtrip_py is run
# but NOT when either is run independently
from neurondm import Config, Neuron, Phenotype, NegPhenotype
config = Config('test-ttl', ttl_export_dir=tel, py_export_dir=pyel)
Neuron(Phenotype('TEMP:turtle-phenotype'))
Neuron(NegPhenotype('TEMP:turtle-phenotype'))
config.write()
a = config.neurons()
config2 = Config('test-ttl', ttl_export_dir=tel, py_export_dir=pyel)
config2.load_existing()
config2.write_python()
b = config2.neurons()
config3 = Config('test-ttl', ttl_export_dir=tel, py_export_dir=pyel)
config3.load_python()
c = config3.neurons()
print(a, b, c)
assert config.existing_pes is not config2.existing_pes is not config3.existing_pes
assert a == b == c
def test_zz_adopt(self):
from neurondm.lang import Neuron, Phenotype, Config
ndl_config = Config('neuron_data_lifted')
ndl_config.load_existing()
bn_config = Config('basic-neurons')
bn_config.load_existing()
ndl_neurons = ndl_config.neurons()
bn_neurons = bn_config.neurons()
config = Config('__test_output', ttl_export_dir=tel)
shapeshifter = Neuron(Phenotype('TEMP:soul-stealer'))
for n in ndl_neurons:
shapeshifter.adopt_meta(n)
assert list(n.synonyms)
assert list(n.definitions)
n.write()
assert list(shapeshifter.synonyms)
assert list(shapeshifter.definitions)
shapeshifter.write()
# it eats _all_ of them
allsyns = set(shapeshifter.synonyms)
alldefs = set(shapeshifter.definitions)
assert all(s in allsyns for s in n.synonyms)
assert all(s in alldefs for s in n.definitions)
def needs_keast_namespace():
""" define neurons for keast spinal """
common = {
L1: (L1_vr, L1_wr),
L2: (L2_vr, L2_wr),
}
common67 = {
L1: (L1_gr, ),
L2: (L2_gr, ),
}
neuron_2 = [Neuron(PG, n_bl, synloc) for synloc in (BNWsyn, BDWsyn)]
neuron_4 = []
with Neuron(SPG4, n_ps, PGax, n_bl):
for soma_location_id in four_soma_locs:
somaloc = Phenotype(soma_location_id, slp)
for exits_via in ((L6_gr, ), (
S1ax,
S1_gr,
)):
for synapses_on in (BNVWsyn, BDVWsyn):
# 4
n4 = Neuron(somaloc, *exits_via, synapses_on)
neuron_4.append(n4)
neuron_6 = []
neuron_7 = []
neuron_8 = []
with NeuronKeast2020(VII, SPGfcon):
for i, somaloc in enumerate((L1, L2)):
soma_index = i + 2 # L1 aligns to the 3rd the sypathetic ganglion
with Neuron(somaloc, *common[somaloc]):
with Neuron(n_ls, *common67[somaloc]):
# 6
n6 = Neuron(IMGax, n_hg, PGsyn)
neuron_6.append(n6) # TODO fcon
# 7
n7 = Neuron(IMGsyn)
neuron_7.append(n7) # TODO fcon
# 8
for syn_index, synloc in enumerate(four_soma_locs):
axons_in = syn_chain_axons_in(syn_index, soma_index)
syn = Phenotype(synloc, synp)
n8 = Neuron(syn, *axons_in)
neuron_8.append(n8)
def test_fail(self):
from neurondm.lang import Neuron, Phenotype, Config
bn_config = Config('basic-neurons')
# TODO config.activate()? context manager for config ... too ...
Neuron(Phenotype('TEMP:test'))
try:
bn_config.load_existing()
raise AssertionError(
'Should have failed because a neuron has been created')
except Config.ExistingNeuronsError as e:
pass
def test_py_simple(self):
from neurondm import Config, Neuron, Phenotype, NegPhenotype
config = Config('test-py', ttl_export_dir=tel, py_export_dir=pyel)
n1 = Neuron(Phenotype('TEMP:python-phenotype'))
n2 = Neuron(NegPhenotype('TEMP:python-phenotype'))
assert n1 != n2
config.write_python()
config2 = Config('test-py', ttl_export_dir=tel, py_export_dir=pyel)
config2.load_python() # FIXME load existing python ...
