def RunAnalysis(self, df, group_name, output_dir):
rows_sb = []
rows_mb = []
for _, row in df.iterrows():
if filter.IsShanghainese().IsMatched(row) and filter.IsPosition('b').IsMatched(row):
rows_sb.append(row)
continue
if filter.IsMandarin().IsMatched(row) and filter.IsPosition('b').IsMatched(row):
rows_mb.append(row)
continue
df_sb = pd.DataFrame(rows_sb)
df_sb = ComputeF1F2Diff(df_sb)
df_sb_avg = pd.DataFrame(
df_sb.loc[:, df_sb.columns.str.startswith("diff")].mean()).T
df_sb.to_csv(output_dir / (group_name +
'@@@Sb.debug.csv'), index=False)
df_sb_avg.to_csv(output_dir / (group_name +
'@@@SbMean.debug.csv'), index=False)
df_mb = pd.DataFrame(rows_mb)
df_mb = ComputeF1F2Diff(df_mb)
df_mb_avg = pd.DataFrame(
df_mb.loc[:, df_mb.columns.str.startswith("diff")].mean()).T
df_mb.to_csv(output_dir / (group_name +
'@@@Mb.debug.csv'), index=False)
df_mb_avg.to_csv(output_dir / (group_name +
'@@@MbMean.debug.csv'), index=False)
x = ['Sb', 'Mb']
y = [df_sb_avg['diff_' + self.formant + '_7525'][0],
df_mb_avg['diff_'+self.formant+'_7525'][0]]
plt.bar(x, y, width=kBarWidth)
plt.title(group_name)
plt.savefig(output_dir / (group_name + '.png'), bbox_inches="tight")
plt.clf()
plt.cla()
def __init__(self):
super().__init__('F2', 'Sb', [filter.IsShanghainese(), filter.IsPosition('b')],
GetGender)
def __init__(self):
super().__init__('F2', 'Sa', [filter.IsShanghainese(), filter.IsPosition('a')],
GetAge)
def __init__(self):
super().__init__({
'Sa': [filter.IsShanghainese(), filter.IsPosition('a')],
'Sb': [filter.IsShanghainese(), filter.IsPosition('b')],
})
def __init__(self):
super().__init__({
'Sb': [filter.IsShanghainese(), filter.IsPosition('b')],
'Mb': [filter.IsMandarin(), filter.IsPosition('b')]
})
def __init__(self):
super().__init__([filter.IsShanghainese(), filter.IsPosition('b')])
import filter as ft
import analyzer as az
GROUP_A = ([
[ft.IsShanghainese(), ft.IsMandarin()],
[ft.IsMale(), ft.IsFemale()],
[ft.IsChild(), ft.IsYouth(),
ft.IsAdult(), ft.IsSenior()],
[ft.IsVariant('a1'), ft.IsVariant('a2')],
], [
az.FormantQuantiles(),
az.FormantRegression(),
])
GROUP_C = (
[
[ft.IsShanghainese(), ft.IsMandarin()],
[ft.IsMale(), ft.IsFemale()],
[ft.IsChild(), ft.IsYouth(),
ft.IsAdult(), ft.IsSenior()],
[
ft.IsVariant('c1'),
ft.IsVariant('c2'),
ft.IsVariant('c2vs'),
ft.IsVariant('c2h'),
ft.IsVariant('c4')
],
[
ft.IsWordNum([1, 2]),
ft.IsWordNum([3, 5, 6]),
ft.IsWordNum([7, 8, 9]),
