def back_transform(self, scores):
"transform nore score back to orginal data"
values = np.full_like(scores, np.nan)
lo_value = self.transform_table['value'][0]
up_value = self.transform_table['value'][-1]
lo_score = self.transform_table['score'][0]
up_score = self.transform_table['score'][-1]
# scores in normal range
normal_mask = np.logical_and(scores <= up_score, scores >= lo_score)
normal_scores = scores[normal_mask]
values[normal_mask] = self.back_func(normal_scores)
# scores in lower tail: 1=linear, 2=power
lower_mask = scores < lo_score
lower_scores = scores[lower_mask]
temp = list()
for sc in lower_scores:
backtr = lo_value
cdflo = gcum(lo_score)
cdfbt = gcum(sc)
if self.ltail == 1: # linear
backtr = powint(0, cdflo, self.zmin, lo_value, cdfbt, 1)
temp.append(backtr)
elif self.ltail == 2: # power
cpow = 1.0 / self.ltpar
backtr = powint(0, cdflo, self.zmin, lo_value, cdfbt, cpow)
temp.append(backtr)
values[lower_mask] = temp
# scores in upper tail: 1=linear, 2=power, 4=hyperbolic
upper_mask = scores > up_score
upper_scores = scores[upper_mask]
temp = list()
for sc in up_score:
backtr = up_value
cdfhi = gcum(up_score)
cdfbt = gcum(sc) # cdf value of the score to be back-transformed
if self.utail == 1: # linear
backtr = powint(cdfhi, 1.0, up_value, self.zmax, cdfbt, 1)
temp.append(backtr)
elif self.utail == 2: # power
cpow = 1.0 / self.utpar
backtr = powint(cdfhi, 1.0, up_value, self.zmax, cdfbt, cpow)
temp.append(backtr)
elif self.utail == 4: # hyperbolic
l = (up_value**self.utpar) * (1 - gcum(up_score))
backtr = (l / (1 - gcum(sc)))**(1 / self.utpar)
temp.append(backtr)
values[upper_mask] = temp
return values
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