config2.write()
config3 = Config('test-py', ttl_export_dir=tel, py_export_dir=pyel)
config3.load_existing()
assert config.existing_pes is not config2.existing_pes is not config3.existing_pes
assert config.neurons() == config2.neurons() == config3.neurons()
def test_0_equality(self):
""" make sure that __eq__ and __hash__ are implmented correctly """
from neurondm import Config, Neuron, Phenotype, NegPhenotype
from neurondm import LogicalPhenotype, AND, OR, ilxtr
from neurondm import NeuronCUT, NeuronEBM
config = Config('test-equality',
ttl_export_dir=tel,
py_export_dir=pyel)
pp = Phenotype(ilxtr.a)
pn = NegPhenotype(ilxtr.a)
assert pp != pn
assert hash(pp) != hash(pn)
assert Phenotype(ilxtr.c) == Phenotype(ilxtr.c)
assert NegPhenotype(ilxtr.c) == NegPhenotype(ilxtr.c)
assert hash(Phenotype(ilxtr.c)) == hash(Phenotype(ilxtr.c))
assert hash(NegPhenotype(ilxtr.c)) == hash(NegPhenotype(ilxtr.c))
lppo = LogicalPhenotype(OR, pp, Phenotype(ilxtr.b))
lppa = LogicalPhenotype(AND, pp, Phenotype(ilxtr.b))
lpno = LogicalPhenotype(OR, pn, NegPhenotype(ilxtr.b))
assert lppo != lppa
assert lppo != lpno
assert lppa != lpno
assert hash(lppo) != hash(lppa)
assert hash(lppo) != hash(lpno)
assert hash(lppa) != hash(lpno)
lpe1 = LogicalPhenotype(OR, Phenotype(ilxtr.c), Phenotype(ilxtr.d))
lpe2 = LogicalPhenotype(OR, Phenotype(ilxtr.c), Phenotype(ilxtr.d))
assert lpe1 == lpe2
assert hash(lpe1) == hash(lpe2)
# make sure that self.pes equality works
assert (pp, pn, lppo, lppa, lpno) == (pp, pn, lppo, lppa, lpno)
npp = Neuron(pp)
npn = Neuron(pn)
assert npp != npn
assert hash(npp) != hash(npn)
nlppo = Neuron(lppo)
nlppa = Neuron(lppa)
nlpno = Neuron(lpno)
assert nlppo != nlppa
assert nlppo != nlpno
assert nlppa != nlpno
assert hash(nlppo) != hash(nlppa)
assert hash(nlppo) != hash(nlpno)
assert hash(nlppa) != hash(nlpno)
assert Neuron(pp) == Neuron(pp)
assert Neuron(pn) == Neuron(pn)
assert hash(Neuron(pp)) == hash(Neuron(pp))
assert hash(Neuron(pn)) == hash(Neuron(pn))
assert NeuronCUT(pp) != NeuronEBM(pp)
assert hash(NeuronCUT(pp)) != hash(NeuronEBM(pp))
def needs_keast_namespace(config):
""" define neurons for keast spinal """
plut = {v:k for k, v in Keast2020.items()}
def fn(phen):
return plut[phen]
# define the inferred parents for neuron populations
for n in (2, 4, 5, 6, 7, 8, 9, 10, 11, 12):
NeuronKeast2020(Keast2020[f'ntk_{n}'], id_=ntkb[str(n)], **kld(n))
# FIXME hack
config.out_graph.add((npkb[str(n)], rdfs.subClassOf, ilxtr.Phenotype))
# FIXME spinal cord white matter axons are missing from these bags
# neuron populations
neuron_1 = [kNeuron(PG, n_bl, BDWsyn, BNWsyn, para, post, id_=ntkb['1'], **kld(1))] # pos
neuron_2 = [kNeuron(PG, n_bl, synloc, sym, post, ntk_2_ent, id_=ntku[f'2-{fn(synloc)}'])
for synloc in (BDWsyn, BNWsyn)] # sos # FIXME smooth muslce only
neuron_3 = [kNeuron(IMG, BDWsyn, BNWsyn, sym, post, id_=ntkb['3'], **kld(3))] # sos # FIXME smooth muscle only
neuron_4 = [] # sos
with Neuron(n_ps, PGax, n_bl, sym, post, id_=ntkh['common-4']):
