import pandas as pd
import re
mapping = {s.split()[0]: s.split()[1] for s in """
AA AA
AE AE
AH UX
AO AO
AW AW
AY AY
B  b
CH C
D  d
DH D
EH EH
ER UXr
EY EY
F  f
G  g
HH h
IH IH
IY IY
JH J
K  k
L  l
M  m
N  n
NG N
OW OW
OY OY
P  p
R  r
S  s
SH S
T  t
TH T
UH UH
UW UW
V  v
W  w
Y  y
X  x
Z  z
ZH Z
""".strip().split('\n')}

sim_mat = pd.read_csv('./similarity.csv',header=0,index_col=0)

def convert_ph(ph):
    stress_level = re.search("(\w+)([0-9])",ph)
    if stress_level:
        return stress_level.group(2)+mapping[stress_level.group(1)]
    else:
        return mapping[ph]

def sim_mat_to_apple_table(smt):
    colnames = [convert_ph(ph) for ph in smt.index.tolist()]
    smt = pd.DataFrame(np.nan_to_num(smt.values))
    fsmt = (smt.T+smt)
    np.fill_diagonal(fsmt.values,100.0)
    asmt = pd.DataFrame.copy(fsmt)
    asmt.columns = colnames
    asmt.index = colnames
    apple_sim_lookup = asmt.stack().reset_index()
    apple_sim_lookup.columns = ['q','r','s']
    return apple_sim_lookup

apple_sim_lookup = sim_mat_to_apple_table(sim_mat)

def top_match(ph):
    selected = apple_sim_lookup[(apple_sim_lookup.q == ph) & (apple_sim_lookup.s < 100) & (apple_sim_lookup.s >= 70)]
    tm = ph
    if len(selected) > 0:
        tm = pd.DataFrame.sort_values(selected,'s',ascending=False).iloc[0].r
    return tm

def similar_phoneme(ph_str):
    return ph_str