def to_devanagari(obj):
if isinstance(obj, (six.text_type, six.string_types)):
obj = SanskritObject(obj, encoding=SLP1)
if isinstance(obj, SanskritObject):
return obj.devanagari()
else:
return map(to_devanagari, obj)
def get(self, v):
""" Parse a presegmented sentence """
strict_p = True
if request.args.get("strict") == "false":
strict_p = False
vobj = SanskritObject(v,
strict_io=strict_p,
replace_ending_visarga=None)
parser = Parser(input_encoding="SLP1",
output_encoding="Devanagari",
replace_ending_visarga='s')
mres = []
print(v)
for split in parser.split(vobj.canonical(),
limit=10,
pre_segmented=True):
parses = list(split.parse(limit=10))
sdot = split.to_dot()
mres = [x.serializable() for x in parses]
pdots = [x.to_dot() for x in parses]
r = {
"input": v,
"devanagari": vobj.devanagari(),
"analysis": mres,
"split_dot": sdot,
"parse_dots": pdots
}
return r
def get(self, v):
""" Get morphological tags for v """
vobj = SanskritObject(v, strict_io=False, replace_ending_visarga=None)
g = analyzer.getSandhiSplits(vobj, tag=True)
if g:
splits = g.find_all_paths(10, score=True)
else:
splits = []
mres = {}
plotbase = {}
for sp in splits:
bn = f"{randint(0,9999):4}"
vg = VakyaGraph(sp, max_parse_dc=5)
sl = "_".join([n.devanagari(strict_io=False)
for n in sp])
for (ix, p) in enumerate(vg.parses):
if sl not in mres:
mres[sl] = []
t = []
for n in p:
preds = list(p.predecessors(n))
if preds:
pred = preds[0] # Only one
lbl = p.edges[pred, n]['label']
t.append(jedge(pred, n, lbl))
else:
t.append(jnode(n))
mres[sl].append(t)
plotbase[sl] = bn
try:
vg.write_dot(f"static/{bn}.dot")
except Exception:
pass
r = {"input": v, "devanagari": vobj.devanagari(), "analysis": mres, "plotbase": plotbase}
return r
def get(self, p):
""" Get lexical tags for p """
pobj = SanskritObject(p, strict_io=False)
tags = analyzer.getLexicalTags(pobj)
if tags is not None:
ptags = jtags(tags)
else:
ptags = []
r = {"input": p, "devanagari": pobj.devanagari(), "tags": ptags}
return r
def get(self, v):
""" Get lexical tags for v """
vobj = SanskritObject(v, strict_io=False, replace_ending_visarga=None)
g = analyzer.getSandhiSplits(vobj)
if g:
splits = g.find_all_paths(10)
jsplits = [[ss.devanagari(strict_io=False) for ss in s] for s in splits]
else:
jsplits = []
r = {"input": v, "devanagari": vobj.devanagari(), "splits": jsplits}
return r
def get(self, v):
""" Presegmented Split """
vobj = SanskritObject(v, strict_io=True, replace_ending_visarga=None)
parser = Parser(input_encoding="SLP1",
output_encoding="Devanagari",
replace_ending_visarga='s')
splits = parser.split(vobj.canonical(), limit=10, pre_segmented=True)
r = {
"input": v,
"devanagari": vobj.devanagari(),
"splits": [x.serializable()['split'] for x in splits]
}
return r
def getannotation(v):
""" Get morphological tags for v """
vobj = SanskritObject(v, strict_io=False, replace_ending_visarga=None)
g = analyzer.getSandhiSplits(vobj, tag=True)
if g:
splits = g.findAllPaths(10)
else:
splits = []
mres = {}
for sp in splits:
p = analyzer.constrainPath(sp)
if p:
sl = "_".join([spp.devanagari(strict_io=False) for spp in sp])
mres[sl] = []
for pp in p:
mres[sl].append([(spp.devanagari(strict_io=False),
jtag(pp[spp.canonical()])) for spp in sp])
r = {"input": v, "devanagari": vobj.devanagari(), "analysis": mres}
return r