for soma_location_id in four_soma_locs:
somaloc = Phenotype(soma_location_id, slp)
for exits_via in ((L6_gr,), (S1ax, S1_gr,)):
for synapses_on in (BNVWsyn, BDVWsyn):
# 4
n4 = kNeuron(
somaloc, *exits_via, synapses_on, ntk_4_ent,
id_=ntku[f'4-{fn(somaloc)}-{fn(exits_via[0])}-{fn(synapses_on)}'])
neuron_4.append(n4)
neuron_5 = [] # pre
with Neuron(VII, n_ps, PGax, PGsyn, ntk_1_fcon, para, pre,
id_=ntkh['common-5']):
for somaloc, ventral_root_exit in zip((L6, S1),
(L6_vr, S1_vr)):
n5 = kNeuron(somaloc, ventral_root_exit, ntk_5_ent,
id_=ntku[f'5-{fn(somaloc)}'])
neuron_5.append(n5)
common = {L1: (L1_vr, L1_wr),
L2: (L2_vr, L2_wr),}
common67 = {L1: (L1_gr,),
L2: (L2_gr,),}
neuron_6 = [] # sre
neuron_7 = [] # sre
neuron_8 = [] # sre
with Neuron(VII, WMax, sos_fcon, sym, pre,
id_=ntkh['common-6-7-8']):
for i, somaloc in enumerate((L1, L2)):
soma_index = i + 2 # L1 aligns to the 3rd the sypathetic ganglion
# which is of course the L1 sypathetic ganglion, but it is
# the 3rd ganlion in the model with a neuron 4 soma
with Neuron(somaloc, *common[somaloc], id_=ntkh[f'common-6-7-8-{fn(somaloc)}']):
with Neuron(n_ls, *common67[somaloc], id_=ntkh[f'common-6-7-{fn(somaloc)}-extra']):
# 6
n6 = kNeuron(IMGax, n_hg, PGsyn, ntk_6_ent, ntk_2_fcon,
id_=ntku[f'6-{fn(somaloc)}'])
neuron_6.append(n6)
# 7
n7 = kNeuron(IMGsyn, ntk_7_ent, ntk_3_fcon,
id_=ntku[f'7-{fn(somaloc)}'])
neuron_7.append(n7)
# 8
for syn_index, synloc in enumerate(four_soma_locs):
axons_in = syn_chain_axons_in(syn_index, soma_index)
syn = Phenotype(synloc, synp)
n8 = kNeuron(syn, *axons_in, ntk_8_ent, ntk_4_fcon,
id_=ntku[f'8-{fn(somaloc)}-{fn(syn)}'])
neuron_8.append(n8)
neuron_9 = [] # slm
with Neuron(IX, WMax, n_pu, URTsyn, motor, id_=ntkh['common-9']):
for somaloc, ventral_root_exit in zip((L5, L6),
(L5_vr, L6_vr)):
n9 = kNeuron(somaloc, ventral_root_exit, ntk_9_ent, id_=ntku[f'9-{fn(somaloc)}'])
neuron_9.append(n9)
neuron_10 = [] # fos
neuron_11 = [] # fos
neuron_12 = [] # fos
with Neuron(Isyn, IIsyn, Vsyn, VIIsyn, Xsyn, sens,
id_=ntkh['common-10-11-12']): # 10 11 12 XXX layers are wrong, intersection has to come before phenotype
for somaloc in (L6_dr, S1_dr):
n10 = kNeuron(somaloc, n_ps_dn, PGdn, n_bl_dn, ntk_10_ent,
id_=ntku[f'10-{fn(somaloc)}']) # TODO a bunch of other sensory terminals
neuron_10.append(n10)
n12 = kNeuron(somaloc, n_pu_dn, URTdn, ntk_12_ent,
id_=ntku[f'12-{fn(somaloc)}']) # TODO URTdn layer is rhabdosphincter
neuron_12.append(n12)
for somaloc in (L1_dr, L2_dr):
n11 = kNeuron(somaloc, n_ls_dn, IMGdn, n_hg_dn, PGdn, n_bl_dn, ntk_11_ent,
id_=ntku[f'11-{fn(somaloc)}']) # TODO bladder and layers
neuron_11.append(n11)
# thankfully alphabetical from head to toe
with Neuron(pons_ax, id_=ntkh['axon-pons']):
with Neuron(midbrain_ax, id_=ntkh['axon-midbrain']):
with Neuron(dienc_ax, id_=ntkh['axon-dienceph']):
with Neuron(cn_ax, id_=ntkh['axon-cernuc']):
neuron_13 = [kNeuron(BNST, BRGTNsyn, id_=ntkb['13'], **kld(13))] # soma in BNST ???
