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#!/usr/bin/env python
import numpy as np
from scipy import stats
class InterQuartileRange:
def __init__(self, low, high, extend = False):
self.low = low
self.high = high
# extend is 1.5 extension of IQR
self.extend = extend
def remOutliers(self, vector):
svect = np.sort(vector)
q1 = stats.scoreatpercentile(svect, self.low)
q3 = stats.scoreatpercentile(svect, self.high)
# match the values \in svect which are closer to q[1|3]
# (q1, q3)
q1_pos = -1
q3_pos = -1
cur_pos = 0
for i in svect:
if q1_pos != -1 and q3_pos != -1:
break
if q1_pos == -1 and i > q1:
q1_pos = cur_pos
if q3_pos == -1 and q3 < i:
q3_pos = cur_pos
cur_pos += 1
if self.extend == True:
# 1.5 IQR outliers elimination
eiqr = (svect[q3_pos] - svect[q1_pos]) * 1.5
eq1 = svect[q1_pos] - eiqr
if eq1 < svect[0]:
eq1 = svect[0]
eq3 = svect[q3_pos] + eiqr
if eq3 > svect[len(svect) - 1]:
eq3 = svect[len(svect) - 1]
# match the values \in svect which are closer to eq[1|3]
q1_pos = -1
q3_pos = -1
cur_pos = 0
for i in svect:
if q1_pos != -1 and q3_pos != -1:
break
if q1_pos == -1 and i > eq1:
q1_pos = cur_pos
if q3_pos == -1 and eq3 < i:
q3_pos = cur_pos
cur_pos += 1
return svect[q1_pos : q3_pos]
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