-
Notifications
You must be signed in to change notification settings - Fork 0
/
averagers.py
51 lines (46 loc) · 1.23 KB
/
averagers.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
import numpy as np
class Averager(list):
def __init__(self):
self.n = 0
def __iadd__(self, other): # assume all data has same times and size
self.append(other)
n = len(other)
if self.n < n:
self.n = n
return self
def mean(self):
if self.n == 0:
return []
v = [0] * self.n
for a in self:
for i in range(self.n):
v[i] += a[i] if i < len(a) else 0
n = len(self)
for i in range(self.n):
v[i] /= n
return v
def std(self):
if self.n == 0:
return []
m = self.mean()
v = [0] * self.n
for a in self:
for i in range(self.n):
x = a[i] if i < len(a) else 0
v[i] += (x - m[i]) ** 2
n = len(self)
for i in range(self.n):
v[i] = np.sqrt(v[i]/n)
return v
def quantile(self, q):
if self.n == 0:
return []
v = [0] * self.n
n = len(self)
m = np.zeros((self.n, n))
for i in range(n):
a = self[i]
m[0:len(a), i] = a
for i in range(self.n):
v[i] = np.quantile(m[i, ], q)
return v