neuron_14 = [kNeuron(CeA, BRGTNsyn, id_=ntkb['14'], **kld(14))] # soma in CeA ???
neuron_15 = [kNeuron(MPOA, BRGTNsyn, id_=ntkb['15'], **kld(15))] # soma in MPOA ???
neuron_16 = [kNeuron(MnPO, BRGTNsyn, id_=ntkb['16'], **kld(16))] # soma in MnPO ???
neuron_17 = [kNeuron(LPOA, BRGTNsyn, id_=ntkb['17'], **kld(17))] # soma in LPOA ???
neuron_18 = [kNeuron(LHA, BRGTNsyn, id_=ntkb['18'], **kld(18))] # soma in LHA ???
neuron_19 = [kNeuron(VLPAG, BRGTNsyn, id_=ntkb['19'], **kld(19))] # soma in VLPAG ???
neuron_20 = [kNeuron(BRGTN, id_=ntkb['20'], **kld(20))] # soma in BRGTN ??? # TODO many a syntapse
# implicit types (parents) # FIXME XXX required modelling in many cases
fos = first_order_sensory = [neuron_10, neuron_11, neuron_12]
slm = somatic_lower_motor = [neuron_9]
_pre = parasympathetic_pre_ganglionic = [neuron_5]
pos = parasympathetic_post_ganglionic = [neuron_1]
# para pre -> has soma located in some not sympathetic chain and projects to some ganglion that is also projected to by some sym pre
# para post -> has reverse connection phenotype some para pre
sre = sympathetic_pre_ganglionic = [neuron_6, neuron_7, neuron_8]
sos = sympathetic_post_ganglionic = [neuron_2, neuron_3, neuron_4]
class Keast2020(LocalNameManager):
locals().update(ntks) # LOL ?! this works !?
# soma layers
VII= Phenotype('UBERON:0016578', sllp) # 'ilxtr:spinal-VII'
IX = Phenotype('UBERON:0016580', sllp) # 'ilxtr:spinal-IX'
# soma locations
#L1 = Phenotype('ilxtr:spinal-L1', slp)
#L2 = Phenotype('ilxtr:spinal-L2', slp)
#L5 = Phenotype('ilxtr:spinal-L5', slp)
#L6 = Phenotype('ilxtr:spinal-L6', slp)
#S1 = Phenotype('ilxtr:spinal-S1', slp)
L1 = Phenotype('UBERON:0006448', slp) # XXX fma in sct
L2 = Phenotype('UBERON:0006450', slp) # XXX fma in sct
L5 = Phenotype('UBERON:0006447', slp) # XXX fma in sct
L6 = Phenotype('ILX:0793358', slp)
S1 = Phenotype('UBERON:0006460', slp) # XXX fma in sct
IMG = Phenotype('UBERON:0005453', slp) # ilxtr:IMG
PG = Phenotype('UBERON:0016508', slp) # ilxtr:PG
#L1_dr = Phenotype('ilxtr:dr-L1', slp) # soma implies ax + dn
#L2_dr = Phenotype('ilxtr:dr-L2', slp)
#L6_dr = Phenotype('ilxtr:dr-L6', slp)
#S1_dr = Phenotype('ilxtr:dr-S1', slp)
L1_dr = Phenotype('UBERON:0002855', slp) # soma implies ax + dn
L2_dr = Phenotype('UBERON:0002856', slp)
L6_dr = Phenotype('ILX:0793360', slp)
S1_dr = Phenotype('UBERON:0002860', slp)
# sort of nerves
L1_vr = Phenotype('ILX:0785421', alp) # ventral root 'ilxtr:vr-L1' # XXX fma in sct
L1_wr = Phenotype('ILX:0793220', alp) # white ramus 'ilxtr:wr-L1' # XXX FMA in sct
L1_gr = Phenotype('ILX:0785825', alp) # gray ramus 'ilxtr:gr-L1' # XXX fma in sct
L2_vr = Phenotype('ILX:0788675', alp) # 'ilxtr:vr-L2' # XXX fma in sct
L2_wr = Phenotype('ILX:0793221', alp) # 'ilxtr:wr-L2' # XXX FMA in sct
L2_gr = Phenotype('ILX:0785733', alp) # 'ilxtr:gr-L2' # XXX fma in sct
L5_vr = Phenotype('ILX:0791148', alp) # 'ilxtr:vr-L5' # XXX fma in sct
L6_gr = Phenotype('ILX:0739299', alp) # 'ilxtr:gr-L6'
L6_vr = Phenotype('ILX:0793615', alp) # 'ilxtr:vr-L6'
S1_gr = Phenotype('ILX:0793228', alp) # 'ilxtr:gr-S1' # XXX fma in sct
S1_vr = Phenotype('ILX:0792853', alp) # 'ilxtr:vr-S1' # XXX fma in sct
IMGax = Phenotype('UBERON:0005453', alp)
PGax = Phenotype('UBERON:0016508', alp)
S1ax = Phenotype('ILX:0793350', alp) # 'ilxtr:sc-S1'
# ilxtr:spinal-white-matter technically a layer
# FIXME because axon location does not have cardianlity 1 WMax is
# ambiguous unless it is composed with a layer of the spinal cord
# because the operation is not commutative with card > 1
#WMax = Phenotype('ilxtr:spinal-white-matter', alp)
WMax = Phenotype('UBERON:0002318', alp)
# sensory dendrite sort of nerves
IMGdn = Phenotype('UBERON:0005453', dlp)
PGdn = Phenotype('UBERON:0016508', dlp)
# XXX FIXME I'm being something of a literalist here, and guessing that
# the boxes that are drawn for these might not actually be what is intended
cn_ax = Phenotype('UBERON:0010011', alp) # collection of basal ganglia, syn cerebral nuclei ??
dienc_ax = Phenotype('UBERON:0001894', alp)
midbrain_ax = Phenotype('UBERON:0001891', alp)
pons_ax = Phenotype('UBERON:0000988', alp)
medulla_ax = Phenotype('UBERON:0001896', alp)
# nerves
#n_bl = Phenotype('ilxtr:nerve-bladder', alp)
#n_hg = Phenotype('ilxtr:nerve-hypogastric', alp)
#n_ls = Phenotype('ilxtr:nerve-lumbar-splanic', alp)
#n_ps = Phenotype('ilxtr:nerve-pelvic-splanic', alp) # splanchnic
#n_pu = Phenotype('ilxtr:nerve-pudendal', alp)
n_bl = Phenotype('ILX:0793559', alp)
n_hg = Phenotype('UBERON:0005303', alp)
n_ls = Phenotype('UBERON:0018683', alp)
n_ps = Phenotype('UBERON:0018675', alp)
n_pu = Phenotype('UBERON:0011390', alp)
# sensory ??ents
#n_bl_dn = Phenotype('ilxtr:nerve-bladder', dlp)
#n_hg_dn = Phenotype('ilxtr:nerve-hypogastric', dlp)
#n_ls_dn = Phenotype('ilxtr:nerve-lumbar-splanic', dlp)
#n_ps_dn = Phenotype('ilxtr:nerve-pelvic-splanic', dlp)
#n_pu_dn = Phenotype('ilxtr:nerve-pudendal', dlp)
n_bl_dn = Phenotype('ILX:0793559', dlp)
n_hg_dn = Phenotype('UBERON:0005303', dlp)
n_ls_dn = Phenotype('UBERON:0018683', dlp)
n_ps_dn = Phenotype('UBERON:0018675', dlp)
n_pu_dn = Phenotype('UBERON:0011390', dlp)
#URTdn = Phenotype('ilxtr:urethra', dlp)
URTdn = Phenotype('UBERON:0000057', dlp)
# synaptic locations
IMGsyn = Phenotype('UBERON:0005453', synp)
PGsyn = Phenotype('UBERON:0016508', synp)
BNVWsyn = Phenotype('ilxtr:bladder-neck-vessel-wall', synp)
BDVWsyn = Phenotype('ilxtr:bladder-dome-vessel-wall', synp)
BDWsyn = Phenotype('ilxtr:bladder-dome-wall', synp)
BNWsyn = Phenotype('ILX:0774144', synp) # 'ilxtr:bladder-neck-wall'
#URTsyn = Phenotype('ilxtr:urethra', synp)
URTsyn = Phenotype('UBERON:0000057', synp)
BNST = Phenotype('UBERON:0001880', slp)
CeA = Phenotype('UBERON:0002883', slp) # central amygdala
MPOA = Phenotype('UBERON:0007769', slp)
MnPO = Phenotype('UBERON:0002625', slp)
LPOA = Phenotype('UBERON:0001931', slp)
LHA = Phenotype('UBERON:0002430', slp)
VLPAG = Phenotype('ILX:0793626', slp) # vent lat peri aq gray
BRGTN = Phenotype('UBERON:0007632', slp) # barrington's nucleus
BRGTNsyn = Phenotype('UBERON:0007632', synp)
# FIXME synp into layer has the cardinality issue
# with the fact that (intersection (phenotype layer hp) (phenotype region hp))
# is not commutative with (phenotype (intersection layer region) hp)
# when the relationship hp allows cardinality > 1
#Isyn = Phenotype('ilxtr:spinal-I', synp)
#IIsyn = Phenotype('ilxtr:spinal-II', synp)
#Vsyn = Phenotype('ilxtr:spinal-V', synp)
#VIIsyn = Phenotype('ilxtr:spinal-VII', synp)
#Xsyn = Phenotype('ilxtr:spinal-X', synp)
Isyn = Phenotype('UBERON:0006118', synp)
IIsyn = Phenotype('ILX:0110092', synp)
Vsyn = Phenotype('UBERON:0016576', synp)
VIIsyn = Phenotype('UBERON:0016578', synp)
Xsyn = Phenotype('ILX:0110100', synp)
# sensory substructure locations
# target cell types
#ntk_4 = Phenotype('ilxtr:neuron-phenotype-keast-4', 'ilxtr:hasPhenotype') # FIXME what is this really?
#ntk_4_ent = EntailedPhenotype('ilxtr:neuron-phenotype-keast-4', 'ilxtr:hasPhenotype') # FIXME what is this really?
# XXX using a specific phenotype here is undesireable, because it must _always_ be asserted
# in order for membership to be determined, this is true for individuals as well, therefore this
# should be used as entailed restriction so that anything that matches the necessary contitions
# will automatically classify as a result
# is this a phenotype or a superclass? XXX kind of dirty to do it this way by creating an aribrary
# phenotype that is borne by these, but being marked as a keast-type-4 neuron is a thing so here we are
# in a pure type world, this would be the coloring and the numbering, but it is more assertional
# making it a superclass is probably better than
ntk_1_fcon = Phenotype('ilxtr:neuron-type-keast-1', fconp)
ntk_2_fcon = Phenotype('ilxtr:neuron-type-keast-2', fconp)
ntk_3_fcon = Phenotype('ilxtr:neuron-type-keast-3', fconp)
ntk_4_fcon = Phenotype('ilxtr:neuron-type-keast-4', fconp)
# FIXME this is super confusing, but this id is equivalent to all
# neurons that bear the ilxtr:neuron-phenotype-sym-post phenotype
sos_fcon = Phenotype('ilxtr:sympathetic-post-ganglionic', fconp) # FIXME likely overly broad?
# (para)?sympathetic colorings (until we can get deeper modelling correct)
#para_pre = Phenotype('ilxtr:neuron-phenotype-para-pre')
#para_post = Phenotype('ilxtr:neuron-phenotype-para-post')
#sym_pre = Phenotype('ilxtr:neuron-phenotype-sym-pre')
#sym_post = Phenotype('ilxtr:neuron-phenotype-sym-post')
para = Phenotype('ilxtr:ParasympatheticPhenotype')
sym = Phenotype('ilxtr:SympatheticPhenotype')
pre = Phenotype('ilxtr:PreGanglionicPhenotype')
post = Phenotype('ilxtr:PostGanglionicPhenotype')
sens = Phenotype('ilxtr:SensoryPhenotype')
motor = Phenotype('ilxtr:MotorPhenotype')
# para pre -> has soma located in some not sympathetic chain and projects to some ganglion that is also projected to by some sym pre
# para post -> has reverse connection phenotype some para pre
sre = sympathetic_pre_ganglionic = [neuron_6, neuron_7, neuron_8]
sos = sympathetic_post_ganglionic = [neuron_2, neuron_3, neuron_4]
# sym pre -> has forward connection phenotype some neuron population with cell body ni synaptic chain or other sympathetic ganglion???
# sym post -> has reverse connection phenotype some sym pre
# XXX these definitions are not good an collide with para I think
#[print(repr(n)) for n in Neuron.neurons()]
ntks = {k: v
for i in range(1,13)
for (k, v) in (
(f'ntk_{i}', Phenotype(npkb[str(i)], 'ilxtr:hasPhenotype')),
(f'ntk_{i}_ent', EntailedPhenotype(npkb[str(i)], 'ilxtr:hasPhenotype')))}
class Keast2020(LocalNameManager):
locals().update(ntks) # LOL ?! this works !?
# soma layers
VII= Phenotype('UBERON:0016578', sllp) # 'ilxtr:spinal-VII'
IX = Phenotype('UBERON:0016580', sllp) # 'ilxtr:spinal-IX'
# soma locations
#L1 = Phenotype('ilxtr:spinal-L1', slp)
#L2 = Phenotype('ilxtr:spinal-L2', slp)
class Keast2020(LocalNameManager):
# soma layers
VII = Phenotype('ilxtr:spinal-VII', 'ilxtr:hasSomaLocatedInLayer')
# soma locations
L1 = Phenotype('ilxtr:spinal-L1', slp)
L2 = Phenotype('ilxtr:spinal-L2', slp)
IMG = Phenotype('ilxtr:IMG', slp)
PG = Phenotype('ilxtr:PG', slp)
# sort of nerves
L1_vr = Phenotype('ilxtr:vr-L1', alp)
L1_wr = Phenotype('ilxtr:wr-L1', alp)
L1_gr = Phenotype('ilxtr:gr-L1', alp)
L2_vr = Phenotype('ilxtr:vr-L2', alp)
L2_wr = Phenotype('ilxtr:wr-L2', alp)
L2_gr = Phenotype('ilxtr:gr-L2', alp)
L6_gr = Phenotype('ilxtr:gr-L6', alp)
S1_gr = Phenotype('ilxtr:gr-S1', alp)
IMGax = Phenotype('ilxtr:IMG', alp)
PGax = Phenotype('ilxtr:PG', alp)
S1ax = Phenotype('ilxtr:sc-S1', alp)
# nerves
n_ls = Phenotype('ilxtr:nerve-lumbar-splanic', alp)
n_hg = Phenotype('ilxtr:nerve-hypogastric', alp)
n_ps = Phenotype('ilxtr:nerve-pelvic-splanic', alp)
n_bl = Phenotype('ilxtr:nerve-bladder', alp)
# synaptic locations
IMGsyn = Phenotype('ilxtr:IMG', synp)
PGsyn = Phenotype('ilxtr:PG', synp)
BNVWsyn = Phenotype('ilxtr:bladder-neck-vessel-wall', synp)
BDVWsyn = Phenotype('ilxtr:bladder-dome-vessel-wall', synp)
BDWsyn = Phenotype('ilxtr:bladder-dome-wall', synp)
BNWsyn = Phenotype('ilxtr:bladder-neck-wall', synp)
# target cell types
SPGfcon = Phenotype('ilxtr:sympathetic-post-ganglionic', fconp)
SPG4 = Phenotype('ilxtr:keast-neuron-4-type',
'ilxtr:hasPhenotype') # FIXME what is this really?
four_soma_locs = [
'ilxtr:sc-T12',
'ilxtr:sc-T13',
'ilxtr:sc-L1',
'ilxtr:sc-L2',
'ilxtr:sc-L3',
'ilxtr:sc-L4',
'ilxtr:sc-L5',
'ilxtr:sc-L6',
#'ilxtr:sc-S1',
]
[
setattr(Keast2020, f'sc{sl.split("-")[-1]}', Phenotype(sl, slp))
for sl in four_soma_locs
]
[
setattr(Keast2020, f'sc{sl.split("-")[-1]}ax', Phenotype(sl, alp))
for sl in four_soma_locs
]
[
setattr(Keast2020, f'sc{sl.split("-")[-1]}syn', Phenotype(sl, synp))
for sl in four_soma_locs
]
sympathetic_chain_axons = [Phenotype(l, alp)
for l in four_soma_locs] # FIXME intersegmental